Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance

• Published in: European radiology 2023-06, Vol.33 (6), p.3792-3800
• Main Authors: Sushentsev, Nikita, Rundo, Leonardo, Abrego, Luis, Li, Zonglun, Nazarenko, Tatiana, Warren, Anne Y., Gnanapragasam, Vincent J., Sala, Evis, Zaikin, Alexey, Barrett, Tristan, Blyuss, Oleg
• Format: Article
• Language: English
• Subjects: Decision analysis; Decision making; Density; Diagnostic Radiology; Humans; Image analysis; Image processing; Imaging; Internal Medicine; Interventional Radiology; Long short-term memory; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medicine; Medicine & Public Health; Neural networks; Neuroradiology; Patients; Performance prediction; Prediction models; Prostate - pathology; Prostate cancer; Prostate-Specific Antigen; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - pathology; Radiology; Radiomics; Recurrent neural networks; Retrospective Studies; Surveillance; Time Factors; Time series; Tumors; Ultrasound; Urogenital; Watchful Waiting
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-09438-x

Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78–0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64–0.87]; p  = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76–0.93]; p  = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. Key Points • LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. • Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.