ArticleBritish journal of cancer2022
MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer.
Article in British journal of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- MRI radiomics prediction modelling for pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients.BMC medical imaging · 2023Trial
- Article
- Integrating CT-Based Radiomics Analysis With Serum CEA Level to Predict Neoadjuvant Chemoradiotherapy Response in Colorectal Cancer.The Kaohsiung journal of medical sciences · 2026Article
- MRI-Based Radiomics to Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer: A Retrospective Study.Journal of personalized medicine · 2026Article
- Macro Habitat-Based T2-Weighted MRI Radiomics and Deep Learning Fusion for Predicting Treatment Response and Prognosis After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.Cancer medicine · 2026Article
- MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer: a multicenter study.European radiology · 2026Article
- Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer.Oncology letters · 2026Article
- Development and validation of a predictive model for tumor regression following neoadjuvant chemoradiotherapy in rectal cancer.Updates in surgery · 2025Article
- Research progress in multimodal radiomics of rectal cancer tumors and peritumoral regions in MRI.Abdominal radiology (New York) · 2025Review
- Targeting tumoral heterogeneity in lung cancer: a novel, CT-texture-guided targeted biopsy approach with exome sequencing.NPJ precision oncology · 2025Article
- Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction.European radiology · 2025Review
- The optimal time interval between neoadjuvant chemoradiotherapy and surgery for patients with an unfavorable pathological response in locally advanced rectal cancer: a retrospective cohort study.Frontiers in oncology · 2025Article
- Development and Cross-Institutional Validation of a Comprehensive Machine Learning Model Predicting Response to Neoadjuvant Therapy for Rectal Cancer.Cancer management and research · 2025Article
- Diagnostic Value of Plasma Long Non-coding SLC26A4 Antisense RNA 1 Combined with Magnetic Resonance Imaging in Rectal Cancer.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2024Article
- 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
- Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2024Article
- Article
- Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.BMC medical imaging · 2024Article
- T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.World journal of gastrointestinal oncology · 2024Article
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC).
methodsBaseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models' development and validation.
resultsWe finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance.
conclusionsThe performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC's true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.
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