ArticleFrontiers in oncology2022
MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer.
Article in Frontiers in oncology, 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.
- Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Article
- Radiomic prediction of early progression at 2 years post-treatment in patients with resectable rectal cancer based on rectal tumor and mesentery characteristics: a two-center study.Journal of gastrointestinal oncology · 2026Article
- MRI-based multiregional radiomics for pretreatment prediction of pathologic complete response to neoadjuvant chemoradiation therapy in locally advanced rectal cancer: a bicenter study.Abdominal radiology (New York) · 2026Article
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- Multiphase CT-Based Tumor and Peritumoral Radiomics for Characterization of Clear Cell Renal Cell Carcinoma.Journal of imaging informatics in medicine · 2026Article
- Generative adversarial networks: multiparametric, multiregion super-resolution MRI in predicting lymph node metastasis in rectal cancer.Insights into imaging · 2026Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Preoperative prediction of tumor deposits in advanced gastric cancer using intratumoral and peritumoral CT radiomics: development and validation of an ensemble model.Frontiers in oncology · 2026Article
- MRI-based radiomics nomogram to predict complete response to definitive chemoradiation in patients with anal cancer.The British journal of radiology · 2025Article
- Reliability study of MRI in detecting lymph node metastasis of cervical cancer: a multi-center retrospective study result analysis.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning model for predicting lymph node metastasis around rectal cancer based on rectal tumor core area and mesangial imaging features.BMC medical imaging · 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
- Utilizing intratumoral and peritumoral ultrasound radiomics for predicting KRAS gene mutation status in rectal cancer patients.Journal of applied clinical medical physics · 2025Article
- Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.Scientific reports · 2025Article
- Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging.Annals of surgical oncology · 2025Article
- Beyond the tumor region: Peritumoral radiomics enhances prognostic accuracy in locally advanced rectal cancer.World journal of gastroenterology · 2025Article
- Multi-Institutional MR-Derived Radiomics to Predict Post-Exenteration Disease Recurrence in Patients With T4 Rectal Cancer.Cancer medicine · 2025Article
- Diagnostic performance of Node-RADS score for mesorectal lymph node metastasis in rectal cancer.Abdominal radiology (New York) · 2025Article
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5 authors.
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Abstract
Purpose: To establish and evaluate multiregional T2-weighted imaging (T2WI)-based clinical-radiomics model for predicting lymph node metastasis (LNM) and prognosis in patients with resectable rectal cancer. Methods: A total of 346 patients with pathologically confirmed rectal cancer from two hospitals between January 2019 and December 2021 were prospectively enrolled. Intra- and peritumoral features were extracted separately, and least absolute shrinkage and selection operator regression was applied for feature selection. Radiomics signatures were built using the selected features from different regions. The clinical-radiomic nomogram was developed by combining the intratumoral and peritumoral radiomics signatures score (radscore) and the most predictive clinical parameters. The diagnostic performances of the nomogram and clinical model were evaluated using the area under the receiver operating characteristic curve (AUC). The prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis. Results: The intratumoral radscore (radscore 1) included four features, the peritumoral radscore (radscore 2) included five features, and the combined intratumoral and peritumoural radscore (radscore 3) included ten features. The AUCs for radscore 3 were higher than that of radscore 1 in training cohort (0.77 vs. 0.71, Conclusion: A clinical-radiomics nomogram exhibits good performance for predicting preoperative LNM. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed clinical potential for assessing 3-year RFS.
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