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Related Experiment Video

Updated: May 17, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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A Multiparametric MRI and Baseline-Clinical-Feature-Based Dense Multimodal Fusion Artificial Intelligence (MFAI)

Dianning He1, Haoming Zhuang2, Ying Ma2

  • 1School of Health Management, China Medical University, Shenyang 110122, China.

Cancers
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

A new AI model accurately predicts if prostate cancer (PCa) patients will develop hormone-resistant prostate cancer (CRPC) within 12 months. This tool aids urologists in treatment planning and prognosis for PCa patients.

Keywords:
castration-resistant prostate cancerdeep learningmpMRIprostate cancerradiomics

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Area of Science:

  • Oncology
  • Artificial Intelligence in Medicine
  • Medical Imaging

Background:

  • Prostate cancer (PCa) management often involves hormone therapy.
  • Predicting progression to castration-resistant prostate cancer (CRPC) is crucial for patient outcomes.
  • Accurate prediction models are needed to guide treatment strategies.

Purpose of the Study:

  • To identify if prostate cancer (PCa) patients progress to castration-resistant prostate cancer (CRPC) after 12 months of hormone therapy.
  • To develop and validate an artificial intelligence (AI) model for predicting PCa progression to CRPC.

Main Methods:

  • A retrospective study included 96 PCa patients with baseline data and multiparametric MRI.
  • A dense multimodal fusion artificial intelligence (Dense-MFAI) model was developed using DenseNet and XGBoost.
  • Model performance was evaluated using accuracy, AUC, sensitivity, and specificity.

Main Results:

  • The Dense-MFAI model achieved 94.2% accuracy and an AUC of 0.945 in predicting PCa progression to CRPC.
  • Combining radiomics features with clinical data significantly enhanced the model's predictive performance.
  • Multimodal data integration proved vital for accurate classification.

Conclusions:

  • The proposed Dense-MFAI model accurately predicts PCa progression to CRPC.
  • This AI tool can assist urologists in optimizing treatment plans and prognostic assessments.
  • The model highlights the value of multimodal data in predicting cancer progression.