ArticleAbdominal radiology (New York)2021
A radiomics-based nomogram for preoperative T staging prediction of rectal cancer.
Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- MRI radiomics prediction modelling for pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- The accuracy of radiomics in diagnosing tumor deposits and perineural invasion in rectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- MST-Net: a multi-scale spatial transformation network for automated T2/T3 staging of rectal cancer on preoperative T2-weighted MRI.Insights into imaging · 2026Article
- Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.BMC cancer · 2026Article
- MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.International journal of colorectal disease · 2025Article
- A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer.BMC cancer · 2025Article
- Oral contrast-enhanced ultrasonographic features and radiomics analysis to predict NIH risk stratification for gastrointestinal stromal tumors.Frontiers in oncology · 2025Article
- Preoperative discrimination of invasive and non-invasive breast cancer using machine learning based on automated breast volume scanning (ABVS) radiomics and virtual touch quantification (VTQ).Discover oncology · 2024Article
- Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3.BMC gastroenterology · 2024Article
- Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study.Journal of bone oncology · 2024Article
- A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer.Abdominal radiology (New York) · 2024Article
- T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.World journal of gastrointestinal oncology · 2024Article
- MRI/RNA-Seq-Based Radiogenomics and Artificial Intelligence for More Accurate Staging of Muscle-Invasive Bladder Cancer.International journal of molecular sciences · 2023Article
- Technological Advancements in Interventional Oncology.Diagnostics (Basel, Switzerland) · 2023Review
- Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer.European radiology · 2023Article
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI.Frontiers in oncology · 2022Article
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10 authors.
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Abstract
purposeTo investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer.
methodsA total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram.
resultsThe Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835-0.930) in the training dataset and 0.846 (95% CI 0.757-0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730-0.987).
conclusionA radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.
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