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[2602.22381] Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention
Summary
This article presents a novel deep learning framework for predicting malignancy in renal tumors using 3D CT images, eliminating the need for manual segmentation and improving predictive accuracy.
Why It Matters
Accurate malignancy prediction in renal tumors is vital for effective treatment planning. This study introduces a more efficient method that enhances predictive performance without labor-intensive segmentation, potentially transforming clinical practices in oncology.
Key Takeaways
- The proposed framework uses an Organ Focused Attention loss function to enhance prediction accuracy.
- It achieves higher AUC and F1-scores compared to traditional segmentation-based models.
- The method reduces reliance on expert knowledge and manual processes, streamlining clinical workflows.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.22381 (cs) [Submitted on 25 Feb 2026] Title:Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention Authors:Zhengkang Fan, Chengkun Sun, Russell Terry, Jie Xu, Longin Jan Latecki View a PDF of the paper titled Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention, by Zhengkang Fan and 4 other authors View PDF HTML (experimental) 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 t...
