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Abstract <p>Prostate cancer remains a leading cause of cancer-related morbidity in men, and its accurate aggressiveness grading is critical for optimizing treatment strategies. While multiparametric MRI (mpMRI) has revolutionized prostate cancer diagnostics through noninvasive visualization of anatomical and functional tissue contrasts, current automated grading frameworks face significant limitations due to interscanner heterogeneity, inadequate modeling of tumor microarchitecture, and insufficient clinical reliability. This study introduces MR-ProGradeNet (Multiresolution Prostate Grading Network), a novel deep learning architecture that systematically addresses these challenges via three synergistic modules. First, the A-HASH preprocessing module unifies modality-specific intensity profiles and spatial representations using learned histogram normalization and prostate-specialized UNet segmentation, ensuring scanner-invariant input harmonization. Second, the FRDF (fractal radiomic deep fusion) module captures fine-grained angiogenic textures and high-level semantic abstractions through dual-path processing of fractal dimension-enhanced DCE MRI and EfficientNet-encoded T2/ADC modalities, fused via gated attention. Finally, the ProMMGrader classification module incorporates label distribution learning and confidence-weighted ensembling across modalities, reflecting Gleason-grade uncertainty and reducing misclassification risk. A comprehensive evaluation on the public PROSTATEx dataset demonstrates that MR-ProGradeNet achieves 97.5% accuracy, a 0.974 F1-score, and an AUC of 0.999, substantially outperforming traditional CNNs and state-of-the-art prostate grading methods. This work represents a clinically aligned and interpretable advancement in AI-based mpMRI analysis, capable of enhancing diagnostic confidence and reproducibility in real-world multicenter settings.</p>

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MR-ProGradeNet: A Multiresolution Deep Learning Framework for Automated Prostate Cancer Grading from MRI

  • 期刊论文

  • 发表日期：

    2026年1月1日

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  • Kranti Vithal Ghag,
  • Jayashree Jagdale,
  • Madhura Vyawahare

摘要

Abstract

Prostate cancer remains a leading cause of cancer-related morbidity in men, and its accurate aggressiveness grading is critical for optimizing treatment strategies. While multiparametric MRI (mpMRI) has revolutionized prostate cancer diagnostics through noninvasive visualization of anatomical and functional tissue contrasts, current automated grading frameworks face significant limitations due to interscanner heterogeneity, inadequate modeling of tumor microarchitecture, and insufficient clinical reliability. This study introduces MR-ProGradeNet (Multiresolution Prostate Grading Network), a novel deep learning architecture that systematically addresses these challenges via three synergistic modules. First, the A-HASH preprocessing module unifies modality-specific intensity profiles and spatial representations using learned histogram normalization and prostate-specialized UNet segmentation, ensuring scanner-invariant input harmonization. Second, the FRDF (fractal radiomic deep fusion) module captures fine-grained angiogenic textures and high-level semantic abstractions through dual-path processing of fractal dimension-enhanced DCE MRI and EfficientNet-encoded T2/ADC modalities, fused via gated attention. Finally, the ProMMGrader classification module incorporates label distribution learning and confidence-weighted ensembling across modalities, reflecting Gleason-grade uncertainty and reducing misclassification risk. A comprehensive evaluation on the public PROSTATEx dataset demonstrates that MR-ProGradeNet achieves 97.5% accuracy, a 0.974 F1-score, and an AUC of 0.999, substantially outperforming traditional CNNs and state-of-the-art prostate grading methods. This work represents a clinically aligned and interpretable advancement in AI-based mpMRI analysis, capable of enhancing diagnostic confidence and reproducibility in real-world multicenter settings.