Uncertainty-Aware Logistic Regression with Gray-Zone Refinement for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

Aixa Ximena Torres Fuertes, Fatima R. Jara Cuya, Rodrigo Romero Tello, Jesus A. Sullon Silva, Ariana M. Villegas Suarez
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:1336-1345, 2026.

Abstract

Predicting response to neoadjuvant chemotherapy ({NAC}) in breast cancer remains a clinical challenge. We developed a machine learning framework combining bibliographically-weighted Elastic Net for dimensionality reduction with regularized Logistic Regression ({LR}) as the primary model, and a selective escalation strategy using a multilayer perceptron ({MLP}) for ambiguous predictions. From GSE205568 (n=2551), 730 robust genes were selected. {LR} achieved strong performance (nested-{CV} {AUCPR} = 0.82, {ROC}-{AUC} = 0.93), but uncertainty analysis identified a “gray zone” near the decision threshold, concentrating misclassifications. Routing these cases to an {MLP} and aggregating outputs via stacking with isotonic recalibration improved gray-zone {AUCPR} by +0.24 and yielded perfect calibration ({ECE} $\approx$ 0). External validation on GSE25065 (n=198) showed that while discrimination transferred ({ROC}-{AUC} = 0.94, {AUCPR} = 0.76), recalibration and local threshold adjustment were required to recover clinically useful performance (F1 = 0.74, Recall = 0.95) (de Hond et al., 2023). These findings support the use of {LR} as a reliable baseline, augmented by explicit uncertainty detection and selective complexity to improve robustness in clinical prediction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-torres-fuertes26a, title = {Uncertainty-Aware Logistic Regression with Gray-Zone Refinement for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer}, author = {Torres Fuertes, Aixa Ximena and Jara Cuya, Fatima R. and Romero Tello, Rodrigo and Sullon Silva, Jesus A. and Villegas Suarez, Ariana M.}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {1336--1345}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/torres-fuertes26a/torres-fuertes26a.pdf}, url = {https://proceedings.mlr.press/v297/torres-fuertes26a.html}, abstract = {Predicting response to neoadjuvant chemotherapy ({NAC}) in breast cancer remains a clinical challenge. We developed a machine learning framework combining bibliographically-weighted Elastic Net for dimensionality reduction with regularized Logistic Regression ({LR}) as the primary model, and a selective escalation strategy using a multilayer perceptron ({MLP}) for ambiguous predictions. From GSE205568 (n=2551), 730 robust genes were selected. {LR} achieved strong performance (nested-{CV} {AUCPR} = 0.82, {ROC}-{AUC} = 0.93), but uncertainty analysis identified a “gray zone” near the decision threshold, concentrating misclassifications. Routing these cases to an {MLP} and aggregating outputs via stacking with isotonic recalibration improved gray-zone {AUCPR} by +0.24 and yielded perfect calibration ({ECE} $\approx$ 0). External validation on GSE25065 (n=198) showed that while discrimination transferred ({ROC}-{AUC} = 0.94, {AUCPR} = 0.76), recalibration and local threshold adjustment were required to recover clinically useful performance (F1 = 0.74, Recall = 0.95) (de Hond et al., 2023). These findings support the use of {LR} as a reliable baseline, augmented by explicit uncertainty detection and selective complexity to improve robustness in clinical prediction.} }
Endnote
%0 Conference Paper %T Uncertainty-Aware Logistic Regression with Gray-Zone Refinement for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer %A Aixa Ximena Torres Fuertes %A Fatima R. Jara Cuya %A Rodrigo Romero Tello %A Jesus A. Sullon Silva %A Ariana M. Villegas Suarez %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-torres-fuertes26a %I PMLR %P 1336--1345 %U https://proceedings.mlr.press/v297/torres-fuertes26a.html %V 297 %X Predicting response to neoadjuvant chemotherapy ({NAC}) in breast cancer remains a clinical challenge. We developed a machine learning framework combining bibliographically-weighted Elastic Net for dimensionality reduction with regularized Logistic Regression ({LR}) as the primary model, and a selective escalation strategy using a multilayer perceptron ({MLP}) for ambiguous predictions. From GSE205568 (n=2551), 730 robust genes were selected. {LR} achieved strong performance (nested-{CV} {AUCPR} = 0.82, {ROC}-{AUC} = 0.93), but uncertainty analysis identified a “gray zone” near the decision threshold, concentrating misclassifications. Routing these cases to an {MLP} and aggregating outputs via stacking with isotonic recalibration improved gray-zone {AUCPR} by +0.24 and yielded perfect calibration ({ECE} $\approx$ 0). External validation on GSE25065 (n=198) showed that while discrimination transferred ({ROC}-{AUC} = 0.94, {AUCPR} = 0.76), recalibration and local threshold adjustment were required to recover clinically useful performance (F1 = 0.74, Recall = 0.95) (de Hond et al., 2023). These findings support the use of {LR} as a reliable baseline, augmented by explicit uncertainty detection and selective complexity to improve robustness in clinical prediction.
APA
Torres Fuertes, A.X., Jara Cuya, F.R., Romero Tello, R., Sullon Silva, J.A. & Villegas Suarez, A.M.. (2026). Uncertainty-Aware Logistic Regression with Gray-Zone Refinement for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:1336-1345 Available from https://proceedings.mlr.press/v297/torres-fuertes26a.html.

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