Improving Prostate Cancer Risk Prediction through Partial AUC Optimization
Authors: 
Xinyuan Zhu, Xiaohan Ren, Wentao Shi, Changming Wang, Xuehan Liu, Yuqing Liu, Tao Tao, Fuli FengAuthors Info & Claims
Pages 1170 - 1173
Abstract
Prostate cancer risk prediction (PCRP) is crucial in guiding clinical decision-making and ensuring accurate diagnoses. The area under the receiver operating characteristic curve (AUC) is typically used for the evaluation of PCRP models. However, AUC considers regions with high false positive rates (FPRs), which are not applicable in clinical practice. To address this concern, we propose to use partial AUC (pAUC) as a more clinically meaningful metric which evaluates PCRP models with restricted FPR. Moreover, we propose a new PCRP framework named pAUCP, which optimizes pAUC to train PCRP models and adopts model ensemble to further enhance its usability. We construct clinical datasets obtained from two medical centers over an extended period to evaluate the proposed pAUCP framework. Extensive experiments demonstrate the rationality and superiority of the pAUCP framework, especially the cross-time and cross-center transferability of the obtained PCRP model.
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Published In
May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw,
- Roy Ka-Wei Lee
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Published: 13 May 2024
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- University Synergy Innovation Program of Anhui Province
- the Key Direction of New Medicine Fund of USTC
- the Key Research and Development Project of Anhui Province
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WWW '24
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