Differentiation of Small Clear Renal Cell Carcinoma and Oncocytoma through Magnetic Resonance Imaging-Based Radiomics Analysis: Toward the End of Percutaneous Biopsy

Purpose: The aim of this study was to ascertain whether radiomics data can assist in differentiating small (<4 cm) clear cell renal cell carcinomas (ccRCCs) from small oncocytomas using T2-weighted magnetic resonance imaging (MRI). Material and Methods: This retrospective study incorporated 48 tu...

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Bibliographic Details
Published in:	Journal of personalized medicine Vol. 13; no. 10; p. 1444
Main Authors:	Toffoli, Thibault, Saut, Olivier, Etchegaray, Christele, Jambon, Eva, Le Bras, Yann, Grenier, Nicolas, Marcelin, Clément
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 28-09-2023 MDPI
Subjects:	Algorithms Biopsy Cancer Cell differentiation Classification Clear cell-type renal cell carcinoma Feature selection Kidney cancer Machine learning Magnetic resonance imaging Mann-Whitney U test Medical imaging equipment Patients Precision medicine Radiomics Scanners Statistical analysis Surgery Tumors France
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Summary:	Purpose: The aim of this study was to ascertain whether radiomics data can assist in differentiating small (<4 cm) clear cell renal cell carcinomas (ccRCCs) from small oncocytomas using T2-weighted magnetic resonance imaging (MRI). Material and Methods: This retrospective study incorporated 48 tumors, 28 of which were ccRCCs and 20 were oncocytomas. All tumors were less than 4 cm in size and had undergone pre-biopsy or pre-surgery MRI. Following image pre-processing, 102 radiomics features were evaluated. A univariate analysis was performed using the Wilcoxon rank-sum test with Bonferroni correction. We compared multiple radiomics pipelines of normalization, feature selection, and machine learning (ML) algorithms, including random forest (RF), logistic regression (LR), AdaBoost, K-nearest neighbor, and support vector machine, using a supervised ML approach. Results: No statistically significant features were identified via the univariate analysis with Bonferroni correction. The most effective algorithm was identified using a pipeline incorporating standard normalization, RF-based feature selection, and LR, which achieved an area under the curve (AUC) of 83%, accuracy of 73%, sensitivity of 79%, and specificity of 65%. Subsequently, the most significant features were identified from this algorithm, and two groups of uncorrelated features were established based on Pearson correlation scores. Using these features, an algorithm was established after a pipeline of standard normalization and LR, achieving an AUC of 90%, an accuracy of 77%, sensitivity of 83%, and specificity of 69% for distinguishing ccRCCs from oncocytomas. Conclusions: Radiomics analysis based on T2-weighted MRI can aid in distinguishing small ccRCCs from small oncocytomas. However, it is not superior to standard multiparameter renal MRI and does not yet allow us to dispense with percutaneous biopsy.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2075-4426 2075-4426
DOI:	10.3390/jpm13101444