Molecular imaging · 2026
Radiomic Analysis of MRI for Assessing Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma: A Systematic Review and Metaanalysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Jakipov Murat, Tamadon Amin, Burkitbayev Zhandos, Kochiev Bayram, Karimov Aslan, Temirbayeva Aigerim, Iztleuov Yerbolat, Jamwal Prashant, Daneshvar Keivan, Mussin Nadiar M, Safarzoda Sharoffidin Ramazon
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.