Machine Learning Algorithm for Distinguishing Ductal Carcinoma In Situ from Invasive Breast Cancer

SIMPLE SUMMARY: Breast cancer nowadays is the most common cancer among women. Two types refer to whether cancer has spread or not: Non-invasive and invasive breast cancers. Invasive ductal carcinoma is responsible for approximately 80% of all breast cancers, and ductal carcinoma in situ accounts for the majority of the remainder. Early identification of types of breast cancers provides breast cancer patients with more options for less invasive therapy. Our study aimed to develop a machine-learning classification model to differentiate ductal carcinoma in situ and minimally invasive breast cancer using clinical characteristics, mammography findings, ultrasound findings, and histopathology features. Our model showed that the five most important features were calcifications on mammograms, lymph node presence, microcalcifications on histopathology, the shape of the mass on ultrasound, and the orientation of the mass on ultrasound. ABSTRACT: Purpose: Given that early identification of breast cancer type allows for less-invasive therapies, we aimed to develop a machine learning model to discriminate between ductal carcinoma in situ (DCIS) and minimally invasive breast cancer (MIBC). Methods: In this retrospective study, the health records of 420 women who underwent biopsies between 2010 and 2020 to confirm breast cancer were collected. A trained XGBoost algorithm was used to classify cancers as either DCIS or MIBC using clinical characteristics, mammographic findings, ultrasonographic findings, and histopathological features. Its performance was measured against other methods using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score. Results: The model was trained using 357 women and tested using 63 women with an overall 420 patients (mean [standard deviation] age, 57.1 [12.0] years). The model performed well when feature importance was determined, reaching an accuracy of 0.84 (95% confidence interval [CI], 0.76–0.91), an AUC of 0.93 (95% CI, 0.87–0.95), a specificity of 0.75 (95% CI, 0.67–0.83), and a sensitivity of 0.91 (95% CI, 0.76–0.94). Conclusion: The XGBoost model, combining clinical, mammographic, ultrasonographic, and histopathologic findings, can be used to discriminate DCIS from MIBC with an accuracy equivalent to that of experienced radiologists, thereby giving patients the widest range of therapeutic options.