Radiomic Analysis of MRI for Assessing Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma: A Systematic Review and Metaanalysis.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology, National Research Oncology Center, Astana, Kazakhstan.
- Department of Natural Sciences, West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan.
- Department of Multidisciplinary Surgery, National Research Oncology Center, Astana, Kazakhstan.
- Department of Radiology, West Kazakhstan Marat Ospanov Medical University (WKMU), Aktobe, Kazakhstan.
- Department of Electrical and Computer Engineering, Nazarbayev University, Astana, Kazakhstan.
- Department of Diagnostic, Interventional and Paediatric Radiology (DIPR), Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
- Department of Surgery No. 2, West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan.
- Department of Pharmaceutical Technology, Avicenna Tajik State Medical University, Dushanbe, Tajikistan.
Abstract
This study aimed to evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based radiomics for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients with locally advanced rectal adenocarcinoma. Eligible studies developed MRI-based radiomics or deep learning models to predict pCR and reported sufficient data to reconstruct 2 × 2 contingency tables. Only validation cohorts were included in the quantitative synthesis. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio were estimated using a bivariate random-effects model. Hierarchical summary receiver operating characteristic (HSROC) analysis was performed. Thirty-eight studies were included. The pooled sensitivity and specificity were 0.82 (95% CI, 0.71-0.90) and 0.86 (95% CI, 0.80-0.91), respectively. The pooled PLR and NLR were 6.0 (95% CI, 4.0-8.9) and 0.21 (95% CI, 0.12-0.35), corresponding to a diagnostic odds ratio of 29 (95% CI, 14-61). HSROC analysis showed an area under the curve of 0.846. Subgroup analyses suggested improved performance for deep learning and combined clinical-radiomic models. MRI-based radiomics demonstrates good diagnostic accuracy for predicting pCR after neoadjuvant chemoradiotherapy in rectal cancer, although methodological heterogeneity and limited prospective validation remain challenges.