Using a Recurrent Neural Network To Inform the Use of Prostate-specific Antigen (PSA) and PSA Density for Dynamic Monitoring of the Risk of Prostate Cancer Progression on Active Surveillance

Abstract

The global uptake of prostate cancer (PCa) active surveillance (AS) is steadily increasing. While prostate-specific antigen density (PSAD) is an important baseline predictor of PCa progression on AS, there is a scarcity of recommendations on its use in follow-up. In particular, the best way of measuring PSAD is unclear. One approach would be to use the baseline gland volume (BGV) as a denominator in all calculations throughout AS (nonadaptive PSAD, PSAD_NA), while another would be to remeasure gland volume at each new magnetic resonance imaging scan (adaptive PSAD, PSAD_A). In addition, little is known about the predictive value of serial PSAD in comparison to PSA. We applied a long short-term memory recurrent neural network to an AS cohort of 332 patients and found that serial PSAD_NA significantly outperformed both PSAD_A and PSA for follow-up prediction of PCa progression because of its high sensitivity. Importantly, while PSAD_NA was superior in patients with smaller glands (BGV ≤55 ml), serial PSA was better in men with larger prostates of >55 ml.

Patient summary: Repeat measurements of prostate-specific antigen (PSA) and PSA density (PSAD) are the mainstay of active surveillance in prostate cancer. Our study suggests that in patients with a prostate gland of 55 ml or smaller, PSAD measurements are a better predictor of tumour progression, whereas men with a larger gland may benefit more from PSA monitoring.

Keywords: Active surveillance; Artificial intelligence; Longitudinal data; Predictive modelling; Prostate cancer; Prostate-specific antigen; Recurrent neural networks.

Fig. 1

Use of serial PSA and PSAD for predicting prostate cancer progression on active surveillance. (A) LOWESS curves demonstrating serial changes in prostate GV, PSA, PSAD_A, and PSAD_NA for patients with and without progression. (B) ROC curves for serial PSA, PSAD_A, and PSAD_NA applied to the whole cohort to assess the ability to predict prostate cancer progression in patients on active surveillance. (C) ROC curves for serial PSA, PSAD_A, and PSAD_NA for patients with differing BGV. (D) LOWESS curves demonstrating changes in serial PSA and PSAD_NA for patients with smaller (≤55 ml) and larger (>55 ml) BGV. (E) LOWESS curves demonstrating the difference between PSAD_A and PSAD_NA by BGV. (F) Serial changes in median PSA and PSAD_NA for patients with smaller (≤55 ml) and larger (>55 ml) BGV. AUC = area under the ROC curve; BGV = baseline GV; GV = gland volume; LOWESS = locally weighted scatterplot smoothing; PSA = prostate-specific antigen; PSAD = PSA density (in ng/ml/ml); PSAD_A = adaptive PSAD; PSAD_NA = nonadaptive PSAD; ROC = receiver operating characteristic.