Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention

2026-02-25

AI Summary

Accurately distinguishing benign from malignant renal tumors preoperatively remains challenging, as existing approaches rely on time-consuming and costly manual segmentation to suppress background noise. This work proposes an end-to-end 3D deep learning framework that eliminates the need for manual segmentation by incorporating a novel Organ Focused Attention mechanism, which guides the model to automatically focus on the kidney region without explicit segmentation annotations. An accompanying attention loss function is introduced to effectively reduce dependence on expert-labeled data. The method achieves AUC/F1 scores of 0.685/0.872 on the private UF IDR dataset and 0.760/0.852 on the public KiTS21 dataset, outperforming conventional segmentation-dependent baseline approaches.

Technology Category

• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate prediction of malignancy in renal tumors is crucial for informing clinical decisions and optimizing treatment strategies. However, existing imaging modalities lack the necessary accuracy to reliably predict malignancy before surgical intervention. While deep learning has shown promise in malignancy prediction using 3D CT images, traditional approaches often rely on manual segmentation to isolate the tumor region and reduce noise, which enhances predictive performance. Manual segmentation, however, is labor-intensive, costly, and dependent on expert knowledge. In this study, a deep learning framework was developed utilizing an Organ Focused Attention (OFA) loss function to modify the attention of image patches so that organ patches attend only to other organ patches. Hence, no segmentation of 3D renal CT images is required at deployment time for malignancy prediction. The proposed framework achieved an AUC of 0.685 and an F1-score of 0.872 on a private dataset from the UF Integrated Data Repository (IDR), and an AUC of 0.760 and an F1-score of 0.852 on the publicly available KiTS21 dataset. These results surpass the performance of conventional models that rely on segmentation-based cropping for noise reduction, demonstrating the frameworks ability to enhance predictive accuracy without explicit segmentation input. The findings suggest that this approach offers a more efficient and reliable method for malignancy prediction, thereby enhancing clinical decision-making in renal cancer diagnosis.

Problem

• renal tumor
• malignancy prediction
• 3D CT
• manual segmentation
• deep learning

Innovation

• Organ Focused Attention
• deep learning
• 3D CT
• malignancy prediction
• segmentation-free