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Table 2 Sample characteristics of the US population-based sample (n = 1143) and the Swiss clinic-based sample (n = 2481)

From: Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models

Variables included in BCRAT and BOADICEA models and in ML algorithms

US population-based sample n = 1143

Swiss clinic-based sample n = 2481

Age (range)

50.86 ± 6.22 (35–64)

50.78 ± 12.77 (13–89)

Age at menarche (range)

12.56 ± 1.54 (8–18)

12.91 ± 1.59 (8–18)

Age at first live birth (range)

24.29 ± 5.62 (13–42)

24.13 ± 5.72 (15–48)

Number of biopsies (n = 847)

1.20 ± 1.21

–

 Atypical hyperplasia

14 (1.65%)

–

Breast cancer

850 (74.37%)

886 (35.71%)

 First-ductal carcinoma in situ (DCIS)

434 (51.06%)

50 (5.64%)

 First-invasive breast cancer

404 (47.52%)

807 (91.08%)

 First-breast cancer age onset (range)

40.03 ± 4.79 (26–54)

46.07 ± 10.69 (22–84)

 Bilateral breast cancer

4 (0.47%)

160 (18.06%)

 Estrogen receptor (ER) positive

–

618 (69.75%)

 Progesterone receptor (PR) positive

–

561 (63.32%)

Pancreatic cancer

–

13 (0.52%)

 Pancreatic cancer age onset (range)

 

55.10 ± 9.35 (36–75)

Ovarian cancer

9 (0.79%)

133 (5.36%)

 Ovarian cancer age onset (range)

45.83 ± 5.00 (36–50)

56.44 ± 13.16 (21–85)

 Having also breast cancer

4

20

Ethnicity (% Black)

401 (35.08%)

71 (2.86%)

Ashkenazi Jewish origin

12 (1.05%)

65 (2.29%)

Number of first-degree relatives with breast cancer

0.98 ± 1.05

0.25 ± 0.55

 Breast cancer patients

0.81 ± 1.05

–

 Relatives of breast cancer patients

1.49 ± 0.88

–

BRCA1 or BRCA2 germline mutations

32 (2.79%) 235 tested

209 (8.42%) 1052 tested

  1. – Data not available