Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer
DC Field | Value | Language |
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dc.contributor.author | Lee, Changhee | - |
dc.contributor.author | Light, Alexander | - |
dc.contributor.author | Saveliev, Evgeny S. | - |
dc.contributor.author | Van der Schaar, Mihaela | - |
dc.contributor.author | Gnanapragasam, Vincent J. | - |
dc.date.accessioned | 2023-03-08T06:03:49Z | - |
dc.date.available | 2023-03-08T06:03:49Z | - |
dc.date.issued | 2022-08 | - |
dc.identifier.issn | 2398-6352 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/61248 | - |
dc.description.abstract | Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate "live" updates of progression risk during follow-up has hitherto been lacking. To address this, we developed a deep learning-based individualised longitudinal survival model using Dynamic-DeepHit-Lite (DDHL) that learns data-driven distribution of time-to-event outcomes. Further refining outputs, we used a reinforcement learning approach (Actor-Critic) for temporal predictive clustering (AC-TPC) to discover groups with similar time-to-event outcomes to support clinical utility. We applied these methods to data from 585 men on AS with longitudinal and comprehensive follow-up (median 4.4 years). Time-dependent C-indices and Brier scores were calculated and compared to Cox regression and landmarking methods. Both Cox and DDHL models including only baseline variables showed comparable C-indices but the DDHL model performance improved with additional follow-up data. With 3 years of data collection and 3 years follow-up the DDHL model had a C-index of 0.79 (+/- 0.11) compared to 0.70 (+/- 0.15) for landmarking Cox and 0.67 (+/- 0.09) for baseline Cox only. Model calibration was good across all models tested. The AC-TPC method further discovered 4 distinct outcome-related temporal clusters with distinct progression trajectories. Those in the lowest risk cluster had negligible progression risk while those in the highest cluster had a 50% risk of progression by 5 years. In summary, we report a novel machine learning approach to inform personalised follow-up during active surveillance which improves predictive power with increasing data input over time. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer | - |
dc.type | Article | - |
dc.identifier.doi | 10.1038/s41746-022-00659-w | - |
dc.identifier.bibliographicCitation | NPJ DIGITAL MEDICINE, v.5, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000836791500001 | - |
dc.identifier.scopusid | 2-s2.0-85135475318 | - |
dc.citation.number | 1 | - |
dc.citation.title | NPJ DIGITAL MEDICINE | - |
dc.citation.volume | 5 | - |
dc.type.docType | Article | - |
dc.publisher.location | 독일 | - |
dc.relation.journalResearchArea | Health Care Sciences & Services | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Health Care Sciences & Services | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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