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Integration of Clinical Identifications With Deep Transferrable Imaging Feature Representations Can Help Predict Prostate Cancer Aggressiveness and Outcome

2021-01-25

Abstract excerpt

<h4>Background: </h4> A pretreatment risk stratification of prostate cancer (PCa) is important for determining appropriate treatment option and prognostic prediction. Although numerous quantifiable approaches can provide fundamental insights in PCa, a clinical tool should leverage the integration of all data representations to enables detailed assessment. We developed a generalizable machine learning platform, des...

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Literature Corpus work
2e0def50-3eed-51ce-9de0-3233ade3e693
DOI
10.21203/rs.3.rs-152466/v1
Open publication

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Integration of Clinical Identifications With Deep Transferrable Imaging Feature Representations Can Help Predict Prostate Cancer Aggressiveness and OutcomeDOI 10.21203/rs.3.rs-152466/v1
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