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Article

A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)

1
Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
2
SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France
3
Department of Urology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(15), 2548; https://doi.org/10.3390/diagnostics13152548
Submission received: 21 February 2023 / Revised: 5 July 2023 / Accepted: 25 July 2023 / Published: 31 July 2023
(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

Abstract

Background: Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. Purpose: This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. Materials and methods: A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. Results: A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/− 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and “intensity mean value” was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. Conclusion: Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.
Keywords: renal tumors; radiomics; RCC; CT; machine learning renal tumors; radiomics; RCC; CT; machine learning

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MDPI and ACS Style

Garnier, C.; Ferrer, L.; Vargas, J.; Gallinato, O.; Jambon, E.; Le Bras, Y.; Bernhard, J.-C.; Colin, T.; Grenier, N.; Marcelin, C. A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics 2023, 13, 2548. https://doi.org/10.3390/diagnostics13152548

AMA Style

Garnier C, Ferrer L, Vargas J, Gallinato O, Jambon E, Le Bras Y, Bernhard J-C, Colin T, Grenier N, Marcelin C. A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics. 2023; 13(15):2548. https://doi.org/10.3390/diagnostics13152548

Chicago/Turabian Style

Garnier, Cassandre, Loïc Ferrer, Jennifer Vargas, Olivier Gallinato, Eva Jambon, Yann Le Bras, Jean-Christophe Bernhard, Thierry Colin, Nicolas Grenier, and Clément Marcelin. 2023. "A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)" Diagnostics 13, no. 15: 2548. https://doi.org/10.3390/diagnostics13152548

APA Style

Garnier, C., Ferrer, L., Vargas, J., Gallinato, O., Jambon, E., Le Bras, Y., Bernhard, J.-C., Colin, T., Grenier, N., & Marcelin, C. (2023). A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics, 13(15), 2548. https://doi.org/10.3390/diagnostics13152548

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