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Proceedings Paper

Prediction of prostate cancer aggressiveness using quantitative radiomic features using multi-parametric MRI
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Paper Abstract

The use of quantitative radiomic features of MRI to predict the aggressiveness of prostate cancer has attracted increasing amounts of attention due to its potential as a non-invasive biomarker for prostate cancer. Although clinical studies have shown that apparent diffusion coefficient (ADC) values correlate with the aggressiveness of prostate cancer, most studies on radiomic features have been performed only with T2-weighted MR (T2wMR). Therefore, we investigate the usefulness of radiomic features of T2wMR and ADC to predict prostate cancer aggressiveness. To define the prostate cancer region of T2wMR based on ground truth pathology, a radiologist manually segmented prostate cancer referring to a fusion result of registration of histopathology image and T2wMR. The prostate cancer region of the ADC is then defined as the same region as the T2wMR through registration of the ADC on the T2wMR. To extract radiomic features to predict prostate cancer aggressiveness, total 68 features are calculated for each region of T2wMR and ADC. To predict the aggressiveness of prostate cancer, a random forest classifier is trained for each region in T2wMR and ADC. The prostate cancer regions were categorized as G1 (GS <= 3+4) and G2 (GS <= 4+3). As results, the prediction model of ADC was provided high performance than that of T2wMR, and the area under the curves of the receiver operating characteristic (ROC) were 0.70 and 0.74 in T2wMR and ADC. Experiment results showed that the possibility of determining the aggressiveness of prostate cancer through the quantitative radiomic features of T2wMR and ADC.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143I (16 March 2020); doi: 10.1117/12.2551298
Show Author Affiliations
Julip Jung, Seoul Women's Univ. (Korea, Republic of)
Helen Hong, Seoul Women's Univ. (Korea, Republic of)
Young-Gi Kim, Seoul National Univ. Bundang Hospital (Korea, Republic of)
Sung Il Hwang, Seoul National Univ. Bundang Hospital (Korea, Republic of)
Hak Jong Lee, Seoul National Univ. Bundang Hospital (Korea, Republic of)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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