ArticleScientific reports2025
Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- MRI Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer.Journal of magnetic resonance imaging : JMRI · 2026Article
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Letter to the Editor: MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer-a multicenter study.European radiology · 2026Article
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Diagnostic Performance of a Deep Learning-Based Tool for the Detection and Staging of Rectal Cancers on Endoscopic Ultrasound: Prospective Study.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal radiomics for precision management of colorectal cancer.Discover oncology · 2026Review
- MRI-Based Morphological Features as Predictors of Clinical Outcomes in Locally Advanced Rectal Cancer Treated with Neoadjuvant Chemoradiotherapy: Insights from a Single-Institution Experience.Journal of clinical medicine · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- A novel MRI-based habitat analysis and deep learning for predicting perineural invasion in prostate cancer: a two-center study.BMC cancer · 2025Article
- Explanation and Elaboration with Examples for METRICS (METRICS-E3): an initiative from the EuSoMII Radiomics Auditing Group.Insights into imaging · 2025Article
- Comparative analysis of deep learning and radiomics models in predicting hepatocellular carcinoma differentiation via ultrasound.Frontiers in medicine · 2025Article
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Authors and funding
8 authors.
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Abstract
To explore the value of applying the MRI-based radiomic nomogram for predicting lymph node metastasis (LNM) in rectal cancer (RC). This retrospective analysis used data from 430 patients with RC from two medical centers. The patients were categorized into the LNM negative (LNM-) and LNM positive (LNM+) according to their surgical pathology results. We developed a physician model by selecting clinical independent predictors through physician assessments. Additionally, we developed deep learning radscore (DLRS) models by extracting deep features from multiparametric MRI (mpMRI) images. A nomogram model was constructed by combining the physician model and DLRS models. Among the patients, 192 (44.65%, 192/430) experienced LNM+. Six prediction models were developed, namely the physician model, three sequence models, the DLRS, and the nomogram. The physician model achieved AUC of the receiver operating characteristic (ROC) values of 0.78, 0.79, and 0.7, whereas the sequence models, DLRS model, and nomogram model achieved AUC values ranging from 0.83 to 0.99. The predictive performance of the DLRS and nomogram models was superior to that of the physician model. DLRS and nomogram models based on mpMRI provided higher accuracy in predicting LNM status in patients with RC than the other models.
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