MeD-3D: A Multimodal Deep Learning Framework for Precise Recurrence Prediction in Clear Cell Renal Cell Carcinoma (ccRCC)

• URL: http://arxiv.org/abs/2507.07839v1
• Date: Thu, 10 Jul 2025 15:11:09 GMT
• Title: MeD-3D: A Multimodal Deep Learning Framework for Precise Recurrence Prediction in Clear Cell Renal Cell Carcinoma (ccRCC)
• Authors: Hasaan Maqsood, Saif Ur Rehman Khan,
• Abstract summary: This study proposes a deep learning (DL) framework that integrates multimodal data, including CT, MRI, histopathology whole slide images (WSI), clinical data, and genomic profiles.<n>The framework is designed to handle incomplete data, a common challenge in clinical settings, by enabling inference even when certain modalities are missing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of recurrence in clear cell renal cell carcinoma (ccRCC) remains a major clinical challenge due to the disease complex molecular, pathological, and clinical heterogeneity. Traditional prognostic models, which rely on single data modalities such as radiology, histopathology, or genomics, often fail to capture the full spectrum of disease complexity, resulting in suboptimal predictive accuracy. This study aims to overcome these limitations by proposing a deep learning (DL) framework that integrates multimodal data, including CT, MRI, histopathology whole slide images (WSI), clinical data, and genomic profiles, to improve the prediction of ccRCC recurrence and enhance clinical decision-making. The proposed framework utilizes a comprehensive dataset curated from multiple publicly available sources, including TCGA, TCIA, and CPTAC. To process the diverse modalities, domain-specific models are employed: CLAM, a ResNet50-based model, is used for histopathology WSIs, while MeD-3D, a pre-trained 3D-ResNet18 model, processes CT and MRI images. For structured clinical and genomic data, a multi-layer perceptron (MLP) is used. These models are designed to extract deep feature embeddings from each modality, which are then fused through an early and late integration architecture. This fusion strategy enables the model to combine complementary information from multiple sources. Additionally, the framework is designed to handle incomplete data, a common challenge in clinical settings, by enabling inference even when certain modalities are missing.

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