Prediction Models for Pathological Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: A Systematic Review and Meta-Analysis.
Authors
Affiliations (5)
Affiliations (5)
- Medical Big Data and Bioinformatics Research Center, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China.
- The First School of Clinical Medicine, Gannan Medical University, Ganzhou 341000, China.
- Ganzhou Key Laboratory of Medical Big Data, Ganzhou 341000, China.
- Gannan Subcenter for Medical Genomics, National Genomics Data Center, Ganzhou 341000, China.
- School of Public Health and Health Management, Gannan Medical University, Ganzhou 341000, China.
Abstract
<b>Background</b>: Accurate pretreatment prediction of pCR is critical for stratifying patients with LARC and selecting candidates for organ-preserving approaches. However, pCR is a postoperative pathological endpoint, whereas watch-and-wait strategies are based on cCR. Despite a rapidly expanding literature encompassing gene expression signatures, MRI radiomics, deep learning, and multi-modal fusion models, no prior systematic review has comprehensively evaluated predictive performance across all modalities using contemporary meta-analytic techniques for diagnostic test accuracy. <b>Methods</b>: Systematic review and random- effects meta-analysis of studies developing or validating predictive models for pCR in LARC treated with neoadjuvant chemoradiotherapy (nCRT) were conducted. Summary AUC was estimated using logit-transformed random-effects models. Heterogeneity was quantified by I<sup>2</sup> and τ<sup>2</sup>. Subgroup analyses were performed according to model modality and validation strategy. Publication bias was assessed with Egger's linear regression test. Sensitivity analyses were performed using leave-one-out cross-validation. <b>Results</b>: Forty-three studies with 67,302 patients were included. The pooled AUC across all models was 0.874 (95% CI: 0.855-0.892), with substantial heterogeneity (I<sup>2</sup> = 94.3%, τ<sup>2</sup> = 0.247). Deep learning models showed the highest pooled AUC (0.919, 95% CI: 0.877-0.948), with MRI radiomics (0.885, 95% CI: 0.852-0.911), multi-modal (0.875, 95% CI: 0.813-0.919), gene expression (0.855, 95% CI: 0.797-0.898), and clinical factors (0.820, 95% CI: 0.788-0.849) showing progressively lower pooled estimates. External validation cohorts had a lower pooled AUC (0.864) than internal validation (0.884). Significant publication bias was detected (Egger's test <i>p</i> < 0.001). Leave-one-out sensitivity analysis confirmed robustness of the overall estimate. <b>Conclusions</b>: Deep learning and MRI radiomics models demonstrate promising diagnostic accuracy for predicting pCR after nCRT in LARC. However, the predominance of retrospective, single-center designs with limited external validation represents a critical translational gap, and the lack of studies in the total neoadjuvant therapy era limits generalizability. Prospective, multicenter validation studies incorporating standardized endpoints and class imbalance-appropriate metrics are urgently needed.