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<p>This study introduces NemoNet, a novel deep-learning framework designed for the automated detection and staging of Renal Cell Carcinoma (RCC) in 3D CT images. Leveraging the comprehensive HubMAP RCC dataset, NemoNet integrates a 3D encoder-decoder architecture with advanced radiomic feature analysis to enhance tumour segmentation and staging accuracy. The model employs a multi-objective loss function to balance segmentation precision and staging prediction, outperforming traditional architectures like U-Net and ResNet. Evaluation metrics, including Dice Coefficient, sensitivity, and specificity, indicate superior performance, achieving an accuracy of 92% and a Dice score of 0.88. While the model demonstrates robust results, challenges remain in handling variability in imaging quality and achieving full interpretability. The findings suggest that NemoNet offers significant advancements in RCC detection and staging, with potential applications in personalized oncology treatment planning.</p>

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A novel NEMONET framework for enhanced RCC detection and staging in CT images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月15日

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  • Saleh Alyahyan

摘要

This study introduces NemoNet, a novel deep-learning framework designed for the automated detection and staging of Renal Cell Carcinoma (RCC) in 3D CT images. Leveraging the comprehensive HubMAP RCC dataset, NemoNet integrates a 3D encoder-decoder architecture with advanced radiomic feature analysis to enhance tumour segmentation and staging accuracy. The model employs a multi-objective loss function to balance segmentation precision and staging prediction, outperforming traditional architectures like U-Net and ResNet. Evaluation metrics, including Dice Coefficient, sensitivity, and specificity, indicate superior performance, achieving an accuracy of 92% and a Dice score of 0.88. While the model demonstrates robust results, challenges remain in handling variability in imaging quality and achieving full interpretability. The findings suggest that NemoNet offers significant advancements in RCC detection and staging, with potential applications in personalized oncology treatment planning.