{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["38"],"submitter":["Xiang Y"],"pubmed_abstract":["Highlights • Pre-treatment MRI images were used to construct a prediction model for pathologic T downstaging in locally advanced rectal cancer.• The prediction model showed good performance in training cohort (AUC 0.842), internal testing cohort (AUC 0.809) and prospective cohort (AUC 0.727).• High-probability group (score > 81.82) had potential benefits from sufficient consolidation chemotherapy. <h4>Background and purpose</h4> Predicting tumour response would be useful for selecting patients with locally advanced rectal cancer (LARC) for organ preservation strategies. We aimed to develop and validate a prediction model for T downstaging (ypT0-2) in LARC patients after neoadjuvant chemoradiotherapy and to identify those who may benefit from consolidation chemotherapy. <h4>Materials and methods</h4> cT3-4 LARC patients at three tertiary medical centers from January 2012 to January 2019 were retrospectively included, while a prospective cohort was recruited from June 2021 to March 2022. Eight filter (principal component analysis, least absolute shrinkage and selection operator, partial least-squares discriminant analysis, random forest)-classifier (support vector machine, logistic regression) models were established to select radiomic features. A nomogram combining radiomics and significant clinical features was developed and validated by calibration curve and decision curve analysis. Interaction test was conducted to investigate the consolidation chemotherapy benefits. <h4>Results</h4> A total of 634 patients were included (426 in training cohort, 174 in testing cohort and 34 in prospective cohort). A radiomic prediction model using partial least-squares discriminant analysis and a support vector machine showed the best performance (AUC: 0.832 [training]; 0.763 [testing]). A nomogram combining radiomics and clinical features showed significantly better prognostic performance (AUC: 0.842 [training]; 0.809 [testing]) than the radiomic model. The model was also tested in the prospective cohort with AUC 0.727. High-probability group (score > 81.82) may have potential benefits from ≥ 4 cycles consolidation chemotherapy (OR: 4.173, 95 % CI: 0.953–18.276, p = 0.058, pinteraction = 0.021). <h4>Conclusion</h4> We identified and validated a model based on multicenter pre-treatment radiomics to predict ypT0-2 in cT3-4 LARC patients, which may facilitate individualised treatment decision-making for organ-preservation strategies and consolidation chemotherapy."],"journal":["Clinical and translational radiation oncology"],"pagination":["175-182"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9719068"],"repository":["biostudies-literature"],"pubmed_title":["MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study"],"pmcid":["PMC9719068"],"pubmed_authors":["Liu J","Niu Z","Wang F","Wang H","Geng J","Teng H","Li Y","Song M","Li S","Zhang Y","Xiang Y","Cai Y","Hu K","Zhu X","Wang W","Wang Z"],"additional_accession":[]},"is_claimable":false,"name":"MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study","description":"Highlights • Pre-treatment MRI images were used to construct a prediction model for pathologic T downstaging in locally advanced rectal cancer.• The prediction model showed good performance in training cohort (AUC 0.842), internal testing cohort (AUC 0.809) and prospective cohort (AUC 0.727).• High-probability group (score > 81.82) had potential benefits from sufficient consolidation chemotherapy. <h4>Background and purpose</h4> Predicting tumour response would be useful for selecting patients with locally advanced rectal cancer (LARC) for organ preservation strategies. We aimed to develop and validate a prediction model for T downstaging (ypT0-2) in LARC patients after neoadjuvant chemoradiotherapy and to identify those who may benefit from consolidation chemotherapy. <h4>Materials and methods</h4> cT3-4 LARC patients at three tertiary medical centers from January 2012 to January 2019 were retrospectively included, while a prospective cohort was recruited from June 2021 to March 2022. Eight filter (principal component analysis, least absolute shrinkage and selection operator, partial least-squares discriminant analysis, random forest)-classifier (support vector machine, logistic regression) models were established to select radiomic features. A nomogram combining radiomics and significant clinical features was developed and validated by calibration curve and decision curve analysis. Interaction test was conducted to investigate the consolidation chemotherapy benefits. <h4>Results</h4> A total of 634 patients were included (426 in training cohort, 174 in testing cohort and 34 in prospective cohort). A radiomic prediction model using partial least-squares discriminant analysis and a support vector machine showed the best performance (AUC: 0.832 [training]; 0.763 [testing]). A nomogram combining radiomics and clinical features showed significantly better prognostic performance (AUC: 0.842 [training]; 0.809 [testing]) than the radiomic model. The model was also tested in the prospective cohort with AUC 0.727. High-probability group (score > 81.82) may have potential benefits from ≥ 4 cycles consolidation chemotherapy (OR: 4.173, 95 % CI: 0.953–18.276, p = 0.058, pinteraction = 0.021). <h4>Conclusion</h4> We identified and validated a model based on multicenter pre-treatment radiomics to predict ypT0-2 in cT3-4 LARC patients, which may facilitate individualised treatment decision-making for organ-preservation strategies and consolidation chemotherapy.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-04T07:48:06.549Z","creation":"2025-02-19T04:14:28.141Z"},"accession":"S-EPMC9719068","cross_references":{}}