Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy
| Title: | Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy |
|---|---|
| Authors: | Xinyu Cao, Yin Fang, Chunguang Yang, Zhenghao Liu, Guoping Xu, Yan Jiang, Peiyan Wu, Wenbo Song, Hanshuo Xing, Xinglong Wu |
| Source: | International Neurourology Journal, Vol 28, Iss 1, Pp 33-43 (2024) |
| Publisher Information: | Korean Continence Society, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Diseases of the genitourinary system. Urology |
| Subject Terms: | prostatic neoplasms, risk stratification, machine learning, stacking learning, artificial intelligence, Diseases of the genitourinary system. Urology, RC870-923 |
| More Details: | Purpose Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. Methods Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. Results Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. Conclusions This study aimed to improve clinicians’ ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2093-4777 2093-6931 |
| Relation: | http://einj.org/upload/pdf/inj-2346332-166.pdf; https://doaj.org/toc/2093-4777; https://doaj.org/toc/2093-6931 |
| DOI: | 10.5213/inj.2346332.166 |
| Access URL: | https://doaj.org/article/61d87dd01b8f40cba45f5d4af45dcf5a |
| Accession Number: | edsdoj.61d87dd01b8f40cba45f5d4af45dcf5a |
| Database: | Directory of Open Access Journals |
| ISSN: | 20934777 20936931 |
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| DOI: | 10.5213/inj.2346332.166 |
| Published in: | International Neurourology Journal |
| Language: | English |