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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite

Hao Li1, Han Liu1, Heinrich von Busch1

  • 1From Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (H. Li, H. Liu, D.C., A.K., B.L.); Diagnostic Imaging, Siemens Healthineers, Erlangen, Bavaria, Germany (H.v.B., R.G.); Vanderbilt University, Nashville, Tenn (H. Li, H. Liu, I.O.); Radboud University Medical Center, Nijmegen, the Netherlands (H.H.); New York University, New York, NY (A.T.); Universitätsspital Basel, Basel, Switzerland (D.W.); Charité, Universitätsmedizin Berlin, Berlin, Germany (T.P.); Patero Clinic, Moscow, Russia (I.S.); Eunpyeong St. Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea (M.H.C.); Department of Radiology, Changhai Hospital of Shanghai, Shanghai, China (Q.Y.); Diagnostikum Graz Süd-West, Graz, Austria (D.S.); Department of Radiology, Loyola University Medical Center, Maywood, Ill (S.S.); Department of Diagnostic Radiology, Oregon Health and Science University School of Medicine, Portland, Ore (F.C.); and Massachusetts General Hospital, Boston, Mass (M.H.).

Radiology. Artificial Intelligence
|August 21, 2024
PubMed
Summary
This summary is machine-generated.

Unsupervised domain adaptation using generated images significantly improves prostate cancer detection accuracy in multisite MRI scans. This method enhances supervised learning models, especially with non-standard imaging protocols.

Keywords:
Diffusion-weighted ImagingMultisiteProstate Cancer DetectionUnsupervised Domain Adaptationb Value

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Prostate cancer (PCa) detection using biparametric MRI (bpMRI) faces challenges due to variations in imaging protocols across different centers.
  • Supervised learning (SL) models for PCa detection can be limited by domain shift in multisite datasets.
  • Standardizing image acquisition parameters, particularly b values in diffusion-weighted imaging (DWI), is crucial for reliable PCa detection.

Purpose of the Study:

  • To evaluate the efficacy of an unsupervised domain adaptation (UDA) method employing generated images to enhance SL model performance for PCa detection.
  • To assess the impact of UDA on multisite bpMRI datasets with varying DWI acquisition parameters.
  • To determine if UDA can mitigate performance degradation caused by deviations from PI-RADS-recommended imaging protocols.

Main Methods:

  • A novel UDA method was developed using a unified generative model to translate DWI (ADC and DW images) across different b values.
  • Generated images were used to replace original images, aligning them with PI-RADS-recommended b values for PCa detection.
  • The study utilized a large retrospective dataset from 5150 patients across nine imaging centers, with an independent test set of 1692 cases for evaluation.

Main Results:

  • The UDA method significantly improved the area under the receiver operating characteristic curve (AUC) for PCa lesion detection compared to baseline SL.
  • Overall AUC increased from 0.73 to 0.79 for PI-RADS 3+ lesions and 0.77 to 0.80 for PI-RADS 4+ lesions.
  • In challenging cases with significant protocol deviations, UDA boosted AUC from 0.49 to 0.76 (PI-RADS 3+) and 0.50 to 0.77 (PI-RADS 4+).

Conclusions:

  • Unsupervised domain adaptation with generated images effectively enhances SL model performance for PCa detection in multisite bpMRI.
  • The UDA approach demonstrates particular benefit in improving detection accuracy for datasets acquired with non-standard DWI protocols.
  • This method offers a promising strategy to improve the generalizability and robustness of AI-based PCa detection systems across diverse clinical settings.