Abstract:
: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.
journal_name
J Digit Imagingjournal_title
Journal of digital imagingauthors
Stanzione A,Ricciardi C,Cuocolo R,Romeo V,Petrone J,Sarnataro M,Mainenti PP,Improta G,De Rosa F,Insabato L,Brunetti A,Maurea Sdoi
10.1007/s10278-020-00336-ysubject
Has Abstractpub_date
2020-08-01 00:00:00pages
879-887issue
4eissn
0897-1889issn
1618-727Xpii
10.1007/s10278-020-00336-yjournal_volume
33pub_type
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