Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer.
- 1Breast Unit, Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany.
- 2Department of Obstetrics and Gynecology, Münster University Hospital, Münster, Germany.
- 3Breast Unit, Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany. [email protected].
Abstract
Background
Breast cancer patients receiving neoadjuvant treatment may benefit from a presurgical assessment of complete response to avoid unnecessary surgeries. We aimed to develop a machine learning model that predicts pathological complete response (pCR) based on results from different image modalities as well as tumor and patient characteristics.Methods
Clinical data from 388 cases of invasive breast cancer receiving neoadjuvant treatment were extracted in this retrospective, monocentric study. Findings from ultrasound, mammography and magnetic resonance imaging combined with patient and histopathological information were included. A Pearson Product-Moment correlation and a LASSO regularization were performed to preselect the most relevant features for the outcome. Based on the preselected variables, we developed and validated a machine learning algorithm (logistic regression with elastic net penalty (GLM)) to predict pCR. The model's performance was evaluated via area under the curve (AUROC), false positive rate (FPR) and positive predictive value (PPV).Results
In the total cohort, pCR was detected in 158 out of 388 cases, resulting in a pCR rate of 40.7%. From 111 collected variables, 8 variables were removed due to a high correlation with other features. After the LASSO regularization, 14 variables were included as predictors for the final model. In the validation set, the GLM achieved an AUROC of 0.76 with a FPR of 8.7% and a PPV of 75%.Conclusions
Through the combination of imaging and clinicopathological features, our machine learning model shows a promising approach to improve the prediction of pCR in breast cancer patients receiving neoadjuvant treatment. In combination with the LASSO regularization, it allows interesting insights into the predictive potential of different variables, which are available as part of standard clinical practice.Trial registration
Not applicable.
Related
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BibTeX
@article{reisig2026machine,
title = {{Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer.}},
author = {Eva Reisig and Lie Cai and Maria Müller and Benedikt Schäfgen and André Pfob},
journal = {BMC cancer},
year = {2026},
month = oct,
doi = {10.1186/s12885-026-17115-7},
url = {https://europepmc.org/article/MED/42875758},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer.
AU - Eva Reisig
AU - Lie Cai
AU - Maria Müller
AU - Benedikt Schäfgen
AU - André Pfob
PY - 2026
DA - 2026/10/08
JO - BMC cancer
DO - 10.1186/s12885-026-17115-7
UR - https://europepmc.org/article/MED/42875758
AB - Background Breast cancer patients receiving neoadjuvant treatment may benefit from a presurgical assessment of complete response to avoid unnecessary surgeries. We aimed to develop a machine learning model that predicts pathological complete response (pCR) based on results from different image modalities as well as tumor and patient characteristics.Methods Clinical data from 388 cases of invasive breast cancer receiving neoadjuvant treatment were extracted in this retrospective, monocentric study. Findings from ultrasound, mammography and magnetic resonance imaging combined with patient and histopathological information were included. A Pearson Product-Moment correlation and a LASSO regularization were performed to preselect the most relevant features for the outcome. Based on the preselected variables, we developed and validated a machine learning algorithm (logistic regression with elastic net penalty (GLM)) to predict pCR. The model's performance was evaluated via area under the curve (AUROC), false positive rate (FPR) and positive predictive value (PPV).Results In the total cohort, pCR was detected in 158 out of 388 cases, resulting in a pCR rate of 40.7%. From 111 collected variables, 8 variables were removed due to a high correlation with other features. After the LASSO regularization, 14 variables were included as predictors for the final model. In the validation set, the GLM achieved an AUROC of 0.76 with a FPR of 8.7% and a PPV of 75%.Conclusions Through the combination of imaging and clinicopathological features, our machine learning model shows a promising approach to improve the prediction of pCR in breast cancer patients receiving neoadjuvant treatment. In combination with the LASSO regularization, it allows interesting insights into the predictive potential of different variables, which are available as part of standard clinical practice.Trial registration Not applicable.
SN - 1471-2407
ER -