A core laboratory approach to large-scale radiomics and machine-learning prediction of DLBCL outcomes after first-line treatment using results from the phase III GOYA study.

Abstract

e19042 Background: We are using the phase 3 GOYA study to retrospectively investigate using quantitative image texture features (i.e. radiomics, evaluating tumor heterogeneity) to improve the prognostic value of baseline FDG-PET. We report on the considerations for analysis, which is ongoing, and solutions to methodological challenges. Methods: GOYA, an open-label, randomized, phase 3 study, enrolled 1418 patients from 207 centers; 1334 had a baseline FDG-PET scan with detectable lesions. Image data with annotated regions of interest (ROIs) defined by qualified physicians were transferred to the Cuneo imaging core laboratory. Images were interpolated to a common 4x4x4 mm voxel size and ROI masks using 4 different definition methodologies were used to define image sub-volumes that contained the lesions. Large ROIs in normal liver were also defined. The 42 Haralick radiomic image texture features were computed using the PET oncology radiomics test suite. Results: A total of 9286 lesions were analyzed. Interpolation of images to a fixed size is challenging in this cohort due the large number of features to be computed (~1.5M). As a test, the lesions were differentiated from liver ROIs using 5 classifiers (e.g. support vector machine, random forest, etc) with an accuracy of 0.995–0.999. The mean (±SD) of the metabolically active tumor volume was 21.2±22.2 mL (range 0.2–146 mL) with a preponderance of smaller lesions as shown by the median of 15.0 mL. The skewed distribution had downstream effects on many of the quantitative (e.g. total lesion glycolysis) and radiomic image texture features. The majority of texture features were computable; 5 (Coarseness, Contrast, Busyness, Complexity, Texture Strength) required interpolation to a 2x2x2 mm voxel size, requiring extended computation time. FDG-PET images came from a variety of PET/CT scanners; thus, a unified image reconstruction protocol was not possible. Conclusions: Radiomics analysis of large phase 3 imaging trials is feasible. However, careful attention should be paid to the entire processing chain, including tests for non-computable features values due to highly skewed distributions and lesion size. Clinical trial information: NCT01287741.