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Table 1 Patient characteristics

From: A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk

Clinicopathologic characteristics of patients in the training and validation cohorts
Baseline characteristicTraining cohort (N = 159)Validation cohort (N = 185)Difference (p value)
Patient age
 Median age (range), years57 (30–83)59 (36–77)0.30
 Age < 50, n (%)26 (16.3)23 (12.4)
 Age ≥ 50, n (%)133 (83.7)162 (87.6)
Menopausal status, n (%)
 Pre31 (19.5)29 (15.7)0.35
 Post128 (80.5)156 (84.3)
Presentation, n (%)
 Screening85 (53.5)120 (64.9)0.03
 Symptomatic74 (46.5)65 (35.1)
Comedo necrosis, n (%)
 No60 (37.7)34 (18.4)< .0001
 Yes99 (62.3)151 (81.6)
Radiation, n (%)
 No117 (73.6)145 (78.4)0.30
 Yes42 (26.4)40 (21.6)
Grade, n (%)
 125 (15.8)0 (0.0)< .0001
 224 (15.2)0 (0.0)
 3109 (69.0)185 (100.0)
Margins, n (%)
 Negative154 (97.5)183 (98.9)0.31
 Positive4 (2.5)2 (1.1)
Tumor size
 Median tumor size (range), cm1.7 (0.1–14.5)1.7 (0.2–12.0)0.74
 Size < 2.0, n (%)88 (56.4)101 (55.6)
 Size ≥ 2.5, n (%)68 (43.6)84 (45.4)
Survival status, n (%)
 Alive109 (68.6)159 (86.0)0.00
 Dead50 (31.4)26 (14.0)
10-year recurrence status, n (%)
 Recurrence free128 (80.5)159 (85.9)0.18
 Recurred31 (19.5)26 (14.1)
  1. Descriptive data detailing the training and validation cohort’s clinicopathological variables. The cutoff point for positive margins was 2 mm. In the training cohort, the tumor size of 3 cases was not known and a patient has missing data for margin status and grade. The proportional difference of clinicopathological variables are measured with the chi-square test