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Related Experiment Video

Updated: Jun 27, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade

Liang Jin1,2, Zhuo Yu3, Feng Gao2

  • 1Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai, 200040, China.

Insights Into Imaging
|May 7, 2024
PubMed
Summary

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Insights into imaging·2023

A novel deep-learning approach accurately detects prostate cancer and predicts Gleason grade using T2-weighted MRI. This non-invasive method outperforms human experts, potentially reducing the need for biopsies.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate prostate cancer detection and Gleason grade prediction are crucial for effective clinical management.
  • Current methods may involve invasive procedures, highlighting the need for non-invasive alternatives.

Purpose of the Study:

  • To develop and validate a deep-learning model for non-invasive prostate cancer detection.
  • To predict the Gleason grade of prostate cancer using T2-weighted magnetic resonance imaging (MRI).

Main Methods:

  • A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction.
  • Utilized retrospective internal datasets and external validation datasets from multiple centers.
  • Performance was evaluated using the area under the curve (AUC).
Keywords:
CancerGleasonMagnetic resonance imagingProstateRadiologist

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Main Results:

  • The deep-learning model achieved an AUC of 0.918 for prostate cancer detection in the validation set.
  • For Gleason grade prediction, the model demonstrated AUCs of 0.902 (training), 0.854 (validation), 0.776 (external validation), and 0.838 (public challenge).
  • The model outperformed human experts in both detection and prediction tasks.

Conclusions:

  • The proposed deep-learning method effectively detects prostate cancer and predicts Gleason grade using T2-weighted MRI.
  • Multicenter validation confirms the robustness of the deep-learning approach.
  • Non-invasive Gleason grade prediction via this method can potentially decrease unnecessary prostate biopsies.