ArticleTranslational cancer research2020
T stage prediction of colorectal tumor based on multiparametric functional images.
Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Fully automated 3D multi-modal deep learning model for preoperative T-stage prediction of colorectal cancer usingEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma.European journal of radiology open · 2023Article
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4 authors at 3 institutions in 1 country.
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Abstract
backgroundRecent studies have shown radiomics parameters of functional imaging have predictive values in many diseases. This study was to investigate the value of radiomics parameters of both computed tomography (CT) and magnetic resonance imaging (MRI) in predicting T stage of colorectal cancer (CRC).
methodsImaging findings of CT and MRI (both cT1-W and T2-W) and clinical information were collected from 29 patients. A total of 330 radiomics parameters were computed from manually annotated medical images, and a lasso regression model with 10-fold cross validation was employed to predict the T stage with radiomics parameters.
resultsThe lasso regression model showed good performance with area under the curve (AUC) of 0.85. A total of three parameters from MRI were used in this model, while no CT findings were included in this model. The 3 selected parameters were from first-order parameters' group, which include energy and totalenergy from both cT1-W and T2-W. These parameters indicate the magnitude of pixels in the medical images.
conclusionsThis study indicates that some radiomics parameters of functional images have predictive values in T staging of CRC. Also, MRI may be more valuable than CT based on the image findings with lasso regression.
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