ArticleAbdominal radiology (New York)2023
MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer.
Article in Abdominal radiology (New York), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- Development and validation of an interpretable MRI-based multimodal fusion model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter study.Abdominal radiology (New York) · 2026Article
- A multimodal interpretable deep learning-radiomics framework for predicting lymph node metastasis following neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter validation study.NPJ precision oncology · 2026Article
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Predicting postoperative recurrence in colorectal cancer using MRI-derived radiomics features and their correlation with the tumor immune microenvironment.Gastroenterology report · 2026Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Preoperative magnetic resonance imaging-based radiomics nomogram model for predicting postoperative anastomotic leakage in colorectal cancer.Annals of coloproctology · 2025Article
- The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Review
- Multi-Institutional MR-Derived Radiomics to Predict Post-Exenteration Disease Recurrence in Patients With T4 Rectal Cancer.Cancer medicine · 2025Article
- Article
- Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.Insights into imaging · 2024Article
- Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma.Insights into imaging · 2024Article
- Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer.World journal of gastrointestinal oncology · 2024Article
- Ultrasound radiomics signature for predicting central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma.Thyroid research · 2024Article
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6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND
methodsEighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up.
resultsClinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030).
conclusionClinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.
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