TY - JOUR
T1 - Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy
AU - Pfob, André
AU - Sidey-Gibbons, Chris
AU - Lee, Han Byoel
AU - Tasoulis, Marios Konstantinos
AU - Koelbel, Vivian
AU - Golatta, Michael
AU - Rauch, Gaiane M.
AU - Smith, Benjamin D.
AU - Valero, Vicente
AU - Han, Wonshik
AU - MacNeill, Fiona
AU - Weber, Walter Paul
AU - Rauch, Geraldine
AU - Kuerer, Henry M.
AU - Heil, Joerg
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Breast cancer
KW - Individualized treatment
KW - Machine learning
KW - Neoadjuvant systemic treatment
KW - Pathologic complete response
KW - Surgical oncology
KW - Vacuum-assisted biopsy
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U2 - 10.1016/j.ejca.2020.11.006
DO - 10.1016/j.ejca.2020.11.006
M3 - Article
C2 - 33307491
AN - SCOPUS:85097664757
SN - 0959-8049
VL - 143
SP - 134
EP - 146
JO - European Journal of Cancer
JF - European Journal of Cancer
ER -