The purpose of the study was to evaluate the performance of radiomics analysis of MR images for the detection of prostate cancer. The radiomics analysis was conducted using axial T2-weighted images from 49 prostate cancers. The study employs a sophisticated hybrid descriptive-inferential method for the meticulous selection and reduction of features, followed by discriminant analysis to construct a robust predictive model. Among 71 radiomics features, original_glrlm_ShortRunLowGrayLevelEmphasis demonstrated exemplary performance in differentiating between the whole prostate gland and prostate cancer. It had an AUROC of 68.46 (95% CI 0.544 – 0.824; p = 0.022), sensitivity of 76.25%, specificity of 73.15%, and accuracy of 71.02%. Radiomic analysis of T2 weighted MR images was demonstrated to have clinical application in prostate cancer detection, paving the way for improved diagnostic procedures and tailor-made treatment plans for prostate cancer patients.
Ali M., Benfante V., Cutaia G., Salvaggio L., Rubino S., Portoghese M., et al. (2024). Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 83-92). GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-51026-7_8].
Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI
Ali M.;Benfante V.;Cutaia G.;Salvaggio L.;Rubino S.;Portoghese M.;Ferraro M.;Corso R.;Ingrassia T.;Tulone G.;Pavan N.;Di Raimondo D.;Tuttolomondo A.;Simonato A.;
2024-01-01
Abstract
The purpose of the study was to evaluate the performance of radiomics analysis of MR images for the detection of prostate cancer. The radiomics analysis was conducted using axial T2-weighted images from 49 prostate cancers. The study employs a sophisticated hybrid descriptive-inferential method for the meticulous selection and reduction of features, followed by discriminant analysis to construct a robust predictive model. Among 71 radiomics features, original_glrlm_ShortRunLowGrayLevelEmphasis demonstrated exemplary performance in differentiating between the whole prostate gland and prostate cancer. It had an AUROC of 68.46 (95% CI 0.544 – 0.824; p = 0.022), sensitivity of 76.25%, specificity of 73.15%, and accuracy of 71.02%. Radiomic analysis of T2 weighted MR images was demonstrated to have clinical application in prostate cancer detection, paving the way for improved diagnostic procedures and tailor-made treatment plans for prostate cancer patients.
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