MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218)

• Published in: European radiology 2021-02, Vol.31 (2), p.716-728
• Main Authors: Gugliandolo, Simone Giovanni, Pepa, Matteo, Isaksson, Lars Johannes, Marvaso, Giulia, Raimondi, Sara, Botta, Francesca, Gandini, Sara, Ciardo, Delia, Volpe, Stefania, Riva, Giulia, Rojas, Damari Patricia, Zerini, Dario, Pricolo, Paola, Alessi, Sarah, Petralia, Giuseppe, Summers, Paul Eugene, Mistretta, Frnacesco Alessandro, Luzzago, Stefano, Cattani, Federica, De Cobelli, Ottavio, Cassano, Enrico, Cremonesi, Marta, Bellomi, Massimo, Orecchia, Roberto, Jereczek-Fossa, Barbara Alicja
• Format: Article
• Language: English
• Subjects: Diagnostic Radiology; Feature extraction; Histograms; Humans; Image classification; Imaging; Immunoglobulins; Internal Medicine; Interventional Radiology; Magnetic Resonance; Magnetic Resonance Imaging; Male; Medical imaging; Medicine; Medicine & Public Health; Multivariate analysis; Neuroradiology; Prospective Studies; Prostate cancer; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - radiotherapy; Radiation therapy; Radiology; Radiomics; Resonance; Retrospective Studies; Risk; Standardization; Texture; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-07105-z

Objectives Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa). Methods This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis. Results Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94. Conclusions MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice. Key Points • A radiomic model was used to classify PCa aggressiveness. • Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. • The most predictive features belong to the texture (57%) and intensity (43%) domains.