Despite technological advances in diagnostic imaging, to distinguish the type of renal tumor without performing a biopsy is still an unsolved challenge. In particular, this is even more striking in the case of clear cell renal cell carcinoma and small oncocytomas. To tackle this problem, a fully automated tool is proposed that can provide decision support for physicians to distinguish between these two types of masses in the most critical cases. In this work three approaches for the development of this tool are implemented and compared, specifically two approaches are based on the use of radiomic features and one on the use of deep features. The nnU-net is exploited to achieve tumor segmentation necessary to obtain the different types of features. The architectures are trained and tested by combining two different datasets, the public dataset KiTS2019 and data from the Careggi University Hospital. The best method is able to obtain 73.77% balanced accuracy, 94.59% sensitivity, 52.94% specificity and 86.84% accuracy.

Distinguishing Kidney Tumor Types Using Radiomics Features and Deep Features / Magherini, Roberto; Servi, Michaela; Volpe, Yary; Campi, Riccardo; Buonamici, Francesco. - In: IEEE ACCESS. - ISSN 2169-3536. - ELETTRONICO. - 12:(2024), pp. 84241-84252. [10.1109/access.2024.3412655]

Distinguishing Kidney Tumor Types Using Radiomics Features and Deep Features

Magherini, Roberto
;
Servi, Michaela;Volpe, Yary;Campi, Riccardo;Buonamici, Francesco
2024

Abstract

Despite technological advances in diagnostic imaging, to distinguish the type of renal tumor without performing a biopsy is still an unsolved challenge. In particular, this is even more striking in the case of clear cell renal cell carcinoma and small oncocytomas. To tackle this problem, a fully automated tool is proposed that can provide decision support for physicians to distinguish between these two types of masses in the most critical cases. In this work three approaches for the development of this tool are implemented and compared, specifically two approaches are based on the use of radiomic features and one on the use of deep features. The nnU-net is exploited to achieve tumor segmentation necessary to obtain the different types of features. The architectures are trained and tested by combining two different datasets, the public dataset KiTS2019 and data from the Careggi University Hospital. The best method is able to obtain 73.77% balanced accuracy, 94.59% sensitivity, 52.94% specificity and 86.84% accuracy.
2024
12
84241
84252
Magherini, Roberto; Servi, Michaela; Volpe, Yary; Campi, Riccardo; Buonamici, Francesco
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1365432
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