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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Bridging the gap between prostate radiology and pathology through machine learning.

Indrani Bhattacharya1,2, David S Lim3, Han Lin Aung4

  • 1Department of Radiology, Stanford University School of Medicine, Stanford, California, USA.

Medical Physics
|May 28, 2022
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Summary
This summary is machine-generated.

Digital pathologist labels significantly improve machine learning models for prostate cancer detection on MRI. These labels enhance accuracy and reduce variability compared to radiologist annotations, aiding in more reliable cancer diagnosis.

Keywords:
aggressive versus indolent cancercancer labelsdeep learningdigital pathologyprostate MRI

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate cancer is a leading cause of cancer death in men.
  • Magnetic resonance imaging (MRI) aids in prostate cancer detection but faces limitations like false positives/negatives.
  • Standardizing interpretations of prostate MRI is crucial for accurate diagnosis.

Purpose of the Study:

  • To compare different ground truth labeling strategies for training machine learning models.
  • To evaluate the impact of labeling strategies on model performance for prostate cancer detection on MRI.

Main Methods:

  • Four deep learning models were trained using pathology-confirmed radiologist labels, pathologist labels, and digital pathologist labels (lesion-level and pixel-level).
  • Labels were mapped to pre-operative MRI via an automated registration platform.
  • Models were evaluated on radical prostatectomy and targeted biopsy cohorts.

Main Results:

  • Radiologist labels resulted in missed cancers and lower overlap compared to pathology labels.
  • Machine learning models trained with digital pathologist labels demonstrated superior performance.
  • Pixel-level digital pathologist labels enabled differentiation of aggressive and indolent cancer components on MRI.

Conclusions:

  • Digital pathologist labels enhance machine learning model performance for prostate MRI interpretation.
  • This approach reduces challenges of human annotation, including variability and labor.
  • Digital pathologist labels bridge radiology and pathology for improved prostate cancer detection.