ArticleAbdominal radiology (New York)2021
High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in 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 54 papers, 2 of them syntheses that pooled it.
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54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- The accuracy of radiomics in diagnosing tumor deposits and perineural invasion in rectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.BMC cancer · 2021Pooled it
- CT-based multi-station lymph-node radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a proof-of-concept comparison with primary-tumor radiomics.European radiology experimental · 2026Article
- MRI Dark-Light-Dark Sign and Node Reporting Score: An Integrated Model for Predicting Lymph Node Metastasis in Rectal Cancer.Journal of clinical medicine · 2026Article
- Development and validation: a whole-tumor histogram model based on intravoxel incoherent motion diffusion-weighted imaging for diagnosing tumor deposits in rectal cancer.Abdominal radiology (New York) · 2026Article
- Letter to the Editor: Perinodal signal in breast magnetic resonance imaging: flare sign and extracapsular spread.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Interpretable MRI Subregional Radiomics-Deep Learning Model for Preoperative Lymphovascular Invasion Prediction in Rectal Cancer: A Dual-Center Study.Journal of imaging informatics in medicine · 2026Article
- MRI-derived radiomics for risk stratification of tumor deposits in rectal cancer: a dual-center study.Insights into imaging · 2026Article
- Magnetic resonance imaging based radiomics for predicting pathogenetic features and survival in rectal cancer.Pathology oncology research : POR · 2026Article
- 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
- Non-invasive multi-phase CT artificial intelligence for predicting pre-treatment enlarged lymph node status in colorectal cancer: a prospective validation study.European radiology · 2025Article
- Research progress in multimodal radiomics of rectal cancer tumors and peritumoral regions in MRI.Abdominal radiology (New York) · 2025Review
- Multiparametric MRI-based radiomics nomogram for noninvasive stratification of HER2 expression status in breast cancer.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
- A novel pN stage prediction model for resectable rectal adenocarcinoma based on preoperative MRI features and multiregional apparent diffusion coefficients.European radiology · 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
- Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.Scientific reports · 2025Article
- Development and validation of machine learning-based MRI radiomics models for preoperative lymph node staging in T3 rectal cancer.Frontiers in oncology · 2025Article
- Machine learning prediction model for lateral lymph node metastasis in rectal cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2025Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo establish and validate two predictive radiomics models for preoperative prediction of lymph node metastases (LNMs) and tumor deposits (TDs) respectively in rectal cancer (RC) patients.
methodsA total of 139 RC patients (98 in the training cohort and 41 in the validation cohort) were enrolled in the present study. High-resolution magnetic resonance images (HRMRI) were retrieved for tumor segmentation and feature extraction. HRMRI findings of RC were assessed by three experienced radiologists. Two radiomics nomograms were established by integrating the clinical risk factors, HRMRI findings and radiomics signature.
resultsThe predictive nomogram of LNMs showed good predictive performance (area under the curve [AUC], 0.90; 95% confidence interval [CI] 0.83-0.96) which was better than clinico-radiological (AUC, 0.83; 95% CI 0.74-0.93; Delong test, p = 0.017) or radiomics signature-only model (AUC, 0.77; 95% CI 0.67-0.86; Delong test, p = 0.003) in training cohort. Application of the nomogram in the validation cohort still exhibited good performance (AUC, 0.87; 95% CI 0.76-0.98). The accuracy, sensitivity and specificity of the combined model in predicting LNMs was 0.86,0.79 and 0.91 in training cohort and 0.83,0.85 and 0.82 in validation cohort. As for TDs, the predictive efficacy of the nomogram (AUC, 0.82; 95% CI 0.71-0.93) was not significantly higher than radiomics signature-only model (AUC, 0.80; 95% CI 0.69-0.92; Delong test, p = 0.71). Radiomics signature-only model was adopted to predict TDs with accuracy=0.76, sensitivity=0.72 and specificity=0.94 in training cohort and 0.68, 0.62 and 0.97 in validation cohort.
conclusionHRMRI-based radiomics models could be helpful for the prediction of LNMs and TDs preoperatively in RC patients.
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