Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study
Bibliographic record
Abstract
Background: Breast cancer is the most prevalent form of cancer worldwide, with 2.3 million new diagnoses in 2022. Recent advancements in treatment have led to a shift in the use of chemotherapy-targeted immunotherapy from a postoperative adjuvant to a preoperative neoadjuvant approach in select cases, resulting in enhanced survival outcomes. A pathological complete response (pCR) is a critical prognostic marker, with higher pCR rates linked to improved overall and disease-free survival. Objective: The objective of this study was to develop robust, machine learning-based prediction models for pCR following neoadjuvant therapy, leveraging clinical, laboratory, and imaging data. Methods: A retrospective cohort study was conducted using data from the Taipei Medical University Clinical Research Database from 2015 to 2022. Eligible patients were those with breast cancer who received neoadjuvant therapy followed by curative surgical resection. Machine learning models were developed using 3 distinct sets of variables. Model 1 included 14 clinical features such as age, height, weight, tumor stage, receptor status, tumor markers, and intrinsic subtype. Model 2 expanded on this by incorporating additional laboratory data and comorbidities (29 variables in total). Model 3 added breast sonography response data to the clinical variables in model 1. Algorithms including logistic regression, random forest, support vector machines, and extreme gradient boosting were used. Feature selection was performed using recursive feature elimination with cross-validation, and model performance was assessed using accuracy and area under the receiver operating characteristic curve (AUROC). Results: A total of 334 patients were analyzed, with 199 in the non-pCR group and 135 in the pCR group. The application of logistic regression with recursive feature elimination with cross-validation was found to demonstrate the optimal performance among the various algorithms that were evaluated in this study. Model 1 attained a mean accuracy of 0.66 (SD 0.02) and a mean AUROC of 0.73 (SD 0.01). The incorporation of laboratory data and comorbidities in model 2 did not yield significant enhancement, with a mean accuracy of 0.67 (SD 0.02) and a mean AUROC of 0.73 (SD 0.01). The incorporation of breast sonography response in model 3 led to a modest enhancement in predictive performance for the sonography group (accuracy 0.68; AUROC 0.60) in comparison to the nonsonography group (accuracy 0.66; AUROC 0.55). Despite the modest sample size (41 patients) of model 3, the integration of sonography data appeared to offer additional value in predicting pCR and warrants further investigation. Conclusions: This study suggests that incorporating breast sonography into models with clinical and laboratory data may modestly improve pCR prediction. It is important to note that the findings of this study are preliminary and require cautious interpretation. Further studies are required to validate this approach and support its integration into a machine learning-based clinical workflow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".