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Multi-Multimodality Integrated Stack-Ensemble Learning for the Prediction of Gleason Grade and Prognostic Outcome in Prostate Cancer: A Proof-of-Concept Study

2021-05-17

Abstract excerpt

<h4>Purpose: </h4> To develop a generalizable model, namely PRISK, for the prediction of Gleason grade and prognostic outcome in prostate cancer (PCa) with multiple clinical factors and multiparametric (mp) MRI using stack-ensemble learning. Methods PRISK is developed to primarily assess PCa Gleason grade between benign (pG0), 3 + 3 (pG1), 3 + 4 (pG2), 4 + 3 (pG3) and ≥ 4 + 4 (pG4) and secondly predict the bioche...

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Literature Corpus work
c9bbbaf0-5742-50b7-8ab2-633bc1702dd6
DOI
10.21203/rs.3.rs-512084/v1
Open publication

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Multi-Multimodality Integrated Stack-Ensemble Learning for the Prediction of Gleason Grade and Prognostic Outcome in Prostate Cancer: A Proof-of-Concept StudyDOI 10.21203/rs.3.rs-512084/v1
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