Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques

Amit Gupta; Rohan Raju Dhanakshirur; Kshitiz Jain; Sanil Garg; Neel Yadav; Amlesh Seth; Chandan J. Das

doi:10.1055/s-0044-1800804

Indian Journal of Radiology and Imaging, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Indian J Radiol Imaging 2025; 35(03): 395-401
DOI: 10.1055/s-0044-1800804

Original Article

Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques

Autor*innen

Amit Gupta

¹Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
Rohan Raju Dhanakshirur

²Amarnath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi, New Delhi, India
Kshitiz Jain

³Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India
Sanil Garg

¹Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
Neel Yadav

¹Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
Amlesh Seth

⁴Department of Urology, All India Institute of Medical Sciences, New Delhi, India
Chandan J. Das

¹Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India

Abstract

Objective The aim of this study was to explore an innovative approach for developing deep learning (DL) algorithm for renal cell carcinoma (RCC) detection and subtyping on computed tomography (CT): clear cell RCC (ccRCC) versus non-ccRCC using two-dimensional (2D) neural network architecture and feature consistency modules.

Materials and Methods This retrospective study included baseline CT scans from 196 histopathologically proven RCC patients: 143 ccRCCs and 53 non-ccRCCs. Manual tumor annotations were performed on axial slices of corticomedullary phase images, serving as ground truth. After image preprocessing, the dataset was divided into training, validation, and testing subsets. The study tested multiple 2D DL architectures, with the FocalNet-DINO demonstrating highest effectiveness in detecting and classifying RCC. The study further incorporated spatial and class consistency modules to enhance prediction accuracy. Models' performance was evaluated using free-response receiver operating characteristic curves, recall rates, specificity, accuracy, F1 scores, and area under the curve (AUC) scores.

Results The FocalNet-DINO architecture achieved the highest recall rate of 0.823 at 0.025 false positives per image (FPI) for RCC detection. The integration of spatial and class consistency modules into the architecture led to 0.2% increase in recall rate at 0.025 FPI, along with improvements of 0.1% in both accuracy and AUC scores for RCC classification. These enhancements allowed detection of cancer in an additional 21 slices and reduced false positives in 126 slices.

Conclusion This study demonstrates high performance for RCC detection and classification using DL algorithm leveraging 2D neural networks and spatial and class consistency modules, to offer a novel, computationally simpler, and accurate DL approach to RCC characterization.

Keywords

renal cell carcinoma - deep learning - classification - detection - F1 score

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