A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging. (7th July 2018)

• Record Type: Journal Article
• Authors: Kirlik, Gokhan; Gullapalli, Rao; D'Souza, Warren; Md Daud Iqbal, Gazi; Naslund, Michael; Wong, Jade; Papadimitrou, John; Roys, Steve; Mistry, Nilesh; Zhang, Hao
• Is Part Of: Cancer informatics. Volume 17(2018)
• Publication Date: 2018-07-07
• Subjects: prostate cancer -- diagnostic imaging -- multiparametric MRI/MRSI -- predictive modeling; Bioinformatics -- Periodicals; Biology -- Data processing -- Periodicals; Cancer -- Periodicals; Cancer -- Research -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1177/1176935118786260
• Languages: English
• ISSNs: 1176-9351

Abstract

Prostate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness. A total of 223 specimens from patients who received magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging prior to the surgery were studied. Multiparametric imaging included extracting T2-map, apparent diffusion coefficient (ADC) using diffusion-weighted MRI, K t r a n s using dynamic contrast-enhanced MRI, and 3-dimensional-MR spectroscopy. A pathologist reviewed all 223 specimens and marked cancerous regions on each and graded them with Gleason scores, which served as the ground truth to validate our prediction model. In cancer aggressiveness prediction, the average area under the receiver operating characteristic curve (AUC) value was 0.73 with 95% confidence interval (0.72-0.74) and the average sensitivity and specificity were 0.72 (0.71-0.73) and 0.73 (0.71-0.75), respectively. For the cancer detection model, the average AUC value was 0.68 (0.66-0.70) and the average sensitivity and specificity were 0.73 (0.70-0.77) and 0.62 (0.60-0.68), respectively. Our method included capability to handle class imbalance using adaptive boosting with random undersampling. In addition, our method was noninvasive and allowed for nonsubjective disease characterization, which provided physician information to make personalized treatment decision.