ArticleFrontiers in oncology2021
Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas.
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 11 papers.
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11 citing papers in PubMed, 21 citations in OpenAlex.
- Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.BMC cancer · 2026Article
- Clinical characteristics and survival outcomes of rectal cancer patients across different mrT3 substages determined by rectal MRI.International journal of colorectal disease · 2025Article
- Review
- Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer.Abdominal radiology (New York) · 2024Article
- Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.Insights into imaging · 2024Article
- Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features.Frontiers in oncology · 2024Article
- A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study.BMC medical imaging · 2023Article
- Radiomic features derived from pretherapeutic MRI predict chemoradiation response in locally advanced rectal cancer.Journal of the Chinese Medical Association : JCMA · 2023Article
- Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models.Frontiers in oncology · 2023Article
- Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma.European journal of radiology open · 2023Article
- MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery.Journal of neuro-oncology · 2022Article
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2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveTo determine whether there is a correlation between texture features extracted from high-resolution T2-weighted imaging (HR-T2WI) or apparent diffusion coefficient (ADC) maps and the preoperative T stage (stages T1-2 MATERIALS AND
methodsOne hundred and fifty four patients with rectal carcinomas who underwent preoperative HR-T2WI and diffusion-weighted imaging were enrolled. Patients were divided into training (n = 89) and validation (n = 65) cohorts. 3D Slicer was used to segment the entire volume of interest for whole tumors based on HR-T2WI and ADC maps. The least absolute shrinkage and selection operator (LASSO) was performed to select feature. The significantly difference was tested by the independent sample t-test and Mann-Whitney U test. The support vector machine (SVM) model was used to develop classification models. The correlation between features and T stage was assessed by Spearman's correlation analysis. Multivariate logistic regression analysis was performed to identify independent predictors of tumor invasion. The performance of classifiers was evaluated by the receiver operating characteristic (ROC) curves.
resultsThe wavelet HHH NGTDM strength (
conclusionsTexture analysis based on ADC maps shows more potential than HR-T2WI in identifying preoperative T stage in rectal carcinomas. The combined application of HR-T2WI and ADC maps may help to improve the accuracy of preoperative diagnosis of rectal cancer invasion.
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