Prediction of pathological complete response after neoadjuvant therapy in breast cancer using pretreatment core biopsy pathology reports: a pathology-report-driven model with exploratory clinical-laboratory fusion

Abstract

Pathological complete response (pCR) after neoadjuvant therapy is an important response endpoint in breast cancer. We investigated whether routinely generated pretreatment core biopsy pathology reports could support pCR prediction and whether clinical, preoperative laboratory, and treatment information provided incremental predictive value. This single-center retrospective model-development study included 171 patients with breast cancer who underwent core biopsy, neoadjuvant therapy, and surgery at Xinxiang Central Hospital between March 1, 2016, and January 1, 2026; 64 achieved pCR and 107 did not. We converted 361 pretreatment pathology files to patient-level text, extracted structured pathology phenotypes with a rule-based pipeline, and represented report text with character/marker n-gram term frequency-inverse document frequency followed by singular value decomposition. Five primary elastic net logistic regression models were assessed using 20 repeats of stratified 5-fold cross-validation, with all learned preprocessing confined to training folds. The clinical-laboratory model showed weak discrimination (area under the receiver operating characteristic curve [AUC] 0.420, 95% confidence interval [CI] 0.326-0.509). The structured pathology and report text models achieved AUCs of 0.738 (95% CI 0.664-0.810) and 0.689 (95% CI 0.606-0.767), respectively. Pathology fusion achieved an AUC of 0.747 (95% CI 0.672-0.815; Brier score 0.200, 95% CI 0.175-0.226). Adding clinical-laboratory variables did not improve AUC (multimodal AUC 0.733, 95% CI 0.656-0.805; difference vs. pathology fusion −0.014, 95% CI -0.048 to 0.017; P = 0.388). In a post-hoc secondary analysis, adding standardized regimen components to pathology fusion yielded an AUC of 0.757 (95% CI 0.679-0.824) and an AUC increase of 0.009 (95% CI −0.019 to 0.037; P = 0.487). The tested frozen BGE, Chinese RoBERTa, and ERNIE-Health embeddings did not outperform the lexical text representation. Among 20 audited image-only cases, OCR character accuracy was 98.72%; across 40 audited cases, critical pathology-field agreement ranged from 95.0% to 100%. Pretreatment pathology reports carried most of the observed predictive signal, whereas the incremental value of treatment information was small and imprecisely estimated. Independent external validation is required before clinical use.