Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI

• Published in: European radiology Vol. 25; no. 11; pp. 3187 - 3199
• Main Authors: Litjens, Geert J. S., Barentsz, Jelle O., Karssemeijer, Nico, Huisman, Henkjan J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2015; Springer Nature B.V
• Subjects: Accuracy; Aged; Aged, 80 and over; Area Under Curve; Biopsy; Biopsy, Needle - methods; Cohort Studies; Computer Applications; Contrast Media; Diagnosis, Computer-Assisted - statistics & numerical data; Diagnostic Radiology; Diffusion Magnetic Resonance Imaging - statistics & numerical data; Humans; Imaging; Internal Medicine; Interventional Radiology; Magnetic resonance imaging; Magnetic Resonance Imaging - statistics & numerical data; Magnetic Resonance Imaging, Interventional - methods; Male; Medicine; Medicine & Public Health; Middle Aged; Neoplasm Grading; Neuroradiology; Patients; Predictive Value of Tests; Prospective Studies; Prostate cancer; Prostatic Neoplasms - classification; Prostatic Neoplasms - pathology; Radiology; ROC Curve; Sensitivity and Specificity; Treatment Outcome; Ultrasound; Diagnostic performance; Observer study; Magnetic resonance imaging; Prostate cancer; Computer-aided diagnosis
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-015-3743-y

Objectives To investigate the added value of computer-aided diagnosis (CAD) on the diagnostic accuracy of PIRADS reporting and the assessment of cancer aggressiveness. Methods Multi-parametric MRI and histopathological outcome of MR-guided biopsies of a consecutive set of 130 patients were included. All cases were prospectively PIRADS reported and the reported lesions underwent CAD analysis. Logistic regression combined the CAD prediction and radiologist PIRADS score into a combination score. Receiver-operating characteristic (ROC) analysis and Spearman’s correlation coefficient were used to assess the diagnostic accuracy and correlation to cancer grade. Evaluation was performed for discriminating benign lesions from cancer and for discriminating indolent from aggressive lesions. Results In total 141 lesions (107 patients) were included for final analysis. The area-under-the-ROC-curve of the combination score was higher than for the PIRADS score of the radiologist (benign vs. cancer, 0.88 vs. 0.81, p = 0.013 and indolent vs. aggressive, 0.88 vs. 0.78, p < 0.01). The combination score correlated significantly stronger with cancer grade (0.69, p = 0.0014) than the individual CAD system or radiologist (0.54 and 0.58). Conclusions Combining CAD prediction and PIRADS into a combination score has the potential to improve diagnostic accuracy. Furthermore, such a combination score has a strong correlation with cancer grade. Key Points • Computer-aided diagnosis helps radiologists discriminate benign findings from cancer in prostate MRI. • Combining PIRADS and computer-aided diagnosis improves differentiation between indolent and aggressive cancer. • Adding computer-aided diagnosis to PIRADS increases the correlation coefficient with respect to cancer grade.