Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.

Journal of Magnetic Resonance Imaging : JMRI
Ahmad AlgoharyAnant Madabhushi

Abstract

Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS). To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients. Retrospective. MRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy. 3T, T2 -weighted (T2 w) and diffusion-weighted (DW) MRI. A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T2 w-MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI-negative-biopsy-negative (Group 1, N = 15), MRI-positive-biopsy-positive (Group 2, N = 16), MRI-negative-biopsy-positive (Group 3, N = 10), and MRI-positive-biopsy-negative (Group 4, N = 15). In all, 308 radiomic features (First-order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T2 w image...Continue Reading

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Citations

Dec 5, 2018·Scientific Data·Andriy FedorovFiona Fennessy
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