Radiomic Phenotypes of High and Low Lesion SUV Components for the Prediction of Refractory Disease in Hodgkin's Lymphoma Patients Treated with ABVD Based Therapy

Background: Hodgkin's lymphoma (HL) is a curable malignancy. However, some patients are refractory to frontline therapy. Early prediction of response to frontline therapy could identify patients who may benefit from more intensive therapy. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) has an established role in the management of HL. Radiomic features provide a way of quantifying imaging phenotypes and have shown promising results as predictors of outcomes in different lymphomas. Furthermore, great interest has been focused on the heterogeneity of standardized uptake value (SUV) within a single lesion. Since high SUV component (HSc) and low-SUV component (LSc) regions within a single lesion may be associated with different phenotypical characteristics, the radiomic analysis for each regional SUV component may provide a more complete description of lesions. Therefore, we proposed and evaluated new descriptors to quantify the image phenotypes based on HSc and LSc of lesions in HL. Methods: A total of 61 patients with HL of all stages who were seen at MD Anderson Cancer Center between 2016 and 2020 and had analyzable pre-treatment PET/CT were selected. All patients received standard of care ABVD or AVD regimens with or without radiation (Table 1A). Pre-treatment PET/CT scans were analyzed, and HL lesions were semi-automatically segmented using MIM 7 (Cleveland, OH) based on a SUV max threshold of 2.5. Manual edits were made and reviewed by a nuclear medicine physician and a lymphoma specialist. A total of 110 radiomic features were extracted from the segmented lesions in CT and PET using the open-source package of ‘PyRadiomics’ (Table 1B). Detailed description and algorithms of the extracted radiomics features are available at https://pyradiomics.readthedocs.io/en/latest/features.html. Additionally, each lesion was partitioned into HSc and LSc based on a cutoff value of 3 times the liver SUV mean (Figure A). The ratio of features between SUV components (HSc, LSc) and the lesion area was calculated. Furthermore, the feature difference between HSc and LSc was obtained. The maximum, minimum, average, and standard deviation of the radiomic features within multiple lesions were computed to reveal the distribution of features. A sequential forward feature selection was applied to select the significant features for building a logistic regression model, to predict refractory disease according to Lugano criteria. Two logistic