Repeatability of Quantitative Imaging Features in Prostate Magnetic Resonance Imaging - Lu, Hong; Parra, Nestor A.; Qi, Jin; Gage, Kenneth; Li, Qian; Fan, Shuxuan; Feuerlein, Sebastian; Pow-Sang, Julio; Gillies, Robert; Choi, Jung W.; Balagurunathan, Yoganand (Frontiers in Oncology, 2020)
Background: Multiparametric magnetic resonance imaging (mpMRI) has emerged as a non-invasive modality to diagnose and monitor prostate cancer. Quantitative metrics on the regions of abnormality have shown to be useful descriptors to discriminate clinically significant cancers. In this study, we evaluate the reproducibility of quantitative imaging features using repeated mpMRI on the same patients. Methods: We retrospectively obtained the de-identified records of patients, who underwent two mpMRI scans within 2 weeks of the first baseline scan. The patient records were obtained as de-identified data (including imaging), obtained through the TCIA (The Cancer Imaging Archive) repository and analyzed in our institution with an IRB-approved HIPAA-compliant retrospective study protocol. Indicated biopsied regions were used as a marker for our study radiologists to delineate the regions of interest. We extracted 307 quantitative features in each mpMRI modality (T2w and ADC with b values of 0 and 1400 mm/s2) across the two sequential scans. Concordance correlation coefficients (CCC) were computed on the features extracted from sequential scans. Redundant features were removed by computing the coefficient of determination (R2 ) among them and replaced with a feature that had the highest dynamic range within inter-correlated groups. Results: We have assessed the reproducibility of quantitative imaging features among sequential scans and found that habitat region characterization improves repeatability in ADC maps. There were 19 T2w features and 2 ADC features in radiologist drawn regions (native raw-image), compared to 18 T2w and 15 ADC features in habitat regions (sphere), that were reproducible (CCC ≥ 0.65) and non-redundant (R2 ≥ 0.99). We also found that z-transformation of the images prior to feature extraction reduced the number of reproducible features with no detrimental effect. Conclusion: We have shown that there are quantitative imaging features that are reproducible across sequential prostate mpMRI acquisition at a preset level of filters. We also find that a habitat approach improves feature repeatability in ADC. A validated set of reproducible image features in mpMRI will allow us to develop clinically useful disease risk stratification; enabling the possibility of using imaging as a surrogate to invasive biopsies.