What question did this study set out to answer?

This study aims to create and validate a deep learning model for classifying renal tumors prior to treatment.

May 7, 2026Open Access

Multimodal deep learning model for multiclass classification of renal tumors

SLShiwei LuoCentral South University QQQuan QuanChinese Academy of Sciences RYRuimeng YangCity Of Hope National Medical Center

Key Points

This study aims to create and validate a deep learning model for classifying renal tumors prior to treatment.
Developed Multi-Phase Attention Network (MPANet) using multiphase contrast-enhanced CT and clinical data.
Enrolled 1688 cases from multiple centers for model validation and comparison.
Assessed performance against single-phase models and expert radiologist evaluations.
MPANet achieved a macro-average AUC of 0.850 and accuracy of 73.3% in the internal test set.
MPANet's performance outperformed four radiologists' assessments (accuracies between 43.6% and 62.4%).
In external test sets, MPANet reached macro-average AUCs of 0.811 and 0.813.

Abstract

Accurate classification of renal masses before treatment is crucial for therapeutic decision-making and patient outcome. This study developed and validated Multi-Phase Attention Network (MPANet), a multimodal deep learning model integrating multiphase contrast-enhanced CT and clinical information, which can utilize both complete-phase and missing-phase CT data for multiclass classification of four common and easily confusable renal tumors-clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), oncocytic neoplasms (including chromophobe renal cell carcinoma (chRCC) and renal oncocytoma (RO)), and fat-poor angiomyolipoma (fpAML). A total of 1688 multi-center cases were enrolled. Across all test sets, MPANet consistently outperformed single-phase models. In the internal test set, MPANet achieved a macro-average AUC of 0.850, a micro-average AUC of 0.865, and an accuracy of 73.3%. These results compared favorably to assessments by four radiologists based on CT (accuracies 43.6-62.4%) and two radiologists using MRI with clear cell likelihood score (ccLS) system (accuracies 52.5% and 49.5%). The net improvement rate of MPANet over radiologist assessment ranged from 10.9% to 29.7%. In the two external test sets, macro-average AUCs were 0.811 and 0.813, and micro-average AUCs were 0.867 and 0.909, respectively. MPANet shows potential as a clinical decision-support tool for personalized renal tumor diagnosis.

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