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Deep Learning Classification of Prostate Cancer on Confidently Labeled Micro-Ultrasound Images.

Jake Pensa, Wayne Brisbane, Adam Kinnaird

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    High-resolution micro-ultrasound shows promise for prostate cancer detection. AI models trained with a novel co-registration method achieved performance comparable to expert reviewers, outperforming novices.

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    Area of Science:

    • Medical Imaging
    • Oncology
    • Artificial Intelligence

    Background:

    • Micro-ultrasound offers an affordable alternative to MRI for prostate cancer diagnosis.
    • Challenges exist in correlating micro-ultrasound images with MRI and pathology due to tissue deformation and non-standard image orientations.
    • A validated methodology for co-registering micro-ultrasound, MRI, and whole-mount pathology has been previously established.

    Purpose of the Study:

    • To utilize a validated co-registration methodology to train preliminary prostate cancer classifiers using reconstructed micro-ultrasound images.
    • To compare the performance of these AI classifiers against an expert micro-ultrasound reviewer.

    Main Methods:

    • Developed and validated a co-registration methodology for micro-ultrasound, MRI, and whole-mount pathology.
    • Used this methodology to confidently label reconstructed micro-ultrasound images.
    • Trained preliminary cancer classifiers on these labeled images.
    • Compared classifier performance against an expert reviewer using a dataset of 15 patients.

    Main Results:

    • The trained AI models demonstrated superior performance compared to a novice reviewer.
    • The models achieved performance metrics (sensitivity: 78.9%, specificity: 72.7%) similar to an expert reviewer (sensitivity: 60.6%, specificity: 80.5%) on a limited dataset.
    • The study highlights the potential of AI in interpreting micro-ultrasound for prostate cancer.

    Conclusions:

    • The developed AI classifiers show encouraging results for prostate cancer identification using micro-ultrasound.
    • Further investigation with larger datasets and advanced models is warranted to improve diagnostic accuracy.
    • This approach could enhance the utility of micro-ultrasound as a cost-effective tool in prostate cancer diagnostics.