Abstract
Background: Novel radiomic features are enabling the extraction of biological data from routine sequences of MRI images. This study’s purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients. Methods: A retrospective dataset comprised of the diagnostic imaging data of 99 PCa patients was used, extracted from The Cancer Imaging Archive’s (TCIA) T2-Weighted (T2-WI) and apparent diffusion coefficient (ADC) images. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Kruskal-Wallis test and Spearman’s rank correlation identified features related to the GS. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS. Results: Five JIM-derived features: contrast, homogeneity, difference variance, dissimilarity, and inverse difference were independent predictors of GS (p < 0.05). Combined JIM and GLCM analysis provided the best performing area-under-the-curve, with values of 78.40% for GS ≤ 6, 82.35% for GS = 3 + 4, and 64.76% for GS ≥ 4 + 3. Conclusion: This retrospective study produced a novel predictive model for GS by the incorporation of JIM data from standard diagnostic MRI images.
Highlights
In both Canada and the United States, prostate cancer (PCa) is the most common non-dermatological cancer in men [1,2]
We considered a dataset of 99 PCa patients collected from the SPIE-AAPM-NCI and the Cancer
We found several grey level co-occurrence matrix (GLCM) and joint intensity matrix (JIM) features with predictive value
Summary
In both Canada and the United States, prostate cancer (PCa) is the most common non-dermatological cancer in men [1,2]. Even with low morbidity rates [4], the questionable net benefit from screening has resulted in guidelines that emphasized the need to increase our pre-test probability with non-invasive approaches prior to prostate biopsy. Patients with PCa are classified into either a low, intermediate, or high risk group based on their prostate-specific Antigen (PSA) level, pathological assessment/Gleason Score (GS) [5,6], and clinical stage (i.e., T stage) [7]. This study’s purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
