ArticleFrontiers in oncology2021
Comparable Performance of Deep Learning-Based to Manual-Based Tumor Segmentation in KRAS/NRAS/BRAF Mutation Prediction With MR-Based Radiomics 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 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.Journal of gastrointestinal cancer · 2026Pooled it
- Performance of Machine Learning in Diagnosing KRAS (Kirsten Rat Sarcoma) Mutations in Colorectal Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients.BMC medical imaging · 2023Trial
- Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram.The British journal of radiology · 2022Trial
- Habitat-based amide proton transfer-weighted MRI model for predicting BRAF mutation and prognostic stratification in rectal cancer.Frontiers in oncology · 2026Article
- Deep learning model for automated detection of fresh and old vertebral fractures on thoracolumbar CT.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.Journal of medical radiation sciences · 2024Article
- Article
- Using tumor habitat-derived radiomic analysis during pretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review.Annals of coloproctology · 2024Review
- Review
- Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review.Exploration of targeted anti-tumor therapy · 2023Review
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Cleaning radiotherapy contours for radiomics studies, is it worth it? A head and neck cancer study.Clinical and translational radiation oncology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomic features extracted from segmented tumor regions have shown great power in gene mutation prediction, while deep learning-based (DL-based) segmentation helps to address the inherent limitations of manual segmentation. We therefore investigated whether deep learning-based segmentation is feasible in predicting KRAS/NRAS/BRAF mutations of rectal cancer using MR-based radiomics. In this study, we proposed DL-based segmentation models with 3D V-net architecture. One hundred and eight patients' images (T2WI and DWI) were collected for training, and another 94 patients' images were collected for validation. We evaluated the DL-based segmentation manner and compared it with the manual-based segmentation manner through comparing the gene prediction performance of six radiomics-based models on the test set. The performance of the DL-based segmentation was evaluated by Dice coefficients, which are 0.878 ± 0.214 and 0.955 ± 0.055 for T2WI and DWI, respectively. The performance of the radiomics-based model in gene prediction based on DL-segmented VOI was evaluated by AUCs (0.714 for T2WI, 0.816 for DWI, and 0.887 for T2WI+DWI), which were comparable to that of corresponding manual-based VOI (0.637 for T2WI,
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