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Updated: Sep 8, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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A Systematic Review of Multimodal Deep Learning and Machine Learning Fusion Techniques for Prostate Cancer

Farhana Manzoor1, Vibhuti Gupta1, Lubna Pinky2

  • 1Department of Computer Science and Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN 37208, USA.

Medrxiv : the Preprint Server for Health Sciences
|August 20, 2025
PubMed
Summary
This summary is machine-generated.

Multimodal deep learning and machine learning fusion significantly improves prostate cancer classification accuracy. This approach integrates diverse data, outperforming traditional methods and single-data techniques for better diagnosis.

Keywords:
data fusionmultimodalprostate cancer

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Prostate cancer is a leading cause of male cancer deaths globally.
  • Current diagnostic methods (PSA testing, DRE, mpMRI) have limitations in accuracy and consistency.
  • Multimodal fusion of data offers a promising avenue for enhanced prostate cancer classification.

Purpose of the Study:

  • To review state-of-the-art deep learning (DL) and machine learning (ML) fusion techniques for prostate cancer classification.
  • To analyze the implementation, performance, challenges, and clinical applicability of these fusion methods.
  • To provide an overview of recent advancements in AI-driven prostate cancer diagnostics.

Main Methods:

  • Systematic review following PRISMA guidelines.
  • Identification and analysis of 27 studies published between 2021-2025 from an initial pool of 131.
  • Extraction of data on input techniques, DL architectures, performance metrics, and validation strategies.

Main Results:

  • Early fusion with convolutional neural networks was the predominant integration technique.
  • Clinical and imaging data were the most frequently utilized modalities.
  • Multimodal DL/ML fusion demonstrated superior performance compared to unimodal approaches in prostate cancer classification.

Conclusions:

  • Multimodal DL and ML fusion represent a significant advancement in prostate cancer classification.
  • These integrated approaches show potential to overcome limitations of traditional diagnostic methods.
  • Further research and validation are crucial for widespread clinical adoption in prostate cancer diagnostics.