Open Access

Optical imaging correlates with magnetic resonance imaging breast density and revealscomposition changes during neoadjuvant chemotherapy

  • Thomas D O'Sullivan1,
  • Anaïs Leproux1,
  • Jeon-Hor Chen2, 3,
  • Shadfar Bahri2,
  • Alex Matlock1,
  • Darren Roblyer1,
  • Christine E McLaren5, 6,
  • Wen-Pin Chen5,
  • Albert E Cerussi1,
  • Min-Ying Su2 and
  • Bruce J Tromberg1, 4Email author
Breast Cancer Research201315:R14

https://doi.org/10.1186/bcr3389

Received: 1 October 2012

Accepted: 22 February 2013

Published: 22 February 2013

Abstract

Introduction

In addition to being a risk factor for breast cancer, breast density has beenhypothesized to be a surrogate biomarker for predicting response toendocrine-based chemotherapies. The purpose of this study was to evaluate whethera noninvasive bedside scanner based on diffuse optical spectroscopic imaging(DOSI) provides quantitative metrics to measure and track changes in breast tissuecomposition and density. To access a broad range of densities in a limited patientpopulation, we performed optical measurements on the contralateral normal breastof patients before and during neoadjuvant chemotherapy (NAC). In this work, DOSIparameters, including tissue hemoglobin, water, and lipid concentrations, wereobtained and correlated with magnetic resonance imaging (MRI)-measuredfibroglandular tissue density. We evaluated how DOSI could be used to assessbreast density while gaining new insight into the impact of chemotherapy on breasttissue.

Methods

This was a retrospective study of 28 volunteers undergoing NAC treatment forbreast cancer. Both 3.0-T MRI and broadband DOSI (650 to 1,000 nm) were obtainedfrom the contralateral normal breast before and during NAC. Longitudinal DOSImeasurements were used to calculate breast tissue concentrations of oxygenated anddeoxygenated hemoglobin, water, and lipid. These values were compared withMRI-measured fibroglandular density before and during therapy.

Results

Water (r = 0.843; P < 0.001), deoxyhemoglobin (r =0.785; P = 0.003), and lipid (r = -0.707; P = 0.010)concentration measured with DOSI correlated strongly with MRI-measured densitybefore therapy. Mean DOSI parameters differed significantly between pre- andpostmenopausal subjects at baseline (water, P < 0.001;deoxyhemoglobin, P = 0.024; lipid, P = 0.006). During NACtreatment measured at about 90 days, significant reductions were observed inoxyhemoglobin for pre- (-20.0%; 95% confidence interval (CI), -32.7 to -7.4) andpostmenopausal subjects (-20.1%; 95% CI, -31.4 to -8.8), and water concentrationfor premenopausal subjects (-11.9%; 95% CI, -17.1 to -6.7) compared with baseline.Lipid increased slightly in premenopausal subjects (3.8%; 95% CI, 1.1 to 6.5), andwater increased slightly in postmenopausal subjects (4.4%; 95% CI, 0.1 to 8.6).Percentage change in water at the end of therapy compared with baseline correlatedstrongly with percentage change in MRI-measured density (r = 0.864; P = 0.012).

Conclusions

DOSI functional measurements correlate with MRI fibroglandular density, bothbefore therapy and during NAC. Although from a limited patient dataset, theseresults suggest that DOSI may provide new functional indices of density based onhemoglobin and water that could be used at the bedside to assess response totherapy and evaluate disease risk.

Introduction

Breast density, an assessment of the volume fraction of the human breast that containsepithelial and connective tissues, is a risk factor for breast cancer. Numerous studieshave shown that women with the highest density category evaluated from mammography havefourfold to sixfold increased cancer risk compared with women with lower density[1]. In addition, the International BreastCancer Intervention Study (IBIS-I) primary chemoprevention study, evaluating theselective estrogen-receptor modulator (SERM) tamoxifen [2, 3], revealed that only women who exhibited>10% reduction in percentage mammographic density experienced the protective effect oftamoxifen in decreased cancer incidence [4].Women in the treated group who showed <10% decreased density had exactly the samecancer rates compared with the control group. Similar results were also recently foundregarding the use of tamoxifen and aromatase inhibitors in the adjuvant setting[5]. These and related studies suggest thatbreast tissue density, in addition to being a risk factor, may also be a surrogatebiomarker for monitoring, predicting, and optimizing individual response to hormonaltherapies. However, methods to measure breast density by mammography or MRI have notbeen adopted for various reasons, preventing the utilization of breast-densitymeasurements in the clinic to assess risk or predict outcome.

The only established criterion for assessing breast density is given by the BreastImaging Reporting and Data System (BI-RADS) [6].That system defines four categories for breast density, qualitatively based on therelative amounts of fat and dense fibroglandular tissue in a mammogram. Although thesystem is useful for evaluating the probability that a tumor is obscured by dense tissueon a mammogram, it is not suitable for quantifying or measuring small changes indensity. An alternative approach is to quantify PMD by using computer-basedimage-analysis techniques of mammograms [711]. Whereas it is possible to quantify density from the image, a 2Dradiograph projection is inherently limited in its ability to quantify longitudinaldensity changes accurately [12]. The use ofionizing radiation also limits the frequency of measurements, making it unsuitable formonitoring adjuvant or preventive chemotherapy. We and others have investigated the useof MRI [1219], which is asafe and quantitative technique for measuring breast density and volume, but its highcost precludes it from being applied for risk assessment and screening in mostwomen.

Optical-based imaging modalities are a promising alternative to characterize breastdensity. Because of the absorption and scattering properties of breast tissue,near-infrared light (650 to 1,000 nm) is able to penetrate several centimeters deep.Researchers have measured breast tissue in transmission and reflectance geometries byusing continuous-wave spectroscopy, frequency-domain, or time-domain techniques[20, 21]. Tocorrelate tissue optical measurements with breast density, several groups have comparedspectroscopic features or measured tissue components (such as blood, water, and lipid)with mammographic density. These optically measured parameters have been comparedagainst breast densities that have been qualitatively analyzed from mammograms,classified into only three or four density categories [2228]. As mentioned earlier, this limits the ability of themeasurement to detect small density changes. Promising work attempted to correlatemammographic density with transillumination optical spectroscopy [29, 30], but these studies did notquantify tissue scattering and biochemical composition. This makes it difficult tocompare patient spectroscopic features and to link functional changes with underlyingmechanisms of breast density.

We hypothesize that diffuse optical spectroscopic imaging (DOSI) provides quantitativemetrics to measure and track changes in breast tissue composition and density. DOSIprovides a quantitative measure of tissue functional components, allowing noninvasiveimaging of breast tissue composition and metabolism [31]. DOSI is capable of measuring tissue concentrations (ct) ofoxygenated hemoglobin (ctO2Hb), deoxygenated hemoglobin (ctHHb), water, andlipid. These measurements are directly related to tissue metabolism and vascularcharacteristics. For example, high levels of ctO2Hb are considered to be asurrogate marker for elevated vascular supply and perfusion. High levels of ctHHbreflect high oxygen consumption and tissue metabolism due to cell proliferation and/orpoor vascular drainage. Total hemoglobin (ctTHb) corresponds to the total blood volumein tissue and has been validated as an index that corresponds to increased vasculardensity [32].

DOSI scanning is performed without compression or injection of contrast agents by usinga bedside hand-held probe. Much of this work has been focused on determining functionalchanges in breast tumors during chemotherapy, a topic that is now under investigation inan American College of Radiology Imaging Networks (ACRIN) multicenter clinical trial[33]. The DOSI technique combineslaser-based frequency-domain photon migration with broadband near-infrared spectroscopyto separate optical absorption and scattering over a broad spectral range [20]. This results in a quantitative dataset of tissueconcentrations that can be compared longitudinally in the same patient, or acrossdifferent patients.

Our previous studies [3436] showed that premenopausal women tend to have greaterwater concentration than do postmenopausal women, reflecting the high water content ofepithelial connective-tissue compartments. Similarly, premenopausal women have a higherhemoglobin concentration (both ctHHb and ctO2Hb), because of greater vasculardemands of the glandular tissue, and a lower lipid concentration. Based on these data,we expect that breast density, which quantifies the abundance of hormonally controlledglandular tissue, exhibits a positive correlation with water and ctTHb and a negativecorrelation with lipid.

Because it is known that neoadjuvant chemotherapy (NAC) affects the density andcomposition of normal breast [37], in thisstudy, we measured the contralateral normal side of breast cancer patients before andduring NAC treatment with MRI and DOSI. We examined baseline composition, comparingdifferences between pre- and postmenopausal women. We analyzed DOSI parameters formarkers of breast density and metabolism. The correlation between these results andfibroglandular tissue density measured with MRI was examined to test the hypothesis thatDOSI can provide a quantitative measure of breast density. We further hypothesize that,in addition to providing an optical index of breast density, DOSI may help provideinsight into mechanisms of chemotherapy-induced changes in breast metabolism.

Methods

Subject measurements

This study is a retrospective analysis conducted on a subset of subjects with newlydiagnosed, operative, primary breast cancer measured with DOSI or DOSI+MRI duringtheir neoadjuvant chemotherapy treatment between 2007 and 2012. Subject demographicsare shown in Table 1. DOSI measurements were acquired at aminimum of 30 locations (taken in a rectangular grid pattern with 10-mm spacingbetween measurement points) on a contralateral breast not suggestive of malignanciesat two or more time points during the first 120 days of therapy (n = 28).The DOSI+MRI cohort is a subset (n = 12) of subjects who also received MRIimaging before NAC. Post-NAC MRI images were available for nine subjects.
Table 1

Subject demographics

 

Age (years)

mean ± SD

Number

DOSI cohort

47.4 ± 10.2

28

   Premenopausal

40.8 ± 5.0

17

   Postmenopausal

57.6 ± 7.2

11

DOSI+MRI cohort

43.6 ± 11.0

12

   Premenopausal

38.l ± 5.3

9

   Postmenopausal

60.0 ± 4.0

3

DOSI, diffuse optical spectroscopic imaging.

All subjects provided informed written consent and participated in this study underclinical protocols approved by the Institutional Review Board at the University ofCalifornia, Irvine (2002-2306, 2007-6084, and 2011-7812). Exclusion criteria includedpregnant women and women who were younger than 21 years or older than 75 years. Allsubjects were histologically diagnosed with invasive carcinoma before neoadjuvanttreatment.

DOSI measurement

A comprehensive description of the diffuse optical spectroscopic imaging (DOSI)system and underlying concepts may be found elsewhere [3840]. Inbrief, the instrument combines frequency domain photon migration (FDPM) andcontinuous-wave near-infrared spectroscopy (CW-NIRS) measurements to determine theoptical scattering and absorption spectra (650 to 1,000 nm) of the measured tissue.The FDPM component consists of six laser diode sources (660, 680, 780, 810, 830, and850 nm) that are sinusoidally intensity modulated between 50 and 500 MHz. Therelative amplitude and phase of the detected signals compared with the source areinput into an analytic model of diffuse light transport to determine tissuescattering and absorption coefficients at these wavelengths. White-light illuminationat each measurement point is used for CW-NIRS spectroscopy. The detected broadbandreflectance spectra are fit and scaled to the frequency-domain scattering andabsorption measurements to obtain full broadband absorption spectra over the entirespectral range. Absolute tissue concentrations are calculated by using theBeer-Lambert law and known extinction coefficient spectra of ctHHb,ctO2Hb, water, and bulk lipid.

All subjects received NAC before surgical resection of tumors and were measured withthe DOSI system before treatment (to establish a baseline measurement), and atseveral time points throughout their treatment. Based on our previous findings,baseline measurements were obtained at least 10 days after diagnostic biopsies tominimize their impact on DOSI scans [41].Subjects were measured in a supine position. The DOSI probe was placed against thebreast tissue, and sequential measurements were taken in a linear or rectangular gridpattern by using 10-mm spacing. Measurement regions on the normal breast were takento mirror the area of the underlying tumor determined by ultrasound and palpation onthe ipsilateral breast. Total measurement time varied between 20 minutes and 1 hourper subject. Repeated DOSI scans were shown previously to be relatively insensitiveto probe-contact pressure fluctuations, displaying less than 5% average variation intest/retest studies of human subjects [42].

Mammographic density analysis

Each subject was characterized by a BI-RADS density category. In this system,category I is described as fatty breast tissue, II is scattered density, III isheterogeneously dense, and IV is extremely dense. The categories were compiled fromprechemotherapy mammographic reports documented by the subjects' radiologists andwere available for 20 of the 28 subjects. Of these, four subjects were BI-RADSdensity II, eleven subjects were BI-RADS III, and five subjects were BI-RADS IV.

MR imaging and breast-density analysis

MR imaging examinations were performed by using a dedicatedsensitivity-encoding-enabled bilateral four-channel breast coil with a 3.0-T system(Achieva; Philips Medical Systems, Best, The Netherlands) at time points before,during, and after completion of NAC. The axial-view T1-weighted imageswithout fat suppression were used for the analysis of breast density in this study.The images were acquired by using a 2D turbo spin-echo pulse sequence with TR, 800milliseconds; TE, 8.6 milliseconds; flip angle, 90 degrees; matrix size, 480 ×480; FOV, 31 to 38 cm; and slice thickness, 2 mm.

The breast and fibroglandular tissue segmentation was performed by using a modifiedpublished method [12, 43, 44]. Before the segmentation, the operator viewed thewhole axial T1W images dataset and determined the superior and inferiorboundaries of the breast (the beginning and ending slices) by comparing the thicknessof breast fat with that of the body fat. The breast-segmentation procedures consistedof (a) an initial horizontal line cut along the posterior margin of each individualsubject's sternum to exclude thoracic region; (b) Applied Fuzzy-C-Means (FCM)clustering and b-spline curve fitting to obtain the breast-chest boundary; (c) a biasfield-correction method based on nonparametric nonuniformity normalization (N3), andan adaptive FCM algorithm [44] was used toremove the strong intensity non-uniformity for segmentation of fibroglandular tissueand fatty tissue; (d) applied dynamic searching to exclude the skin along the breastboundary; and (e) the standard FCM algorithm was applied to classify all pixels onthe image. The default setting was to use a total of six clusters, three forfibroglandular tissue and three for fatty tissues. After completing the segmentationprocesses in all image slices, the quantitative breast volume, fibroglandular tissuevolume, and the percentage density (calculated as the ratio of the fibroglandulartissue volume to the breast volume ×100%) were calculated.

Neoadjuvant chemotherapy regimen

Most patients (n = 19) received a 12-cycle, once-a-week course of apaclitaxel (either albumin-bound (nab-paclitaxel) or Cremophor-bound) andcarboplatin. Other patients received only doxorubicin+cyclophosphamiden (AC therapy)(n = 2), or received additional AC therapy either before (n = 4)or after (n = 2) paclitaxel+carboplatin. The remaining patient receiveddocetaxel+carboplatin for six cycles, once every 3 weeks. Many patients also receivedbevacizumab as part of their treatment (n = 15), and some HER2/neu-positivepatients also received trastuzumab (n = 8).

Statistical analysis

Data description

Summary statistics including mean and standard error were calculated for theDOSI-measured parameters of water, bulk lipid, ctO2Hb, ctHHb, ctTHb,oxygen saturation (stO2), and tumor optical index (TOI) measured atbaseline and during NAC. TOI is a tissue optical index of metabolism that providescontrast for metabolically active tissue, developed for identifying tumors[45], and is given by TOI = ctHHb× water/(% lipid). Subject measurements during NAC were recorded as havingoccurred in one of four intervals with interval midpoints 30 days (mean, 33.1;range, 21 to 43/n = 16 pre-, n = 11 postmenopausal), 60 days(mean, 60.8; range, 55 to 69/n = 10 pre-, n = 7 postmenopausal),90 days (mean, 89.9; range, 78 to 104/n = 16 pre-, n = 9postmenopausal), and 120 days (mean, 116.6; range, 106 to 127/n = 6 pre-,n = 4 postmenopausal) from the beginning of chemotherapytreatment.

Comparison of DOSI parameters between groups

The Mann-Whitney U test was applied to test whether mean values for DOSIparameters differed significantly between pre- and postmenopausal subjects. TheShapiro-Wilkes test was applied to each DOSI parameter to test the fit to a normaldistribution within BI-RADS categories II, III, and IV. Analysis of variance(ANOVA) was applied to compare mean values among BI-RADS categories for water,lipid concentration, ctO2Hb, ctHHb, ctTHb, and scattering power. Thenonparametric Kruskal-Wallis test was applied to compare the distributions ofstO2 and TOI values among BI-RADS categories. For pairs of BI-RADScategories (II versus III, II versus IV, and III versus IV), the mean values forDOSI parameters were compared with application of the Bonferroni-Holm method ofadjustment for multiple comparisons to maintain an experiment-wise significancelevel of 0.05 for each DOSI parameter.

Regression and correlation analyses

The linear relation between age and water concentration measured at baseline wasassessed with linear regression analysis and the Pearson correlation coefficient.The influence of values obtained from individual patients was assessed byexamination of regression residuals and the DFFITS statistic. In addition, thecorrelation between age and change in water concentration at 90 days during NACwas estimated with the Pearson correlation coefficient.

The correlation between DOSI parameters and MRI fibroglandular density measured atbaseline, as well as at 24 days and 82 days during NAC, was assessed with thePearson correlation coefficient. Similarly, the correlation between percentagechange in DOSI parameters and percentage change in MRI fibroglandular density at82 days of NAC (that is, the end of NAC treatment) was assessed with the Pearsoncorrelation coefficient. A significance level of 0.05 was used for assessment ofestimated correlations.

Generalized estimating equations

We applied a statistical method known as generalized estimating equations (GEEs)to estimate and compare the expected (mean) change from baseline for eachspecified DOSI parameter between pre- and postmenopausal subjects. For example,the GEE method was used to model the linear relation between the mean change inbreast-tissue water as a function of predictors including menopausal status,measurement day after NAC, and the interaction between menopausal status andmeasurement day. Measurement day from the beginning of chemotherapy wasrepresented by a categorical variable with four categories. In contrast toordinary linear regression, for which values measured in individual subjects areassumed to be independent, the GEE method takes in account the correlation betweenDOSI values measured within individual subjects. For application of the GEE, it isnecessary to specify nature of the linear relation between the mean value of theDOSI parameters and the predictors and between the mean and variance of DOSIparameter values. The nature of the within-subject correlations between DOSImeasurements must also be specified. In technical language, we specified a normalmodel with an identity-link function and an exchangeable correlation structure. Inbrief, these specifications indicated that the mean and variance of the DOSIparameter are related through a normal distribution and that the within-subjectcorrelation between repeated measurements of DOSI values was assumed to be thesame for each subject.

From the final GEE model for a given outcome, the estimated percentage change frombaseline was calculated and compared for both the pre- and the postmenopausalgroups 30, 60, 90, and 120 days from the beginning of chemotherapy. For analysisof each outcome, the Bonferroni-Holm method of adjustment for multiple comparisonswas applied to maintain an experiment-wise significance level of 0.05.

Results

For each patient examination, tissue concentrations of ctO2Hb, ctHHb, water,and lipid were calculated at each measurement point from the broadband absorptionspectra. These data were used to construct 2D maps by using a linear interpolationbetween measurement points, as shown in Figure 1. The areolarregion provides significant contrast because of the high density of fibroglandulartissue and its increased metabolic activity. Optical absorption spectra of tissue inthis region show higher concentrations of hemoglobin and reduced lipid content. Foranalysis, the average of DOSI measurement parameters (chromophore concentrations andscattering coefficients) was computed over the entire measurement region but excludingthe areola. Because the areolar region is a concentrated region of fibroglandular tissuenot representative of the breast as a whole, it was excluded from the DOSI average forthis analysis; the nipple also was excluded from MRI segmentation of fibroglandulartissue. Identical measurement grids were used for longitudinal analysis.
Figure 1

Diffuse optical spectroscopic imaging (DOSI) measurement schematic. Atwo-dimensional map of functional properties is generated by measuring broadbandabsorption and scattering spectra in a grid pattern. On the left is a grid (10 mm)in which each dot represents a DOSI measurement point. The middle is a map oflipid concentration (actual breast data) generated from the measured spectra onthe right. Note that the areola region (A) has much less lipidconcentration and overall higher optical absorption compared with the rest of thebreast (B) because of the higher concentration of water and hemoglobin infibroglandular tissue.

Baseline pre- and postmenopausal differences

Figure 2 shows the average optical absorption and scatteringspectra over all measurement points, excluding the areola, for all premenopausal(n = 17) and postmenopausal (n = 11) subjects. Discernibledifferences are present in both the absorption and scattering spectra betweenpremenopausal and postmenopausal women. Premenopausal women exhibit higherconcentrations of hemoglobin, as evidenced by the overall higher absorption in the670- to 850-nm range. Increased tissue water concentration exists relative to lipidsin premenopausal women, as revealed by the large water-absorption peak at 980 nmcompared with the lipid peak at 930 nm.
Figure 2

Average absorption and scattering spectra measured at baseline forpremenopausal ( n = 17) and postmenopausal ( n = 11)subjects. First, the average value was computed for measurements withineach subject, and then the means for the resulting spectra of all subjects werecomputed. Error bars represent standard error.

Table 2 shows the absolute DOSI parameters for premenopausal,postmenopausal, and all subjects at baseline before beginning NAC treatment. In theabsence of chemotherapeutic intervention, pre- and postmenopausal subjects exhibitedstatistically significant difference in means for water (P < 0.001),lipid (P = 0.006), ctHHb (P = 0.024), and the tissue optical index(TOI) (P = 0.003). Postmenopausal women had a lower mean ctHHb and meanwater concentration at baseline than did premenopausal women, and a higher mean lipidconcentration.
Table 2

Absolute tissue concentrations (mean ± standard error) measured in thenormal breast at baseline

 

Water (%)

Lipid (%)

ctO2Hb (µM)

ctHHb (µM)

ctTHb (µM)

stO2 (%)

TOI

All

n = 28

21.4 ± 1.3

69.7 ± 1.3

18.0 ± 1.2

5.0 ± 0.2

23.0 ± 1.3

77.2 ± 0.9

1.7 ± 0.2

Pre

n = 17

24.4 ± 1.8c

67.0 ± 1.6b

18.9 ± 1.7

5.3 ± 0.2a

24.2 ± 1.9

76.8 ± 1.1

2.1 ± 0.3b

Post

n = 11

16.6 ± 0.7c

74.0 ± 1.4b

16.6 ± 1.5

4.5 ± 0.2a

21.1 ± 1.6

77.7 ± 1.7

1.0 ± 0.1b

Data are shown for all patients, and for the premenopausal (pre) andpostmenopausal (post) subgroups. ctO2Hb, oxyhemoglobinconcentration; ctHHb, deoxyhemoglobin concentration; ctTHb, total hemoglobinconcentration; stO2, tissue oxygen saturation; TOI, tissue opticalindex. a,b,cA statistically significant difference (Mann-WhitneyU test) between pre- and postmenopausal groups (P <0.05; P < 0.01; P < 0.001, respectively).

Figure 3 shows maps of TOI for a typical premenopausal and apostmenopausal subject. In both subjects, the areolar region (indicated by the blackline) exhibits much higher TOI than does the surrounding tissue, whereas thesurrounding TOI tends to be higher in the premenopausal subjects.
Figure 3

Typical maps of tissue optical index (TOI) in the breast of a premenopausaland postmenopausal subject at baseline. The outer limit of the areolarregion is indicated by the black line. Tick-mark separation equals 1 cm. ctHHb,deoxyhemoglobin concentration.

Because the mean tissue water concentration in the normal breast was significantlydifferent at baseline between pre- and postmenopausal groups, the relation betweenage and water concentration was examined to explore these differences in more detail.For a subgroup of 27 subjects (one patient was excluded as she previously underwentan oophorectomy, which caused premature menopause and confounds the effect ofhormones on breast density), water concentration at baseline exhibited significantnegative correlation (r = -0.479; P = 0.011) with age (Figure 4). One subject with extremely high breast density andcorresponding water concentration (46.1%) exerted substantial influence on theregression coefficients, as indicated by regression diagnostics. Previous studies byour group and others have shown that water concentration can vary dramatically inpremenopausal patients [36, 46, 47], perhaps because of normalfluctuations caused by the menstrual cycle [48], which may account for the outlier.
Figure 4

Breast-tissue water concentration at baseline decreased with age ( r = -0.479; P = 0.011). Data shown are for 27 pre- andpostmenopausal subjects (one patient was excluded because she previouslyunderwent an oophorectomy, confounding the effect of hormones on breastdensity).

Relation between DOSI parameters and mammographic density categories

The relations between DOSI parameters and mammographic-density categories, based onthe four traditional BI-RADS density categories, were assessed (Figure 5; all data shown in Additional file 1, TableS1.). None of the subjects measured was characterized as BI-RADS I. A statisticallysignificant difference was found between BI-RADS density categories III and IV forctO2Hb, ctHHb, ctTHb, and TOI. Additionally, a statisticallysignificant difference was found between BI-RADS II and IV for water and TOI. Meanlipid concentration tended to decrease with increasing BI-RADS density category andapproached statistical significance. No significant difference in the means ofstO2 or scattering power was found.
Figure 5

The mean value of measured diffuse optical spectroscopic imaging parameters,separated by the Breast Imaging-Reporting and Data System (BI-RADS) densitycategory. A statistically significant difference (*) was found betweenBI-RADS density categories III and IV for oxyhemoglobin (ctO2Hb),deoxyhemoglobin (ctHHb), total hemoglobin (ctTHb), and tissue optical index(TOI). Additionally, a statistically significant difference was found betweenBI-RADS II and IV for water and TOI. Error bars represent standard error.

Correlation between DOSI parameters and MRI breast density

The correlations between measured DOSI parameters and MRI fibroglandular tissuevolume were examined at baseline and at various time points during NAC (Table 3). At baseline, breast density calculated from MRI showedstronger correlations with ctHHb (r = 0.785; P = 0.003), waterconcentration (r = 0.843; P < 0.001), lipid (r =-0.707; P = 0.010), and TOI (r = 0.891; P < 0.001) thanwith other measures. Figure 6 illustrates the linear relationbetween water and ctHHb with breast density. A statistically significant correlationwith ctTHb (r = 0.597; P = 0.040) was also demonstrated. Figure7 displays the MR images and DOSI images at the beginningand end of NAC for a 31-year-old premenopausal subject. This subject exhibited asignificant reduction in breast density during NAC, and, similarly, a significantreduction in water concentration. However, when correlations between MRI breastdensity and DOSI parameters were estimated at time points near the conclusion of NACtreatment (that is, about 82 days), nonsignificant correlations were found (P > 0.05 for all DOSI parameters).
Table 3

Correlation between diffuse optical spectroscopic imaging parameters andfibroglandular density measured with magnetic resonance imaging for subjects atbaseline and at time points during chemotherapy

 

Baseline (n= 12)

~25 days (n= 10)

Mean, 24.8 ± 5.5

~82 days (n= 7)

Mean, 82.4 ± 11.9

 

R

P

R

P

r

P

Water

0.843

<0.001

0.675

0.032

0.405

0.367

Lipid

-0.707

0.010

-0.462

0.179

-0.018

0.970

ctO2Hb

0.557

0.060

0.647

0.043

0.464

0.294

ctHHb

0.785

0.003

0.399

0.253

0.705

0.077

ctTHb

0.597

0.040

0.644

0.045

0.599

0.155

stO2

0.196

0.543

0.240

0.505

0.012

0.979

TOI

0.891

<0.001

0.541

0.107

0.593

0.160

Scat. power

0.205

0.523

0.143

0.694

0.265

0.566

Bold values indicate statistical significance at the 0.05 level.ctO2Hb, oxyhemoglobin concentration; ctHHb, deoxyhemoglobinconcentration; ctTHb, total hemoglobin concentration; stO2, tissueoxygen saturation; TOI, tissue optical index; Scat. power, optical scatteringpower.

Figure 6

Correlation between optically measured deoxyhemoglobin (ctHHb) and water,with fibroglandular density measured with magnetic resonance imaging(MRI). Data are for 12 subjects (all subjects for whom we had availablecorresponding data) at baseline before chemotherapy (nine pre- and threepostmenopausal). The fitted linear regression line is superimposed on thegraph.

Figure 7

Corresponding magnetic resonance imaging (MRI) and diffuse opticalspectroscopic imaging (DOSI). Images were taken at baseline and end ofneoadjuvant chemotherapy in the contralateral normal breast of a premenopausalpatient. The yellow outlines in the MRI images depict the result of thesegmentation algorithm for fibroglandular tissue in the shown slices. The DOSImaps depict measured parameters as a function of position (tick mark separationequals 1 cm). The illustrated maps are from an 8- × 6-cm area from theupper-inner region of the left breast. The areolar region has more water,outlined by the semicircle. The decreased density after chemotherapy is clearlyvisible in both MRI and DOSI. MRI shows 30.4% reduction in fibroglandulartissue volume, and DOSI shows 24.4% reduction in tissue water. ctHHb,deoxyhemoglobin concentration; TOI, tissue optical index.

We also examined the correlation between percentage change compared with baseline ofthe optical parameters and MRI fibroglandular density at about 82 days (Table 4). A strong correlation of both change in water (r =0.864; P = 0.012) and TOI (r = 0.818; P = 0.025) with MRIwas found.
Table 4

Correlation between percentage change in optical parameters and percentagechange in fibroglandular density by magnetic resonance imaging

 

r

P

Water

0.864

0.012

Lipid

-0.502

0.251

ctO2Hb

-0.166

0.721

ctHHb

0.606

0.149

ctTHb

0.017

0.971

stO2

-0.412

0.357

TOI

0.818

0.025

Scat. power

0.195

0.675

Data are for the seven subjects measured at approximately 82 days ofchemotherapy (five pre- and two postmenopausal). Bold values indicatestatistical significance at the 0.05 significance level. ctO2Hb,oxyhemoglobin concentration; ctHHb, deoxyhemoglobin concentration; ctTHb, totalhemoglobin concentration; stO2, tissue oxygen saturation; TOI,tissue optical index; Scat. power, optical scattering power.

Variations in breast composition during NAC

Significant compositional changes of the normal breast were observed during NAC inboth pre- and postmenopausal subjects with DOSI. GEE models that incorporatedmenopausal status and measurement day were used to fit the outcomes of percentagechange of DOSI parameter from baseline. Because most NAC regimens lasted 12 weeks, weshow the GEE results at about 90 days from the beginning of NAC treatment (Figure8). The measured DOSI parameters at baseline before NAC andat approximately 30, 60, 90, and 120 days after the start of NAC are shown inAdditional file 2, Table S1. Both the premenopausal(-20.0%; 95% CI, -32.7 to -7.4) and postmenopausal (-20.1%; 95% CI, -31.4 to -8.8)groups exhibited statistically significant decreases in ctO2Hb, whereasctHHb stayed relatively flat (premenopausal, -2.5%; 95% CI, -7.7 to 2.7;postmenopausal, 0.5%; 95% CI, -5.3 to 6.3), yielding a reduction of ctTHb. Bulklipids also remained relatively flat during NAC for both pre- (3.8%; 95% CI, 1.1 to6.5) and postmenopausal (-0.4%; 95% CI, -3.4 to 2.6) groups. The stO2 wasnearly identical between the two groups at baseline, and both decreased in a similarmanner during NAC (premenopausal, -6.9%; 95% CI, -11.5 to -2.3; postmenopausal,-7.4%; 95% CI, -14.7 to 0.0). Scattering amplitude and power did not changeappreciably during NAC or between groups (data not shown).
Figure 8

Percentage change in water, deoxyhemoglobin (ctHHb), oxyhemoglobin(ctO 2 Hb), lipid, and tissue oxygen saturation(stO 2 ) at 90 days of chemotherapy treatment compared withbaseline. Based on longitudinal generalized estimating equation models,a statistically significant difference in water (*) was found betweenpremenopausal and postmenopausal groups. Error bars represent standarderror.

Trends during NAC were similar in both menopause groups for all DOSI parametersexcept water (ctO2Hb, shown in Figure 9A), whichexhibited a percentage change that was statistically different between menopausegroups at 90 days. Figure 9B shows that the premenopausal groupincurred a steady decrease in tissue water during NAC, whereas breast-tissue waterconcentration in the postmenopausal group remained flat. After 90 days of NAC,premenopausal subjects exhibited an estimated -11.9% (95% CI, -17.1 to -6.7)reduction in breast-tissue water concentration, whereas postmenopausal subjectsshowed an estimated increase of 4.4% (95% CI, 0.1 to 8.6). The percentage change inwater after 90 days of chemotherapy was correlated with age (Figure 9C; r = 0.745; P < 0.001), providing further evidencethat the effect of NAC on water concentration is stronger in younger, premenopausalsubjects.
Figure 9

Optically measured changes over time during chemotherapy. (A) Oxyhemoglobin concentration (ctO2Hb) decreased duringchemotherapy (90 days) for both groups of patients. (B) Waterconcentration decreased in premenopausal patients during chemotherapy andremained relatively unchanged in postmenopausal patients. (C) Thereduction of water observed after 90 days of chemotherapy was associated withage (r = 0.745; P < 0.001), with younger subjectsexhibiting a greater reduction. Data shown are for 24 pre- and postmenopausalsubjects in whom we had measurement data at 90 days (one patient was excludedbecause she previously underwent an oophorectomy, confounding the effect ofhormones on breast density). The fitted linear regression line is superimposedon the graph.

Discussion

Breast density is a strong independent risk factor for breast cancer and may also have arole in the risk of recurrence. Optical imaging is a low-cost imaging modality thatshows promise for assessing breast density in the clinic without the use of ionizingradiation. Because optical imaging measures functional characteristics of tissue, italso yields additional information that complements breast-density-assessment techniquesusing MRI and mammography, which are based primarily on structure. This additionalinformation may help to elucidate the origin of breast density and to clarify causes ofboth natural and treatment-induced changes of breast density. By understanding thefactors that modulate breast density and the source of its contrast in imaging, we willbe able to apply the parameter better in risk assessment.

Conflicting information exists in the literature correlating optically measured tissuecomponents with breast density. Most agree that increasing density correlates withincreasing water concentration and ctTHb. However, conflicting data are found on othermeasures, including bulk lipid, which has found to be negatively correlated with density[27], or not at all [24]. Oxygen saturation (the fraction ofctO2Hb to ctTHb, or stO2) has been shown to be reduced in densetissue [25], whereas others do not find asignificant correlation [24, 27]. In addition, Taroni et al. [27] included collagen as a basis chromophore and found that it isstrongly associated with BI-RADS categories II-IV. Optical scattering is also expectedto increase with greater breast density because of the underlying fibroglandularstructures, and some studies have indeed shown this [25, 27]. It is possible that thesedisagreements are due to the broad classification of density categories, to the limitedability of the applied optical modalities to quantify tissue components accurately, orto some combination of the two.

In this study, we found that water, ctO2Hb, ctHHb, ctTHb, and TOI areassociated with the BI-RADS density category, whereas lipid tended to decrease withincreasing category but was not statistically significant. The largest mean differencesin hemoglobin were found between BI-RADS density categories III and IV, but not categoryII and IV. This contradiction is likely due to the small sample size (n = 4with BI-RADS II) and the limitations of a broad density-classification scheme. We expectlipid to be significantly associated with the BI-RADS category in a larger sample size.No association was discovered between stO2 or scattering power with BI-RADSdensity.

Several DOSI parameters were strongly correlated with MRI fibroglandular breast densitymeasured at baseline, including tissue water, lipid, and ctHHb concentrations. Nosignificant correlation was noted between density and scattering parameters orstO2. This significant correlation with water is likely becausefibroglandular tissue has about 30% higher water content than does adipose tissue[24]. These observations are in generalagreement with the findings of other groups, with the addition that here we show, forthe first time, that a quantitative measure of water can be compared longitudinally andbetween patients. Because DOSI is a measurement of chromophore concentration throughoutthe probed tissue volume, the increased water may also be due to the increased vascularsupply required by the dense tissue. Furthermore, we demonstrated a strong baselinecorrelation between ctHHb and breast density, reflecting the increased rate ofmetabolism in fibroglandular breast tissue. Because metabolism is strongly captured inthe tissue optical index (TOI), this metric was also shown to be a good predictor ofbreast density. Finally, greater hemoglobin concentrations were observed in patientswith dense breasts, suggesting greater vascular density in these subjects, and lowerlipid concentrations, as would be expected by the greater volume fraction offibroglandular tissue compared to adipose.

At baseline, we observed statistically significant differences in the breast compositionand indicators of metabolism for premenopausal and postmenopausal groups, confirmingprevious studies [47]. Increased mean ctHHb andmean water concentrations were found, as well as decreased mean bulk lipid concentrationin the breasts of premenopausal subjects, compared with those of postmenopausalsubjects. This is all consistent with increased cell proliferation and metabolicactivity in the denser breast tissue of younger premenopausal women.

Significant changes in optical markers for vascular density and supply(ctO2Hb and water) were observed during NAC treatment. A significant decreasein ctO2Hb was observed in both premenopausal and postmenopausal groups. Thesteady reduction of ctO2Hb without a corresponding decrease in ctHHb suggeststhat NAC agents act directly on the breast tissue, perhaps by causing a reduction ofperfusion. This may be caused by chemotherapy-induced vascular damage and may contributeto the reduction of breast density. In contrast, only premenopausal subjects experienceda significant loss of water in their breast tissue during NAC. This trend better matchesthe observed effect of NAC on MRI-measured breast density wherein premenopausal subjectsexperience a greater loss [37]. Even though theabsolute DOSI parameters were not significantly correlated with MRI measurements at theend of NAC, the percentage change in water and TOI at the end of treatment compared withbaseline did show strong correlation with the percentage change in fibroglandular breastdensity over the same time.

These NAC-induced changes could potentially have been caused by direct antiproliferativeeffects of chemotherapy, or by the indirect effect of ovarian suppression and subsequenthormone-level reductions [37]. Cytoxicchemotherapeutic agents are known to cause suppression of ovarian function andamenorrhea, whereas ovarian-secreted hormones (estrogen and progesterone) are known toincrease breast density [4951]. The greater reduction in breast-tissue water inyounger, premenopausal subjects suggests that their chemo-reduced ovarian hormone levelsmay have a role in reducing breast-tissue density. Consequently, the change inctO2Hb also suggests a hormone-independent mechanism. The enhancedbreast-density reduction in premenopausal subjects could also be because breast tissuenaturally becomes less dense with age [52, 53]. Evidence suggests that this is also a hormonal effect[4951]; however, it is due to natural aging and not induced by thechemotherapy. This is reflected in the reduced baseline water concentration of oldersubjects. If less fibroglandular tissue is present in the breasts of the older women,then it is possible that NAC may not be able to cause a significant change in breastdensity (that is, it has already been reduced to minimum). This complicates the abilityto separate the relative importance of chemotherapy as a direct or indirect modulator ofbreast density.

We note that relatively little change over time was observed in mean DOSI-measured bulklipid in both groups of patients undergoing NAC. This suggests that the rapid changes inbreast density induced by NAC occur because of the reduction of the fibroglandulartissue rather than by increases or replacement by bulk lipid.

This study is limited because it is a retrospective study and includes a small number ofsubjects, especially those with matched DOSI and MRI measurements. Therefore, thereported correlations should be interpreted with caution, and they point to the need forfurther studies. Furthermore, DOSI measurements did not sample the entire breast volume.Recent data have shown, however, that density-related measurements from a spatiallylimited optical sampling on the breast are strongly associated with the BI-RADS category[28]. Multiple therapy regimens wereincluded in the analysis, and it is likely that the underlying changes in breast densityvary, based on the specific chemotherapy drugs given. Future work stratifying DOSIchanges by treatment type and outcome in a larger population may provide insight intomechanisms of these changes, as well as patient response to therapy. Additionally,timing of the subjects' menstrual cycles was not accounted for, which can causevariations in breast density, nor their pregnancy history. Nonetheless, the ability toidentify significant correlations over several years of optical measurements speaks tothe strength of the DOSI method as a promising quantitative tool.

Conclusions

In conclusion, this is the first study to confirm that optical imaging can detectsignificant compositional and functional changes in the contralateral normal breastbefore and during chemotherapy, and these changes are correlated with MRI anatomicmeasurements of breast density. Density is a strong independent risk factor for breastcancer, and the ability to quantify it could be valuable input to breast cancer riskmodels. Imaging biomarkers could be used to provide individualized treatment and predictresponse as well as risk of recurrence in breast cancer patients. Together with tissueanalysis, DOSI may provide insight into the underlying biologic origin of density andimprove our understanding of the hormonal and chemotherapy effects. Prospective studiesmust be performed to understand further the correlation of parameters measured with DOSIand MRI breast-imaging modalities. If validated, DOSI may provide an alternativeapproach to predict cancer risk as well as to monitor the protective effects of cancertherapies.

Abbreviations

AC: 

doxorubicin+cyclophosphamide chemotherapy regimen

ACRIN: 

American College ofRadiology Imaging Network

ANOVA: 

analysis of variance

BI-RADS: 

BreastImaging-Reporting and Data System

CI: 

confidence interval

ct: 

concentration

ctHHb: 

tissue concentration of deoxyhemoglobin

ctO2Hb: 

tissue concentration of oxyhemoglobin

ctTHb: 

tissue concentration of total hemoglobin

CW-NIRS: 

continuous-wavenear-infrared spectroscopy

DFFITS: 

diagnostic to assess the influence of a single pointin a statistical regression

DOSI: 

diffuse optical spectroscopic imaging

FCMs: 

fuzzy-C-Means

FDPM: 

frequency-domain photon migration

FOV: 

field of view

GEE: 

generalized estimating equation

HER2: 

human epidermal growth factor receptor 2

IBIS-I: 

International Breast Cancer Intervention Study-I

MRI: 

magnetic resonance imaging

NAC: 

neoadjuvant chemotherapy

SERM: 

selective estrogen-receptor modulator

stO2

tissue oxygen saturation

TE: 

echo time (in MR imaging)

TOI: 

tissue optical index

TR: 

repetition time (in MR imaging).

Declarations

Acknowledgements

The authors thank Montana Compton and Amanda F Durkin for their assistance, as wellas the patients who generously volunteered for this study. This work was supported byNational Institutes of Health grants P41RR01192 and P41EB015890 (Laser Microbeam andMedical Program), U54-CA105480 (Network for Translational Research in OpticalImaging), U54-CA136400, R01-CA142989, R01-CA127927, NCI-2P30CA62203 (University ofCalifornia, Irvine Cancer Center Support Grant), Air Force Research LaboratoryAgreement Number FA9550-04-1-0101, and NCI-T32CA009054 (University of California,Irvine Institutional Training Grant). Beckman Laser Institute programmatic supportfrom the Arnold and Mabel Beckman Foundation is gratefully acknowledged.

Authors’ Affiliations

(1)
Laser Microbeam and Medical Program, Beckman Laser Institute and Medical Clinic, University of California
(2)
Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California
(3)
Department of Radiology, E-Da Hospital and I-Shou University
(4)
Department of Biomedical Engineering, Boston University
(5)
Chao Family Comprehensive Cancer Center, University of California, Irvine Medical Center
(6)
Department of Epidemiology, University of California

References

  1. Boyd NF, Dite GS, Stone J, Gunasekara A, English DR, McCredie MRE, Giles GG, Tritchler D, Chiarelli A, Yaffe MJ, Hopper JL: Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med. 2002, 347: 886-894. 10.1056/NEJMoa013390.View ArticlePubMedGoogle Scholar
  2. Cuzick J, Forbes J, Edwards R, Baum M, Cawthorn S, Coates A, Hamed H, Howell A, Powles T, Clunie G, Collins R, Day N, Northover J, IBIS Investigators: First results from the International Breast Cancer Intervention Study (IBIS-1): arandomised prevention trial. Lancet. 2002, 360: 817-824.View ArticlePubMedGoogle Scholar
  3. Cuzick J, Forbes JF, Sestak I, Cawthorn S, Hamed H, Holli K, Howell A: Long-term results of tamoxifen prophylaxis for breast cancer: 96-month follow-upof the randomized IBIS-I Trial. J Natl Cancer Inst. 2007, 99: 272-282. 10.1093/jnci/djk049.View ArticlePubMedGoogle Scholar
  4. Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, Forbes JF, Warren RML: Tamoxifen-induced reduction in mammographic density and breast cancer riskreduction: a nested case-control study. J Natl Cancer Inst. 2011, 103: 744-752. 10.1093/jnci/djr079.View ArticlePubMedGoogle Scholar
  5. Kim J, Han W, Moon H-G, Ahn S, Shin H-C, You J-M, Han S-W, Im S-A, Kim T-Y, Koo H, Chang J, Cho N, Moon W, Noh D-Y: Breast density change as a predictive surrogate for response to adjuvant endocrinetherapy in hormone receptor positive breast cancer. Breast Cancer Res. 2012, 14: R102-10.1186/bcr3221.View ArticlePubMedPubMed CentralGoogle Scholar
  6. BI-RADS Breast Imaging Reporting and Data System Breast Imaging Atlas. 2003, Reston, VA: American College of RadiologyGoogle Scholar
  7. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ: The quantitative analysis of mammographic densities. Phys Med Biol. 1994, 39: 1629-10.1088/0031-9155/39/10/008.View ArticlePubMedGoogle Scholar
  8. Glide-Hurst CK, Duric N, Littrup P: A new method for quantitative analysis of mammographic density. Med Phys. 2007, 34: 4491-4498. 10.1118/1.2789407.View ArticlePubMedGoogle Scholar
  9. Harvey JA, Bovbjerg VE: Quantitative assessment of mammographic breast density: relationship with breastcancer risk 1. Radiology. 2004, 230: 29-41. 10.1148/radiol.2301020870.View ArticlePubMedGoogle Scholar
  10. Martin KE, Helvie MA, Zhou C, Roubidoux MA, Bailey JE, Paramagul C, Blane CE, Klein KA, Sonnad SS, Chan H-P: Mammographic density measured with quantitative computer-aided method: comparisonwith radiologists' estimates and BI-RADS categories. Radiology. 2006, 240: 656-665. 10.1148/radiol.2402041947.View ArticlePubMedGoogle Scholar
  11. Zhou C, Chan H-P, Petrick N, Helvie MA, Goodsitt MM, Sahiner B, Hadjiiski LM: Computerized image analysis: estimation of breast density on mammograms. Med Phys. 2001, 28: 1056-1069. 10.1118/1.1376640.View ArticlePubMedGoogle Scholar
  12. Nie K, Chen J-H, Chan S, Chau M-K, Yu HJ, Bahri S, Tseng T, Nalcioglu O, Su M-Y: Development of a quantitative method for analysis of breast density based onthree-dimensional breast MRI. Med Phys. 2008, 35: 5253-5262. 10.1118/1.3002306.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Graham SJ, Bronskill MJ, Byng JW, Yaffe MJ, Boyd NF: Quantitative correlation of breast tissue parameters using magnetic resonance andX-ray mammography. Br J Cancer. 1996, 73: 162-168. 10.1038/bjc.1996.30.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Lee NA, Rusinek H, Weinreb J, Chandra R, Toth H, Singer C, Newstead G: Fatty and fibroglandular tissue volumes in the breasts of women 20-83 years old:comparison of X-ray mammography and computer-assisted MR imaging. AJR Am J Roentgenol. 1997, 168: 501-506. 10.2214/ajr.168.2.9016235.View ArticlePubMedGoogle Scholar
  15. Li L, Chu Y, Salem AF, Clark RA: Image segmentation and 3D visualization for MRI mammography. SPIE Medical Imaging 2002: Image Processing. Edited by: Sonka M, Fitzpatrick JM. 2002, San Diego: SPIE, 1780-1789.View ArticleGoogle Scholar
  16. Klifa C, Carballido-Gamio J, Wilmes L, Laprie A, Lobo C, DeMicco E, Watkins M, Shepherd J, Gibbs J, Hylton N: Quantification of breast tissue index from MR data using fuzzy clustering. Engineering in Medicine and Biology Society, 2004 IEMBS '04 26th AnnualInternational Conference of the IEEE; Sept 1-5, 2004. 2004, 1667-1670.Google Scholar
  17. Wei J, Chan H-P, Helvie MA, Roubidoux MA, Sahiner B, Hadjiiski LM, Zhou C, Paquerault S, Chenevert T, Goodsitt MM: Correlation between mammographic density and volumetric fibroglandular tissueestimated on breast MR images. Med Phys. 2004, 31: 933-942. 10.1118/1.1668512.View ArticlePubMedGoogle Scholar
  18. Yao J, Zujewski JA, Orzano J, Prindiville S, Chow C: Classification and calculation of breast fibroglandular tissue volume on SPGR fatsuppressed MRI. SPIE Medical Imaging 2005: Image Processing; San Diego, CA, USA. Edited by: Fitzpatrick JM, Reinhardt JM. 2005, SPIE, 1942-1949.View ArticleGoogle Scholar
  19. van Engeland S, Snoeren PR, Huisman H, Boetes C, Karssemeijer N: Volumetric breast density estimation from full-field digital mammograms. IEEE T Med Imaging. 2006, 25: 273-282.View ArticleGoogle Scholar
  20. O'Sullivan TD, Cerussi AE, Cuccia DJ, Tromberg BJ: Diffuse optical imaging using spatially and temporally modulated light. J Biomed Opt. 2012, 17: 071311-071314. 10.1117/1.JBO.17.7.071311.PubMedPubMed CentralGoogle Scholar
  21. Taroni P: Diffuse optical imaging and spectroscopy of the breast: a brief outline of historyand perspectives. Photochem Photobiol. 2012, 11: 241-250. 10.1039/c1pp05230f.View ArticleGoogle Scholar
  22. Blackmore KM, Knight JA, Jong R, Lilge L: Assessing breast tissue density by transillumination breast spectroscopy (TIBS):an intermediate indicator of cancer risk. Br J Radiol. 2007, 80: 545-556. 10.1259/bjr/26858614.View ArticlePubMedGoogle Scholar
  23. Blyschak K, Simick M, Jong R, Lilge L: Classification of breast tissue density by optical transillumination spectroscopy:optical and physiological effects governing predictive value. Med Phys. 2004, 31: 1398-1414. 10.1118/1.1738191.View ArticlePubMedGoogle Scholar
  24. Brooksby B, Pogue BW, Jiang S, Dehghani H, Srinivasan S, Kogel C, Tosteson TD, Weaver J, Poplack SP, Paulsen KD: Imaging breast adipose and fibroglandular tissue molecular signatures by usinghybrid MRI-guided near-infrared spectral tomography. Proc Natl Acad Sci USA. 2006, 103: 8828-8833. 10.1073/pnas.0509636103.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Srinivasan S, Pogue BW, Carpenter C, Jiang S, Wells WA, Poplack SP, Kaufman PA, Paulsen KD: developments in quantitative oxygen-saturation imaging of breast tissue in vivousing multispectral near-infrared tomography. Antioxid Redox Sign. 2007, 9: 1143-1156. 10.1089/ars.2007.1643.View ArticleGoogle Scholar
  26. Srinivasan S, Pogue BW, Jiang S, Dehghani H, Kogel C, Soho S, Gibson JJ, Tosteson TD, Poplack SP, Paulsen KD: In vivo hemoglobin and water concentrations, oxygen saturation, and scatteringestimates from near-infrared breast tomography using spectral reconstruction. Acad Radiol. 2006, 13: 195-202. 10.1016/j.acra.2005.10.002.View ArticlePubMedGoogle Scholar
  27. Taroni P, Pifferi A, Quarto G, Spinelli L, Torricelli A, Abbate F, Villa A, Balestreri N, Menna S, Cassano E, Cubeddu R: Noninvasive assessment of breast cancer risk using time-resolved diffuse opticalspectroscopy. J Biomed Opt. 2010, 15: 060501-10.1117/1.3506043.View ArticlePubMedGoogle Scholar
  28. Taroni P, Pifferi A, Quarto G, Spinelli L, Torricelli A, Abbate F, Balestreri N, Ganino S, Menna S, Cassano E, Cubeddu R: Effects of tissue heterogeneity on the optical estimate of breast density. Biomed Opt Express. 2012, 3: 2411-2418. 10.1364/BOE.3.002411.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Blackmore KM, Dick S, Knight J, Lilge L: Estimation of mammographic density on an interval scale by transilluminationbreast spectroscopy. J Biomed Opt. 2008, 13: 064030-10.1117/1.3041498.View ArticlePubMedGoogle Scholar
  30. Blackmore KM, Knight JA, Lilge L: Association between transillumination breast spectroscopy and quantitativemammographic features of the breast. Cancer Epidem Biomar. 2008, 17: 1043-1050. 10.1158/1055-9965.EPI-07-2658.View ArticleGoogle Scholar
  31. Cerussi A, Hsiang D, Shah N, Mehta R, Durkin A, Butler J, Tromberg BJ: Predicting response to breast cancer neoadjuvant chemotherapy using diffuseoptical spectroscopy. Proc Natl Acad Sci USA. 2007, 104: 4014-4019. 10.1073/pnas.0611058104.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Pakalniskis MG, Wells WA, Schwab MC, Froehlich HM, Jiang S, Li Z, Tosteson TD, Poplack SP, Kaufman PA, Pogue BW, Paulsen KD: Tumor angiogenesis change estimated by using diffuse optical spectroscopictomography: demonstrated correlation in women undergoing neoadjuvant chemotherapyfor invasive breast cancer?. Radiology. 2011, 259: 365-374. 10.1148/radiol.11100699.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Monitoring and predicting breast cancer neoadjuvant chemotherapy response usingdiffuse optical spectroscopic imaging (DOSI). [http://www.acrin.org/TabID/681/Default.aspx]
  34. Shah N, Cerussi A, Eker C, Espinoza J, Butler J, Fishkin J, Hornung R, Tromberg B: Noninvasive functional optical spectroscopy of human breast tissue. Proc Natl Acad Sci USA. 2001, 98: 4420-4425. 10.1073/pnas.071511098.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Tromberg BJ, Shah N, Lanning R, Cerussi A, Espinoza J, Pham T, Svaasand L, Butler J: Non-invasive in vivo characterization of breast tumors using photon migrationspectroscopy. Neoplasia. 2000, 2: 26-40. 10.1038/sj.neo.7900082.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Cerussi AE, Berger AJ, Bevilacqua F, Shah N, Jakubowski D, Butler J, Holcombe RF, Tromberg BJ: Sources of absorption and scattering contrast for near-infrared opticalmammography. Acad Radiol. 2001, 8: 211-218. 10.1016/S1076-6332(03)80529-9.View ArticlePubMedGoogle Scholar
  37. Chen J-H, Nie K, Bahri S, Hsu C-C, Hsu F-T, Shih H-N, Lin M, Nalcioglu O, Su M-Y: Decrease in breast density in the contralateral normal breast of patientsreceiving neoadjuvant chemotherapy: MR imaging evaluation 1. Radiology. 2010, 255: 44-52. 10.1148/radiol.09091090.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Bevilacqua F, Berger AJ, Cerussi AE, Jakubowski D, Tromberg BJ: Broadband absorption spectroscopy in turbid media by combined frequency-domain andsteady-state methods. Appl Opt. 2000, 39: 6498-6507. 10.1364/AO.39.006498.View ArticlePubMedGoogle Scholar
  39. Jakubowski D, Bevilacqua F, Merritt S, Cerussi A, Tromberg BJ: Quantitative absorption and scattering spectra in thick tissues using broadbanddiffuse optical spectroscopy. Biomedical Optical Imaging. Edited by: Fujimoto JG, Farkas DL. 2009, Oxford University Press, 330-355.Google Scholar
  40. Pham TH, Coquoz O, Fishkin JB, Anderson E, Tromberg BJ: Broad bandwidth frequency domain instrument for quantitative tissue opticalspectroscopy. Rev Sci Instrum. 2000, 71: 2500-2513. 10.1063/1.1150665.View ArticleGoogle Scholar
  41. Tanamai W, Chen C, Siavoshi S, Cerussi A, Hsiang D, Butler J, Tromberg B: Diffuse optical spectroscopy measurements of healing in breast tissue after corebiopsy: case study. J Biomed Opt. 2009, 14: 014024-014029. 10.1117/1.3028012.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Cerussi A, Siavoshi S, Durkin A, Chen C, Tanamai W, Hsiang D, Tromberg BJ: Effect of contact force on breast tissue optical property measurements using abroadband diffuse optical spectroscopy handheld probe. Appl Opt. 2009, 48: 4270-4277. 10.1364/AO.48.004270.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Chang DHE, Chen J-H, Lin M, Bahri S, Yu HJ, Mehta RS, Nie K, Hsiang DJB, Nalcioglu O, Su M-Y: Comparison of breast density measured on MR images acquired using fat-suppressedversus nonfat-suppressed sequences. Med Phys. 2011, 38: 5961-5968. 10.1118/1.3646756.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Lin M, Chan S, Chen J-H, Chang D, Nie K, Chen S-T, Lin C-J, Shih T-C, Nalcioglu O, Su M-Y: A new bias field correction method combining N3 and FCM for improved segmentationof breast density on MRI. Med Phys. 2011, 38: 5-14. 10.1118/1.3519869.View ArticlePubMedGoogle Scholar
  45. Tromberg B, Cerussi A, Shah N, Compton M, Durkin A, Hsiang D, Butler J, Mehta R: Imaging in breast cancer: diffuse optics in breast cancer: detecting tumors inpre-menopausal women and monitoring neoadjuvant chemotherapy. Breast Cancer Res. 2005, 7: 279-285. 10.1186/bcr1358.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Cubeddu R, D'Andrea C, Pifferi A, Taroni P, Torricelli A, Valentini G: Effects of the menstrual cycle on the red and near-infrared optical properties ofthe human breast. Photochem Photobiol. 2000, 72: 383-391.PubMedGoogle Scholar
  47. Shah N, Cerussi AE, Jakubowski D, Hsiang D, Butler J, Tromberg BJ: Spatial variations in optical and physiological properties of healthy breasttissue. J Biomed Opt. 2004, 9: 534-540. 10.1117/1.1695560.View ArticlePubMedGoogle Scholar
  48. Pogue BW, Jiang S, Dehghani H, Kogel C, Soho S, Srinivasan S, Song X, Tosteson TD, Poplack SP, Paulsen KD: Characterization of hemoglobin, water, and NIR scattering in breast tissue:analysis of intersubject variability and menstrual cycle changes. J Biomed Opt. 2004, 9: 541-552. 10.1117/1.1691028.View ArticlePubMedGoogle Scholar
  49. Bremnes Y, Ursin G, Bjurstam N, Rinaldi S, Kaaks R, Gram IT: Endogenous sex hormones, prolactin and mammographic density in postmenopausalNorwegian women. Int J Cancer. 2007, 121: 2506-2511. 10.1002/ijc.22971.View ArticlePubMedGoogle Scholar
  50. Noh JJ, Maskarinec G, Pagano I, Cheung LWK, Stanczyk FZ: Mammographic densities and circulating hormones: a cross-sectional study inpremenopausal women. The Breast. 2006, 15: 20-28. 10.1016/j.breast.2005.04.014.View ArticlePubMedGoogle Scholar
  51. Ursin G, Astrahan MA, Salane M, Parisky YR, Pearce JG, Daniels JR, Pike MC, Spicer DV: The detection of changes in mammographic densities. Cancer Epidemiol Biomarkers Prev. 1998, 7: 43-47.PubMedGoogle Scholar
  52. Clemons M, Goss P: Estrogen and the risk of breast cancer. N Engl J Med. 2001, 344: 276-285. 10.1056/NEJM200101253440407.View ArticlePubMedGoogle Scholar
  53. Kelemen LE, Pankratz VS, Sellers TA, Brandt KR, Wang A, Janney C, Fredericksen ZS, Cerhan JR, Vachon CM: Age-specific trends in mammographic density. Am J Epidemiol. 2008, 167: 1027-1036. 10.1093/aje/kwn063.View ArticlePubMedGoogle Scholar

Copyright

© O'Sullivan et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), whichpermits unrestricted use, distribution, and reproduction in any medium, provided theoriginal work is properly cited.

Advertisement