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

Nikita Sushentsev; Luis Abrego; Anna Colarieti; Nimalan Sanmugalingam; Arnaldo Stanzione; Jeries Paolo Zawaideh; Iztok Caglic; Alexey Zaikin; Oleg Blyuss; Tristan Barrett

doi:10.1016/j.euros.2023.04.002

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

Eur Urol Open Sci. 2023 Apr 29:52:36-39. doi: 10.1016/j.euros.2023.04.002. eCollection 2023 Jun.

Authors

Nikita Sushentsev¹, Luis Abrego², Anna Colarieti^{1

3}, Nimalan Sanmugalingam¹, Arnaldo Stanzione^{1

4}, Jeries Paolo Zawaideh^{1

5}, Iztok Caglic¹, Alexey Zaikin^{2

6}, Oleg Blyuss^{7

8}, Tristan Barrett¹

Affiliations

¹ Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
² Department of Women's Cancer, Institute for Women's Health, University College London, London, UK.
³ Unit of Radiology, IRCCS Policlinico San Donato, Milan, Italy.
⁴ Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
⁵ Department of Radiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
⁶ Department of Mathematics, University College London, London, UK.
⁷ Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
⁸ Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University, Moscow, Russia.

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.