A Neural Network Model Combining [-2]proPSA, freePSA, Total PSA, Cathepsin D, and Thrombospondin-1 Showed Increased Accuracy in the Identification of Clinically Significant Prostate Cancer

Abstract

Background: The Prostate Health Index (PHI) and Proclarix (PCLX) have been proposed as blood-based tests for prostate cancer (PCa). In this study, we evaluated the feasibility of an artificial neural network (ANN)-based approach to develop a combinatorial model including PHI and PCLX biomarkers to recognize clinically significant PCa (csPCa) at initial diagnosis. Methods: To this aim, we prospectively enrolled 344 men from two different centres. All patients underwent radical prostatectomy (RP). All men had a prostate-specific antigen (PSA) between 2 and 10 ng/mL. We used an artificial neural network to develop models that can identify csPCa efficiently. As inputs, the model uses [-2]proPSA, freePSA, total PSA, cathepsin D, thrombospondin, and age. Results: The output of the model is an estimate of the presence of a low or high Gleason score PCa defined at RP. After training on a dataset of up to 220 samples and optimization of the variables, the model achieved values as high as 78% for sensitivity and 62% for specificity for all-cancer detection compared with those of PHI and PCLX alone. For csPCa detection, the model showed 66% (95% CI 66–68%) for sensitivity and 68% (95% CI 66–68%) for specificity. These values were significantly different compared with those of PHI (p < 0.0001 and 0.0001, respectively) and PCLX (p = 0.0003 and 0.0006, respectively) alone. Conclusions: Our preliminary study suggests that combining PHI and PCLX biomarkers may help to estimate, with higher accuracy, the presence of csPCa at initial diagnosis, allowing a personalized treatment approach. Further studies training the model on larger datasets are strongly encouraged to support the efficiency of this approach.

BioPrev-C – development and validation of a contemporary prostate cancer risk calculator

“…Recent research has also focused on adding additional biomarkers for the prediction of high-grade PCa. This includes for example the Prostate Health index (PHI) ( 26 ), the Proclarix test ( 27 ), 4KScore ( 28 ) or more recently the Stockholm3 test ( 29 , 30 ). However, none of the markers has made it yet into daily clinical practice so far for different reasons (costs, practicability, conflicting results).…”

Identification and validation of an individualized metabolic prognostic signature for predicting the biochemical recurrence of prostate cancer based on the immune microenvironment

“…In addition to the methods used above, an artificial neural network (ANN) model is also another useful approach to identify and validate PCa biomarkers, which facilitate personalized management of PCa [ 50 ]. For example, the ANN model based on clinical tests such as the prostate health index, Proclarix blood test, or multiparametric magnetic resonance had high accuracy in the recognition of progressive PCa at initial diagnosis [ 51 , 52 ]. Therefore, the ANN model combining the three prognostic MTGs and PCa biomarkers in clinical tests may identify new tools with increased accuracy for personalized management of PCa.…”

The therapeutic effect of pelvic floor muscle training on stress urinary incontinence following prostatectomy: a systematic review and meta-analysis

“…Continuous urinary incontinence after prostatectomy is a common postoperative complication, with multiple studies suggesting that invasive treatment for urinary incontinence should be delayed for at least one year ( 17 , 22 ). As such, in certain cases, PFMT will be chosen as an alternative option.…”