ArticleFrontiers in oncology2026
Multimodal deep learning for prediction of postoperative recurrence in clear cell renal cell carcinoma: a clinical-radiologic-pathologic approach.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: This study intends to develop and validate a multiclass system that efficiently integrates complementary information from patients' multimodal data (clinical, imaging, and pathological) to identify individuals at a high risk of 5-year postoperative recurrence among patients with clear cell renal cell carcinoma (ccRCC). Methods: This retrospective multicenter study enrolled 270 clear cell renal cell carcinoma (ccRCC) patients, allocating 215 (79.6%) for model development/validation and 55 (20.4%) to an independent test cohort. Patients were categorized into recurrence and non-recurrence groups based on whether tumor recurrence occurred within 5 years after surgery. The final included case in this cohort underwent surgery in July 2018. Single-modality models were created using radiomics, deep learning (ResNet34), and deep features-based radiomics. Subsequently, multi-modality models were constructed by combining the predicted probabilities from the single-modality models and transferring them to another classifier. All models was assessed using the area under the receiver operating characteristic curve (AUROC). Results: A total of 25 classification models were constructed. Notably, single-modality fusion models generally outperform their radiomics and deep learning-based radiomics (DL) counterparts. Among five single-modality fusion models, the Fused_Non-enhanced model demonstrates best predictive performance, achieving an AUROC value of 0.837 (95% confidence interval [CI]: 0.729-0.946). Similarly, multi-modality radiomics or DL models exhibit superior performance compared to single-modality counterparts. The multi-modality radiomics-DL model demonstrates the highest prediction performance, achieving an AUROC value of 0.967 (95% CI: 0.929-1.0) in the independent testing dataset. Conclusion: The multi-modality radiomics-DL model demonstrates high accuracy in predicting the 5-year postoperative recurrence risk of clear cell renal cell carcinoma (ccRCC).
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