MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Stu
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ORIGINAL PAPER
MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study Arnaldo Stanzione 1 & Carlo Ricciardi 1 & Renato Cuocolo 1 & Valeria Romeo 1 & Jessica Petrone 1 & Michela Sarnataro 1 & Pier Paolo Mainenti 2 & Giovanni Improta 3 & Filippo De Rosa 1 & Luigi Insabato 1 & Arturo Brunetti 1 & Simone Maurea 1
# Society for Imaging Informatics in Medicine 2020
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. Keywords Renal cell carcinoma . MRI . Radiomics . Machine learning . Fuhrman grade
Introduction Despite being relatively rare, renal cancer is estimated to cause almost 15.000 deaths during 2019 in the USA alone [1]. The most common primary kidney malignancy is the renal cell carcinoma (RCC), with the clear cell subtype (CCRCC) accounting for the large majority of cases [2]. While characterization of these lesions using imaging modalities such as CT and MRI still represents a complex and challenging task, the increasing number of management strategies endorses the need for a more informative pre-treatment assessment to allow * Renato Cuocolo [email protected] 1
Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via S. Pansini, 5, 80123 Naples, Italy
2
Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy
3
Department of Public Health, University of Naples “Federico II”, Naples, Italy
treatment tailoring [3]. Fuhrman grade is a histological predictive factor based on nuclear characteristics which is highly regarded as an independent prognostic factor in patients with CCRCC [4]. It allows to stratify pat
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