Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer

• Published in: NPJ breast cancer Vol. 8; no. 1; p. 8
• Main Authors: Park, Ko Woon, Kim, Seon Woo, Han, Heewon, Park, Minsu, Han, Boo-Kyung, Ko, Eun Young, Choi, Ji Soo, Cho, Eun Yoon, Cho, Soo Youn, Ko, Eun Sook
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1347; 631/67/2321; Biomedical and Life Sciences; Biomedicine; Biopsy; Breast cancer; Cancer Research; Cell Biology; Human Genetics; Machine learning; Magnetic resonance imaging; Mammography; Oncology
• ISSN: 2374-4677; 2374-4677
• DOI: 10.1038/s41523-021-00364-z

Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) may be diagnosed with invasive breast cancer after excision. We evaluated the preoperative clinical and imaging predictors of DCIS that were associated with an upgrade to invasive carcinoma on final pathology and also compared the diagnostic performance of various statistical models. We reviewed the medical records; including mammography, ultrasound (US), and magnetic resonance imaging (MRI) findings; of 644 patients who were preoperatively diagnosed with DCIS and who underwent surgery between January 2012 and September 2018. Logistic regression and three machine learning methods were applied to predict DCIS underestimation. Among 644 DCIS biopsies, 161 (25%) underestimated invasive breast cancers. In multivariable analysis, suspicious axillary lymph nodes (LNs) on US (odds ratio [OR], 12.16; 95% confidence interval [CI], 4.94–29.95; P  < 0.001) and high nuclear grade (OR, 1.90; 95% CI, 1.24–2.91; P  = 0.003) were associated with underestimation. Cases with biopsy performed using vacuum-assisted biopsy (VAB) (OR, 0.42; 95% CI, 0.27–0.65; P  < 0.001) and lesion size <2 cm on mammography (OR, 0.45; 95% CI, 0.22–0.90; P  = 0.021) and MRI (OR, 0.29; 95% CI, 0.09–0.94; P  = 0.037) were less likely to be upgraded. No significant differences in performance were observed between logistic regression and machine learning models. Our results suggest that biopsy device, high nuclear grade, presence of suspicious axillary LN on US, and lesion size on mammography or MRI were independent predictors of DCIS underestimation.