ArticleEBioMedicine2021
Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study.
Article in EBioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed.
- Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.JAMA network open · 2022Trial
- Deep Learning-Based Multimodal Prediction of Neoadjuvant Chemotherapy Response in Locally Advanced Rectal Cancer by Integrating MRI and Proteomics.Cancer research and treatment · 2026Article
- CT-derived extracellular volume fraction and AI-assisted body composition for predicting neoadjuvant therapy response and prognosis in colorectal cancer.Abdominal radiology (New York) · 2026Article
- A CT-based deep learning model with biological interpretation for predicting early recurrence after neoadjuvant therapy in gastric cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.Scientific reports · 2026Article
- Application of deep learning on MRI for prognostic prediction in rectal cancer.European radiology · 2026Article
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- A multimodal interpretable deep learning-radiomics framework for predicting lymph node metastasis following neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter validation study.NPJ precision oncology · 2026Article
- Personalized neoadjuvant treatment regimen selection in locally advanced rectal cancer based on regimen-specific response modeling.NPJ digital medicine · 2026Article
- Macro Habitat-Based T2-Weighted MRI Radiomics and Deep Learning Fusion for Predicting Treatment Response and Prognosis After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.Cancer medicine · 2026Article
- 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
- Interpretable machine learning models based on CT radiomics for predicting chemoradiotherapy response in rectal cancer.BMC medical imaging · 2026Article
- Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.World journal of gastrointestinal oncology · 2026Review
- Role of MRI radiomics in deep learning-based prediction of intestinal diseases.International journal of colorectal disease · 2026Review
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Deep learning radiomics models based on contrast-enhanced transrectal ultrasound for predicting distant metastasis in rectal cancer.Frontiers in oncology · 2026Article
- Preoperative CT deep learning of tumor and visceral adipose tissue combined with postoperative pathology predicts colorectal cancer recurrence: a multicenter study.Frontiers in molecular biosciences · 2026Article
- A computed tomography-based deep learning radiomics for predicting the response to neoadjuvant chemotherapy combined with immunotherapy in patients with locally advanced esophageal cancer: a multicenter cohort study.Journal of thoracic disease · 2025Article
- Progress of MRI-based radiomics and deep learning for predicting the prognosis of locally advanced rectal cancer (Review).Oncology letters · 2025Review
- Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction.European radiology · 2025Review
14 more citing papers are in PubMed but not listed here.
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Abstract
backgroundAccurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics.
methodsWe retrospectively sampled 235 patients receiving nCRT with the minimum 36 months' postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram.
findingsDLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P < 0·05).
interpretationMRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT.
fundingThe funding bodies that contributed to this study are listed in the Acknowledgements section.
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