Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy. (January 2021)

• Record Type: Journal Article
• Authors: Pfob, André; Sidey-Gibbons, Chris; Lee, Han-Byoel; Tasoulis, Marios Konstantinos; Koelbel, Vivian; Golatta, Michael; Rauch, Gaiane M.; Smith, Benjamin D.; Valero, Vicente; Han, Wonshik; MacNeill, Fiona; Weber, Walter Paul; Rauch, Geraldine; Kuerer, Henry M.; Heil, Joerg
• Is Part Of: European journal of cancer. Volume 143(2021)
• Page Start: 134
• Page End: 146
• Publication Date: 2021-01
• Subjects: Pathologic complete response -- Neoadjuvant systemic treatment -- Breast cancer -- Individualized treatment -- Machine learning -- Artificial intelligence -- Vacuum-assisted biopsy -- Surgical oncology; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2020.11.006
• Languages: English
• ISSNs: 0959-8049

Abstract

Abstract: Background: Neoadjuvant systemic treatment elicits a pathologic complete response (pCR) in about 35% of women with breast cancer. In such cases, breast surgery may be considered overtreatment. We evaluated multivariate algorithms using patient, tumor, and vacuum-assisted biopsy (VAB) variables to identify patients with breast pCR. Methods: We developed and tested four multivariate algorithms: a logistic regression with elastic net penalty, an Extreme Gradient Boosting (XGBoost) tree, Support Vector Machines (SVM), and neural network. We used data from 457 women, randomly partitioned into training and test set (2:1), enrolled in three trials with stage 1–3 breast cancer, undergoing VAB before surgery. False-negative rate (FNR) and specificity were the main outcome measures. The best performing algorithm was validated in an independent fourth trial. Results: In the test set (n = 152), the logistic regression with elastic net penalty, XGboost tree, SVM, and neural network revealed an FNR of 1.2% (1 of 85 patients with missed residual cancer). Specificity of the logistic regression with elastic net penalty was 52.2% (35 of 67 women with surgically confirmed breast pCR identified), of the XGBoost tree 55.2% (37 of 67), of SVM 62.7% (42 of 67), and of the neural network 67.2% (45 of 67). External validation (n = 50) of the neural network showed an FNR of 0% (0 of 27) and a specificity of 65.2% (15 of 23). Area under the ROC curve for the neural network was 0.97 (95% CI, 0.94–1.00). Conclusion: A multivariate algorithm can accurately select breast cancer patients without residual cancer after neoadjuvant treatment. Highlights: Identify women with a complete response in the breast after neoadjuvant treatment. We compared the diagnostic performance of multivariate algorithms to breast surgery. An international cohort of 457 women was used for the algorithm development. Upon validation (n = 50) the neural network missed 0% cancer compared to surgery. Omitting breast cancer surgery for these women may be evaluated in future trials.