ArticleCancers2020
Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 58 citations in OpenAlex.
- A cfDNA fragmentomics-based machine learning model for predicting treatment response and recurrence in locally advanced rectal cancer after neoadjuvant chemoradiotherapy.The journal of liquid biopsy · 2026Article
- Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- MRI-Based Radiomics to Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer: A Retrospective Study.Journal of personalized medicine · 2026Article
- MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer: a multicenter study.European radiology · 2026Article
- The Efficacy of a Super-Resolution Reconstruction Radiomics Model Based on T2WI for Predicting Placenta Accreta Spectrum Disorders: A Multicenter Study.Current medical imaging · 2026Article
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- Development of clinical inflammatory models to predict the efficacy of neoadjuvant chemoradiotherapy and survival in patients with locally advanced rectal cancer: a retrospective study.International journal of colorectal disease · 2025Article
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- 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
- Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy.NPJ precision oncology · 2024Article
- An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study.NPJ precision oncology · 2024Article
- The Role of Radiomics in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
- Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer.Radiation oncology (London, England) · 2023Article
- Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.Radiation oncology (London, England) · 2023Article
- Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.Quantitative imaging in medicine and surgery · 2023Article
- MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment.Diagnostics (Basel, Switzerland) · 2023Review
- The Role of an MRI-Based Radiomic Signature in Predicting Malignancy of Parotid Gland Tumors.Cancers · 2023Article
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- Article
- Feasibility of delta radiomics-based pCR prediction for rectal cancer patients treated with magnetic resonance-guided adaptive radiotherapy.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.