Cancers
Prediction Models for Pathological Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: A Systematic Review and Meta-Analysis
By Yun Luo, Youping Ding, Wei Liu and 4 others.
Abstract
Background
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.
Methods
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 I2 and τ2. 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.