Prostate Region-Wise Imaging Biomarker Profiles for Risk Stratification and Biochemical Recurrence Prediction

• Published in: Cancers Vol. 15; no. 16; p. 4163
• Main Authors: Sánchez Iglesias, Ángel, Morillo Macías, Virginia, Picó Peris, Alfonso, Fuster-Matanzo, Almudena, Nogué Infante, Anna, Muelas Soria, Rodrigo, Bellvís Bataller, Fuensanta, Domingo Pomar, Marcos, Casillas Meléndez, Carlos, Yébana Huertas, Raúl, Ferrer Albiach, Carlos
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023; MDPI
• Subjects: Automation; Biomarkers; Cancer therapies; Care and treatment; Classification; Clinical medicine; Diagnosis; Diagnostic imaging; diffusion parameters; imaging biomarkers; Localization; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Medical research; Medicine, Experimental; MRI; Patients; Perfusion; perfusion parameters; Prognosis; Prostate cancer; Radiation therapy; Radiomics; Radiotherapy; Risk groups; Spain
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15164163

Background: Identifying prostate cancer (PCa) patients with a worse prognosis and a higher risk of biochemical recurrence (BCR) is essential to guide treatment choices. Here, we aimed to identify possible imaging biomarker (perfusion/diffusion + radiomic features) profiles extracted from MRIs that were able to discriminate patients according to their risk or the occurrence of BCR 10 years after diagnosis, as well as to evaluate their predictive value with or without clinical data. Methods: Patients with localized PCa receiving neoadjuvant androgen deprivation therapy and radiotherapy were retrospectively evaluated. Imaging features were extracted from MRIs for each prostate region or for the whole gland. Univariate and multivariate analyses were conducted. Results: 128 patients (mean [range] age, 71 [50–83] years) were included. Prostate region-wise imaging biomarker profiles mainly composed of radiomic features allowed discriminating risk groups and patients experiencing BCR. Heterogeneity-related radiomic features were increased in patients with worse prognosis and with BCR. Overall, imaging biomarkers profiles retained good predictive ability (AUC values superior to 0.725 in most cases), which generally improved when clinical data were included (particularly evident for the prediction of the BCR, with AUC values ranging from 0.841 to 0.877 for combined models and sensitivity values above 0.960) and when models were built per prostate region vs. the whole gland. Conclusions: Prostate region-aware imaging profiles enable identification of patients with worse prognosis and with a higher risk of BCR, retaining higher predictive values when combined with clinical variables.