Abstract #2871

Matrix analysis of Hybrid Multidimensional MRI for the diagnosis of prostate cancer

Aritrick Chatterjee¹, Xiaobing Fan¹, Shiyang Wang¹, Tatjana Antic², Scott Eggener³, Aytekin Oto¹, and Gregory S Karczmar¹

¹Department of Radiology, University of Chicago, Chicago, IL, United States, ²Department of Pathology, University of Chicago, Chicago, IL, United States, ³Department of Urology, University of Chicago, Chicago, IL, United States

This study investigates the feasibility of diagnosing prostate cancer through matrix analysis of Hybrid Multidimensional MRI (HM-MRI) data. Data was acquired with all combinations of TE (47,75,100ms) and b-values (0,750,1500s/mm²), resulting in a 3×3 matrix associated with each voxel. Matrix analysis parameters: trace, eigenvalues and eigenvectors were calculated for benign tissue and prostate cancer. Prostate cancer showed significantly increased trace, eigenvalue 1, eigenvector components v₁₂ and v₁₃ and reduced v₁₁ compared to normal tissue. PCa diagnosis is feasible using matrix analysis of HM-MRI data with parameters showing good differentiation between PCa and benign prostatic tissue (AUC 0.80-0.96 on ROC analysis).

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