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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.