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Cancers 2018, 10(8), 249; https://doi.org/10.3390/cancers10080249

Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer

1
Division of Radiation Oncology, Department of Oncology, McGill University, Montreal, QC H3S 1Y9, Canada
2
The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
*
Author to whom correspondence should be addressed.
Received: 6 June 2018 / Revised: 24 July 2018 / Accepted: 26 July 2018 / Published: 28 July 2018
(This article belongs to the Special Issue Cancer Biomarkers)
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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. View Full-Text
Keywords: biomarkers; Gleason score; radiomics; prostate cancer biomarkers; Gleason score; radiomics; prostate cancer
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Chaddad, A.; Kucharczyk, M.J.; Niazi, T. Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer. Cancers 2018, 10, 249.

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