ArticleEuropean radiology2023
Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- MRI-based assessment of tumor aggressiveness in nasopharyngeal carcinoma: risk stratification and survival prediction.European radiology · 2026Article
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Radiological staging clinical decision support model for rectal cancer lymph node metastasis detection on MRI.BMC cancer · 2026Article
- Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study.BMC oral health · 2026Article
- MRI-based qualitative, quantitative, and radiomics/deep learning methods for assessing treatment response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer.Precision radiation oncology · 2026Review
- Prediction of lymph node metastasis and recurrence risk in early-stage oral tongue squamous cell carcinoma with fully automated MRI deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- Revolutionizing thyroid nodule diagnosis in Hashimoto's thyroiditis: AI-driven radiomics and deep learning model.International journal of surgery (London, England) · 2025Article
- Predicting Lymph Node Metastasis in Rectal Cancer: Development and Validation of a Machine Learning Model Using Clinical Data.JMIR medical informatics · 2025Article
- Multimodal deep learning: tumor and visceral fat impact on colorectal cancer occult peritoneal metastasis.European radiology · 2025Article
- A Multimodal MRI-Based Model for Colorectal Liver Metastasis Prediction: Integrating Radiomics, Deep Learning, and Clinical Features with SHAP Interpretation.Current oncology (Toronto, Ont.) · 2025Article
- Review
- A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer.Frontiers in oncology · 2025Article
- Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer.Frontiers in oncology · 2025Article
- Article
- Role of Artificial Intelligence in MRI-Based Rectal Cancer Staging: A Systematic Review.Cureus · 2024Review
- Clinical and pathological features of advanced rectal cancer with submesenteric root lymph node metastasis: Meta-analysis.World journal of gastrointestinal oncology · 2024Article
- Article
- Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
objectivesThis study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer.
methodsIn this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method.
resultsIn total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001).
conclusionThe DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer. KEY POINTS: • Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.
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