The CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses.

Recent advances in radiomics have enhanced the value of medical imaging in various aspects of clinical practice, but a crucial component that remains to be investigated further is the robustness of quantitative features to imaging variations and across multiple institutions. In the case of MRI, signal intensity values vary according to the acquisition parameters used, yet no consensus exists on which preprocessing techniques are favorable in reducing scanner-dependent variability of image-based features. Hence, the purpose of this study was to assess the impact of common image preprocessing methods on the scanner dependence of MRI radiomic features in multi-institutional glioblastoma multiforme (GBM) datasets. Two independent GBM cohorts were analyzed: 50 cases from the TCGA-GBM dataset and 111 cases acquired in our institution, and each case consisted of 3 MRI sequences viz. FLAIR, T1-weighted, and T1-weighted post-contrast. Five image preprocessing techniques were examined: 8-bit global rescaling, 8-bit local rescaling, bias field correction, histogram standardization, and isotropic resampling. A total of 420 features divided into eight categories representing texture, shape, edge, and intensity histogram were extracted. Two distinct imaging parameters were considered: scanner manufacturer and scanner magnetic field strength. Wilcoxon tests identified features robust to the considered acquisition parameters under the selected image preprocessing techniques. A machine learning-based strategy was implemented to measure the covariate shift between the analyzed datasets using features computed using the aforementioned preprocessing methods. Finally, radiomic scores (rad-scores) were constructed by identifying features relevant to patients' overall survival after eliminating those impacted by scanner variability. These were then evaluated for their prognostic significance through Kaplan-Meier and Cox hazards regression analyses. Our results demonstrate that overall, histogram standardization contributes the most in reducing radiomic feature variability as it is the technique to reduce the covariate shift for three feature categories and successfully discriminate patients into groups of different survival risks.

BackgroundGlioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis.Methods159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of MGMT status with radiomics was also investigated and all results were validated on the independent test set.ResultsLASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with MGMT identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005).ConclusionOur results suggest that combining radiomics with MGMT is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated MGMT: one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS.

Background Melanoma brain metastases historically portend a dismal prognosis, but recent advances in immune checkpoint inhibitors (ICIs) have been associated with durable responses in some patients. There are no validated imaging biomarkers associated with outcomes in patients with melanoma brain metastases receiving ICIs. We hypothesized that radiomic analysis of magnetic resonance images (MRIs) could identify higher-order features associated with survival. Methods Between 2010 and 2019, we retrospectively reviewed patients with melanoma brain metastases who received ICI. After volumes of interest were drawn, several texture and edge descriptors, including first-order, Haralick, Gabor, Sobel, and Laplacian of Gaussian (LoG) features were extracted. Progression was determined using Response Assessment in Neuro-Oncology Brain Metastases. Univariate Cox regression was performed for each radiomic feature with adjustment for multiple comparisons followed by Lasso regression and multivariate analysis. Results Eighty-eight patients with 196 total brain metastases were identified. Median age was 63.5 years (range, 19–91 y). Ninety percent of patients had Eastern Cooperative Oncology Group performance status of 0 or 1 and 35% had elevated lactate dehydrogenase. Sixty-three patients (72%) received ipilimumab, 11 patients (13%) received programmed cell death protein 1 blockade, and 14 patients (16%) received nivolumab plus ipilimumab. Multiple features were associated with increased overall survival (OS), and LoG edge features best explained the variation in outcome (hazard ratio: 0.68, P = 0.001). In multivariate analysis, a similar trend with LoG was seen, but no longer significant with OS. Findings were confirmed in an independent cohort. Conclusion Higher-order MRI radiomic features in patients with melanoma brain metastases receiving ICI were associated with a trend toward improved OS.

Purpose The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features. Method Fluid‐attenuated inversion recovery (FLAIR) and contrast‐enhanced T1‐weighted (T1WICE) MRI of 90 patients diagnosed with glioblastoma were segmented using a semiautomatic algorithm and an interactive segmentation with two different raters. We analyzed the robustness of 108 radiomic features from five categories (intensity histogram, gray‐level co‐occurrence matrix, gray‐level size‐zone matrix (GLSZM), edge maps, and shape) using intra‐class correlation coefficient (ICC) and Bland and Altman analysis. Results Our results show that both segmentation methods are reliable with ICC ≥ 0.96 and standard deviation (SD) of mean differences between the two raters (SDdiffs) ≤ 30%. Features computed from the histogram and co‐occurrence matrices were found to be the most robust (ICC ≥ 0.8 and SDdiffs ≤ 30% for most features in these groups). Features from GLSZM were shown to have mixed robustness. Edge, shape, and GLSZM features were the most impacted by the choice of segmentation method with the interactive method resulting in more robust features than the semiautomatic method. Finally, features computed from T1WICE and FLAIR images were found to have similar robustness when computed with the interactive segmentation method. Conclusion Semiautomatic and interactive segmentation methods using two raters are both reliable. The interactive method produced more robust features than the semiautomatic method. We also found that the robustness of radiomic features varied by categories. Therefore, this study could help motivate segmentation methods and feature selection in MRI radiomic studies.