ArticleAbdominal radiology (New York)2021
An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer.
Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Magnetic Resonance Imaging Evaluation of the Accuracy of Various Lymph Node Staging Criteria in Rectal Cancer: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2021Pooled it
- 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
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Preoperative prediction of lymphatic metastasis in rectal cancer using a fusion model based on multiparameter magnetic resonance imaging: a retrospective validation study.Frontiers in oncology · 2026Article
- 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
- Precision medicine in the prediction of metachronous liver metastasis in rectal cancer: Applications and challenges.World journal of gastrointestinal oncology · 2025Article
- MRI radiomic study on prediction of nonenlarged lymph node metastasis of rectal cancer: reduced field-of-view versus conventional DWI.European radiology experimental · 2025Article
- Development and validation of machine learning-based MRI radiomics models for preoperative lymph node staging in T3 rectal cancer.Frontiers in oncology · 2025Article
- Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: A dual-center study.Journal of cancer research and clinical oncology · 2024Article
- Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.Radiology. Artificial intelligence · 2024Article
- Radiomics in rectal cancer: current status of use and advances in research.Frontiers in oncology · 2024Review
- Progress in the diagnosis of lymph node metastasis in rectal cancer: a review.Frontiers in oncology · 2023Review
- Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer.Frontiers in oncology · 2023Article
- Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer.Frontiers in oncology · 2023Article
- The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma.BMC gastroenterology · 2022Article
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer.World journal of gastroenterology · 2022Article
- Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective.Journal of clinical medicine · 2022Review
- CT and 3 Tesla MRI in the TN Staging of Colon Cancer: A Prospective, Blind Study.Current oncology (Toronto, Ont.) · 2022Article
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6 authors.
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Abstract
purposeTo apply a multi-objective radiomics model based on pre-operative magnetic resonance imaging (MRI) for improving diagnostic accuracy of LN metastasis in rectal cancer patients.
methodsThis study consisted of 91 patients diagnosed with rectal cancer from April 2018 to March 2019. All patients underwent rectal MRI before surgery without any other treatment. Clinical data, subjective radiologist assessments, and radiomic features of LNs were obtained. A total of 1409 radiomic features were extracted from T2WI LN images. Multi-objective optimization with the iterative multi-objective immune algorithm (IMIA) was used to select radiomic features to build prediction models. Predictive performances of radiomic, radiologist, and combined radiomic and radiologist models were assessed for accuracy by receiver operating characteristics (ROC) curves.
resultsFor the radiologist analysis, heterogeneity was the only significant independent predictor of LN status. The sensitivity, specificity, and accuracy of the subjective radiologist analysis were 72.09%, 73.81%, and 78.12%, respectively. The sensitivity, specificity, and accuracy of the solitary radiomic model consisting of 10 features were 89.81%, 82.57%, and 87.77%, respectively. The sensitivity, specificity, and accuracy of the combined model, which consisted of 12 radiomic and radiologist features, were 92.23%, 84.69%, and 89.88%, respectively. The combined model had the best prediction performance with an AUC of 0.94.
conclusionsThe multi-objective radiomics model based on T2WI images was very useful in predicting pre-operative LN status in rectal cancer patients.
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