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Klontzas ME, Kalarakis G, Koltsakis E, Papathomas T, Karantanas AH, Tzortzakakis A. Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset. Insights Imaging 2024; 15:26. [PMID: 38270726 PMCID: PMC10811309 DOI: 10.1186/s13244-023-01601-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 12/17/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVES To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset. METHODS A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN. RESULTS Inception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873-0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752-0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844-0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma. CONCLUSION Deep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors. CRITICAL RELEVANCE STATEMENT Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors. KEY POINTS • Differentiation between benign and malignant tumors based on CT is extremely challenging. • Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types. • Deep learning can be used to distinguish between benign and malignant renal tumors.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece
| | - Georgios Kalarakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm, Sweden
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - Emmanouil Koltsakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm, Sweden
| | - Thomas Papathomas
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Department of Clinical Pathology, Vestre Viken Hospital Trust, Drammen, Norway
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece
| | - Antonios Tzortzakakis
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden.
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, 14 186, Huddinge, Stockholm, Sweden.
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Trovato P, Simonetti I, Morrone A, Fusco R, Setola SV, Giacobbe G, Brunese MC, Pecchi A, Triggiani S, Pellegrino G, Petralia G, Sica G, Petrillo A, Granata V. Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics. J Clin Med 2024; 13:547. [PMID: 38256682 PMCID: PMC10816509 DOI: 10.3390/jcm13020547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.
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Affiliation(s)
- Piero Trovato
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Igino Simonetti
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Alessio Morrone
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Giuliana Giacobbe
- General and Emergency Radiology Department, “Antonio Cardarelli” Hospital, 80131 Naples, Italy;
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy;
| | - Annarita Pecchi
- Department of Radiology, University of Modena and Reggio Emilia, 41121 Modena, Italy;
| | - Sonia Triggiani
- Postgraduate School of Radiodiagnostics, University of Milan, 20122 Milan, Italy; (S.T.); (G.P.)
| | - Giuseppe Pellegrino
- Postgraduate School of Radiodiagnostics, University of Milan, 20122 Milan, Italy; (S.T.); (G.P.)
| | - Giuseppe Petralia
- Department of Medical Imaging and Radiation Sciences, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
| | - Giacomo Sica
- Radiology Unit, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
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Toffoli T, Saut O, Etchegaray C, Jambon E, Le Bras Y, Grenier N, Marcelin C. Differentiation of Small Clear Renal Cell Carcinoma and Oncocytoma through Magnetic Resonance Imaging-Based Radiomics Analysis: Toward the End of Percutaneous Biopsy. J Pers Med 2023; 13:1444. [PMID: 37888055 PMCID: PMC10608459 DOI: 10.3390/jpm13101444] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 10/28/2023] Open
Abstract
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.
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Affiliation(s)
- Thibault Toffoli
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Imaging and Interventional Radiology, Hôpital Pellegrin, 33000 Bordeaux, France; (T.T.); (E.J.); (Y.L.B.)
| | - Olivier Saut
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project Team Monc, F-33400 Talence, France; (O.S.); (C.E.); (N.G.)
| | - Christele Etchegaray
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project Team Monc, F-33400 Talence, France; (O.S.); (C.E.); (N.G.)
| | - Eva Jambon
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Imaging and Interventional Radiology, Hôpital Pellegrin, 33000 Bordeaux, France; (T.T.); (E.J.); (Y.L.B.)
| | - Yann Le Bras
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Imaging and Interventional Radiology, Hôpital Pellegrin, 33000 Bordeaux, France; (T.T.); (E.J.); (Y.L.B.)
| | - Nicolas Grenier
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project Team Monc, F-33400 Talence, France; (O.S.); (C.E.); (N.G.)
| | - Clément Marcelin
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Imaging and Interventional Radiology, Hôpital Pellegrin, 33000 Bordeaux, France; (T.T.); (E.J.); (Y.L.B.)
- Bordeaux Institute of Oncology, BRIC U1312, INSERM, Bordeaux University, 33000 Bordeaux, France
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