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Journal Article Explainable ML models for a deeper insight on treatment decision for localized prostate cancer
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Authors
Jang Hee Han, Sungyup Lee, Byounghwa Lee, Ock‑kee Baek, Samuel L. Washington III, Annika Herlemann, Peter E. Lonergan, Peter R. Carroll, Chang Wook Jeong, Matthew R. Cooperberg
Issue Date
2023-07
Citation
Scientific Reports, v.13, pp.1-8
ISSN
2045-2322
Publisher
Nature Publishing Group
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1038/s41598-023-38162-1
Abstract
Although there are several decision aids for the treatment of localized prostate cancer (PCa), there are limitations in the consistency and certainty of the information provided. We aimed to better understand the treatment decision process and develop a decision-predicting model considering oncologic, demographic, socioeconomic, and geographic factors. Men newly diagnosed with localized PCa between 2010 and 2015 from the Surveillance, Epidemiology, and End Results Prostate with Watchful Waiting database were included (n = 255,837). We designed two prediction models: (1) Active surveillance/watchful waiting (AS/WW), radical prostatectomy (RP), and radiation therapy (RT) decision prediction in the entire cohort. (2) Prediction of AS/WW decisions in the low-risk cohort. The discrimination of the model was evaluated using the multiclass area under the curve (AUC). A plausible Shapley additive explanations value was used to explain the model’s prediction results. Oncological variables affected the RP decisions most, whereas RT was highly affected by geographic factors. The dependence plot depicted the feature interactions in reaching a treatment decision. The decision predicting model achieved an overall multiclass AUC of 0.77, whereas 0.74 was confirmed for the low-risk model. Using a large population-based real-world database, we unraveled the complex decision-making process and visualized nonlinear feature interactions in localized PCa.
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CC BY