ArticleClinical and translational radiation oncology2023
MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study.
Article in Clinical and translational radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Imaging of young-onset colorectal cancer: what the radiologist needs to know.Abdominal radiology (New York) · 2025Review
- Value of enhanced CT machine learning models combined with clinicoradiological characteristics in predicting lymphatic tissue metastasis in colon cancer.Radiologie (Heidelberg, Germany) · 2025Article
- Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment?Journal of gastrointestinal cancer · 2024Article
- Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses.IEEE open journal of engineering in medicine and biology · 2023Article
- Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: 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. Materials and methods: 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. Results: 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, p Conclusion: 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.
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Registered trials
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