Abstract
Background: Approximately 20–50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Among them, cancer recurrence occurs in about 20–30%. Thus, we aim to reveal the utility of machine learning algorithms for the prediction of early BCR after RP. Methods: A total of 104 prostate cancer patients who underwent magnetic resonance imaging and RP were evaluated. Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer perceptron (MLP), decision tree (DT), and auto-encoder) were applied to build a prediction model for early BCR using preoperative clinical and imaging and postoperative pathologic data. The sensitivity, specificity, and accuracy for detection of early BCR of each algorithm were evaluated. Area under the receiver operating characteristics (AUROC) analyses were conducted. Results: A prediction model using an auto-encoder showed the highest prediction ability of early BCR after RP using all data as input (AUC = 0.638) and only preoperative clinical and imaging data (AUC = 0.656), followed by MLP (AUC = 0.607 and 0.598), KNN (AUC = 0.596 and 0.571), and DT (AUC = 0.534 and 0.495). Conclusion: The auto-encoder-based prediction system has the potential for accurate detection of early BCR and could be useful for long-term follow-up planning in prostate cancer patients after RP.
Highlights
After radical prostatectomy for localized prostate cancer, the rate of biochemical recurrence (BCR)approaches approximately 20–50% [1]
BCR after radical prostatectomy (RP) using all data as input (AUC = 0.638) and only preoperative clinical and imaging data (AUC = 0.656), followed by multi-layer perceptron (MLP) (AUC = 0.607 and 0.598), k-nearest neighbors (KNN) (AUC = 0.596 and 0.571), and decision tree (DT)
The auto-encoder-based prediction system has the potential for accurate detection of early BCR and could be useful for long-term follow-up planning in prostate cancer patients after RP
Summary
After radical prostatectomy for localized prostate cancer, the rate of biochemical recurrence (BCR)approaches approximately 20–50% [1]. After radical prostatectomy for localized prostate cancer, the rate of biochemical recurrence (BCR). Several studies were conducted for developing risk prediction scoring systems with more accurate risk stratification of prostate cancer. Tumor characteristics such as grade, extent, and prostate-specific antigen (PSA) level have revealed independent factors for prediction of an oncologic outcome. 20–50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer perceptron (MLP), decision tree (DT), and auto-encoder) were applied to build a prediction model for early BCR using preoperative clinical and imaging and postoperative pathologic data. Area under the receiver operating characteristics (AUROC) analyses were conducted
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