Evaluating the Role of Artificial Intelligence in Enhancing Multidisciplinary Team Decisions for Breast Cancer Management - European Journal of Breast Health

Background: Predicting tumor regression following neoadjuvant chemoradiotherapy (nCRT) remains a major challenge in the management of locally advanced rectal cancer (LARC). Readily available inflammatory biomarkers may provide useful information regarding treatment response. Methods: This retrospective single-center study included 88 patients with stage II–III rectal adenocarcinoma who underwent neoadjuvant chemoradiotherapy followed by curative-intent surgery between 2017 and 2025. Patients were classified according to the College of American Pathologists tumor regression grading system as CAP 0 (pathological complete response, n = 22), CAP 2 (partial response, n = 50), or CAP 3 (poor/no response, n = 16). Pretreatment C-reactive protein, carcinoembryonic antigen (CEA), neutrophil, platelet, monocyte, and lymphocyte counts, together with NLR, PLR, and PIV, were compared across groups. Receiver operating characteristic and logistic regression analyses were performed for pathological complete response (pCR). Results: None of the evaluated biomarkers differed significantly across CAP groups. The smallest omnibus p-values were observed for neutrophil count (p = 0.052), monocyte count (p = 0.075), and CEA (p = 0.088). Monocyte count showed the highest discriminatory performance for pathological complete response (AUC = 0.663), followed by CEA (AUC = 0.640). In sensitivity analyses adjusted for baseline clinical T stage and receipt of total neoadjuvant therapy, CEA, neutrophil count, and monocyte count were not independently associated with pathological complete response. More favorable tumor regression was associated with lower residual tumor burden and reduced nodal involvement. Conclusions: Pretreatment inflammatory biomarkers showed biologically plausible numerical patterns across tumor regression groups, but their discriminatory and independent predictive performance was limited. These markers should not be considered stand-alone clinical prediction tools and should be validated within larger, multimodal prospective models.

Accurate non-invasive prediction of pathological complete response (pCR) following neoadjuvant chemotherapy (NACT) in breast cancer (BC) remains challenging despite its established prognostic significance. Objective: We aimed to evaluate the prognostic utility of baseline and post-NACT magnetic resonance imaging (MRI) and ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) for predicting pCR and survival outcomes, focusing on molecular subtype-specific performance and post-NACT imaging discordance. Methods: In this multicenter study, we retrospectively analyzed 335 patients with BC who received NACT between 2015 and 2025. Baseline (pre-NACT) and post-NACT imaging assessments were performed using MRI and ¹⁸F-FDG PET/CT. Pathological response was graded using the Miller–Payne classification system. Multivariable logistic regression was applied to identify independent predictors of pCR, whereas survival outcomes were examined using Kaplan–Meier analysis and Cox regression. Results: The overall pCR rate was 41.2%. Post-NACT imaging demonstrated complete response in 58.7% of patients by ¹⁸F-FDG PET/CT and 43.6% by MRI, both significantly correlating with pCR (p < 0.001). Pre-NACT MRI tumor size showed predictive value exclusively in Luminal A/B HER2-negative disease (area under curve = 0.681; p = 0.013). Importantly, post-NACT discordance between MRI and ¹⁸F-FDG PET/CT-based tumor size assessments was an independent predictor of both mortality (hazard ratio, 1.03) and disease progression (hazard ratio, 1.01). Conclusions: Post-NACT MRI and ¹⁸F-FDG PET/CT findings correlate strongly with pCR achievement, whereas pre-NACT MRI tumor size predicts pCR only in hormone receptor-positive HER2-negative subtypes. Importantly, post-NACT imaging discordance independently predicted mortality and disease progression, suggesting that dual-modality imaging assessment may identify high-risk patients requiring intensified surveillance.