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Toward Precision Medicine: Development and Validation of A Machine Learning Based Decision Support System for Optimal Sequencing in Castration-Resistant Prostate Cancer

Authors
 Hakyung Lim  ;  Jeong Woo Yoo  ;  Kwang Suk Lee  ;  Young Hwa Lee  ;  Sangyeop Baek  ;  Sujin Lee  ;  Hoyong Kang  ;  Young Deuk Choi  ;  Won Sik Ham  ;  Seung Hwan Lee  ;  Byung Ha Chung  ;  Abdulghafour Halawani  ;  Jae-Hyeon Ahn  ;  Kyo Chul Koo 
Citation
 CLINICAL GENITOURINARY CANCER, Vol.21(4) : e211-e218.e4, 2023-08 
Journal Title
CLINICAL GENITOURINARY CANCER
ISSN
 1558-7673 
Issue Date
2023-08
MeSH
Artificial Intelligence ; Humans ; Machine Learning ; Male ; Nitriles ; Precision Medicine ; Prostatic Neoplasms, Castration-Resistant* / drug therapy ; Prostatic Neoplasms, Castration-Resistant* / genetics ; Retrospective Studies ; Treatment Outcome
Keywords
Artificial intelligence ; Decision support techniques ; Machine learning ; Prostatic neoplasms
Abstract
Introduction: Selecting a patient-specific sequencing strategy to maximize survival outcomes is a clinically unmet need for patients with castration-resistant prostate cancer (CRPC). We developed and validated an artificial intelligence-based decision support system (DSS) to guide optimal sequencing strategy selection.

Patients and methods: Clinicopathological data of 46 covariates were retrospectively collected from 801 patients diagnosed with CRPC at 2 high-volume institutions between February 2004 and March 2021. Cox-proportional hazards regression survival (Cox) modeling in extreme gradient boosting (XGB) was used to perform survival analysis for cancer-specific mortality (CSM) and overall mortality (OM) according to the use of abiraterone acetate, cabazitaxel, docetaxel, and enzalutamide. The models were further stratified into first-, second-, and third-line models that each provided CSM and OM estimates for each line of treatment. The performances of the XGB models were compared with those of the Cox models and random survival forest (RSF) models in terms of Harrell's C-index.

Results: The XGB models showed greater predictive performance for CSM and OM compared to the RSF and Cox models. C-indices of 0.827, 0.807, and 0.748 were achieved for CSM in the first-, second-, and third-lines of treatment, respectively, while C-indices of 0.822, 0.813, and 0.729 were achieved for OM regarding each line of treatment, respectively. An online DSS was developed to provide visualization of individualized survival outcomes according to each line of sequencing strategy.

Conclusion: Our DSS can be used in clinical practice by physicians and patients as a visualized tool to guide the sequencing strategy of CRPC agents.
Full Text
https://www.sciencedirect.com/science/article/pii/S1558767323000812
DOI
10.1016/j.clgc.2023.03.012
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Urology (비뇨의학교실) > 1. Journal Papers
Yonsei Authors
Koo, Kyo Chul(구교철) ORCID logo https://orcid.org/0000-0001-7303-6256
Yoo, Jeong Woo(유정우)
Lee, Kwang Suk(이광석) ORCID logo https://orcid.org/0000-0002-7961-8393
Lee, Seung Hwan(이승환) ORCID logo https://orcid.org/0000-0001-7358-8544
Chung, Byung Ha(정병하) ORCID logo https://orcid.org/0000-0001-9817-3660
Choi, Young Deuk(최영득) ORCID logo https://orcid.org/0000-0002-8545-5797
Ham, Won Sik(함원식) ORCID logo https://orcid.org/0000-0003-2246-8838
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/197285
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