Evaluation of machine learning strategies for imaging confirmed prostate cancer recurrence prediction on electronic health records. (April 2022)

• Record Type: Journal Article
• Authors: Beinecke, Jacqueline Michelle; Anders, Patrick; Schurrat, Tino; Heider, Dominik; Luster, Markus; Librizzi, Damiano; Hauschild, Anne-Christin
• Is Part Of: Computers in biology and medicine. Volume 143(2022)
• Publication Date: 2022-04
• Subjects: Prostate cancer -- Cancer recurrence -- Ga-68-PSMA PET/CT -- Machine learning -- Multivariate analysis; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105263
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: The main screening parameter to monitor prostate cancer recurrence (PCR) after primary treatment is the serum concentration of prostate-specific antigen (PSA). In recent years, Ga-68-PSMA PET/CT has become an important method for additional diagnostics in patients with biochemical recurrence. Purpose: While Ga-68-PSMA PET/CT performs better, it is an expensive, invasive, and time-consuming examination. Therefore, in this study, we aim to employ modern multivariate Machine Learning (ML) methods on electronic health records (EHR) of prostate cancer patients to improve the prediction of imaging confirmed PCR (IPCR). Methods: We retrospectively analyzed the clinical information of 272 patients, who were examined using Ga-68-PSMA PET/CT. The PSA values ranged from 0 ng/mL to 2270.38 ng/mL with a median PSA level at 1.79 ng/mL. We performed a descriptive analysis using Logistic Regression. Additionally, we evaluated the predictive performance of Logistic Regression, Support Vector Machine, Gradient Boosting, and Random Forest. Finally, we assessed the importance of all features using Ensemble Feature Selection (EFS). Results: The descriptive analysis found significant associations between IPCR and logarithmic PSA values as well as between IPCR and performed hormonal therapy. Our models were able to predict IPCR with an AUC score of 0.78 ± 0.13 (mean ± standard deviation) and a sensitivity of 0.997 ± 0.01. Features such as PSA, PSA doubling time, PSA velocity, hormonal therapy, radiation treatment, and injected activity show high importance for IPCR prediction using EFS. Conclusion: This study demonstrates the potential of employing a multitude of parameters into multivariate ML models to improve identification of non-recurring patients compared to the current focus on the main screening parameter (PSA). We showed that ML models are able to predict IPCR, detectable by Ga-68-PSMA PET/CT, and thereby pave the way for optimized early imaging and treatment. Highlights: Prostate cancer recurrence detection rates quite low for smaller PSA values. Ga-68-PSMA PET/CT imaging important method for additional diagnostics. Significant association between recurrence and PSA values and hormonal therapy. ML models can predict imaging confirmed prostate cancer recurrence. Ensemble Feature Selection identifies clinical variables important for ML prediction.