ArticleFrontiers in oncology2021
Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 34 citations in OpenAlex.
- MRI assessment of extramural venous invasion in rectal cancer: a practical pictorial review.Abdominal radiology (New York) · 2026Review
- A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.Insights into imaging · 2026Article
- Comprehensive machine learning analysis of a radiomics-based model for predicting microsatellite instability in right Colon Cancer.Frontiers in oncology · 2026Article
- Nomogram predicting overall survival in hospitalized cervical cancer patients in Mato Grosso, Brazil.Discover oncology · 2025Article
- Review
- Review
- The clinical validity of radiomics-based prediction of molecular subtypes in breast cancer from digital mammary tomosynthesis.Frontiers in oncology · 2025Article
- Development and validation of a nomogram for predicting tracheostomy risk in traumatic cervical spinal cord injury.Frontiers in neurology · 2025Article
- Clinical outcome and prognostic factors of T4N0M0 colon cancer after R0 resection: A retrospective study.World journal of gastrointestinal oncology · 2024Article
- The Role of Radiomics in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
- Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography.Surgical endoscopy · 2023Article
- Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte.Journal of translational medicine · 2023Article
- Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer.Frontiers in oncology · 2023Article
- Article
- Article
- Radiomic nomogram for discriminating parotid pleomorphic adenoma from parotid adenolymphoma based on grayscale ultrasonography.Frontiers in oncology · 2023Article
- A Nomogram Model for Prediction of Tracheostomy in Patients With Traumatic Cervical Spinal Cord Injury.Neurospine · 2022Article
- Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging.Frontiers in oncology · 2022Article
- Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges.Journal of oncology · 2022Review
- A nomogram model for predicting prognosis of obstructive colorectal cancer.World journal of surgical oncology · 2021Article
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10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.
methodsThe clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves.
resultsThe radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801.
conclusionThe radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.
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