MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study (75 views)
J Digit Imaging (ISSN: 0897-1889linking), 2020 Aug; 33(4): 879-887.
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Paper type: Journal Article,
Impact factor: 3.697, 5-year impact factor: 0
Url: Not available.
Keywords: Fuhrman Grade, Machine Learning, Radiomics, Renal Cell Carcinoma,
Affiliations:
*** IBB - CNR ***
Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy.
Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy.
Department of Public Health, University of Naples "Federico II", Naples, Italy.
References:
Not available.
J Digit Imaging (ISSN: 0897-1889linking), 2020 Aug; 33(4): 879-887.
Export to BibTeX Export to EndNote
Paper type: Journal Article,
Impact factor: 3.697, 5-year impact factor: 0
Url: Not available.
Keywords: Fuhrman Grade, Machine Learning, Radiomics, Renal Cell Carcinoma,
Affiliations:
*** IBB - CNR ***
Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy.
Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy.
Department of Public Health, University of Naples "Federico II", Naples, Italy.
References:
Not available.
MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study
The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.
The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.
MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study
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MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study
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16 Records (14 excluding Abstracts).
Total impact factor: 34.881 (28.714 excluding Abstracts).
Total 5 year impact factor: 27.394 (21.139 excluding Abstracts).
Total impact factor: 34.881 (28.714 excluding Abstracts).
Total 5 year impact factor: 27.394 (21.139 excluding Abstracts).
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