Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma. (March 2022)

• Record Type: Journal Article
• Authors: Jimenez, Jorge E.; Dai, Dong; Xu, Guofan; Zhao, Ruiyang; Li, Tengfei; Pan, Tinsu; Wang, Linghua; Lin, Yingyan; Wang, Zhangyang; Jaffray, David; Hazle, John D.; Macapinlac, Homer A.; Wu, Jia; Lu, Yang
• Is Part Of: Clinical nuclear medicine. Volume 47:Number 3(2022)
• Publication Date: 2022-03
• Subjects: lymphoma -- ibrutinib -- FDG PET/CT -- response prediction -- radiomics -- pretherapy; Nuclear medicine -- Periodicals; Radioisotope scanning -- Periodicals; Nuclear Medicine -- Periodicals; 616.07575
• DOI: 10.1097/RLU.0000000000004060
• Languages: English
• ISSNs: 0363-9762

Abstract

Abstract : Purpose: The aim of this study was to develop a pretherapy PET/CT-based prediction model for treatment response to ibrutinib in lymphoma patients. Patients and Methods: One hundred sixty-nine lymphoma patients with 2441 lesions were studied retrospectively. All eligible lymphomas on pretherapy 18 F-FDG PET images were contoured and segmented for radiomic analysis. Lesion- and patient-based responsiveness to ibrutinib was determined retrospectively using the Lugano classification. PET radiomic features were extracted. A radiomic model was built to predict ibrutinib response. The prognostic significance of the radiomic model was evaluated independently in a test cohort and compared with conventional PET metrics: SUVmax, metabolic tumor volume, and total lesion glycolysis. Results: The radiomic model had an area under the receiver operating characteristic curve (ROC AUC) of 0.860 (sensitivity, 92.9%, specificity, 81.4%; P < 0.001) for predicting response to ibrutinib, outperforming the SUVmax (ROC AUC, 0.519; P = 0.823), metabolic tumor volume (ROC AUC, 0.579; P = 0.412), total lesion glycolysis (ROC AUC, 0.576; P = 0.199), and a composite model built using all 3 (ROC AUC, 0.562; P = 0.046). The radiomic model increased the probability of accurately predicting ibrutinib-responsive lesions from 84.8% (pretest) to 96.5% (posttest). At the patient level, the model's performance (ROC AUC = 0.811; P = 0.007) was superior to that of conventional PET metrics. Furthermore, the radiomic model showed robustness when validated in treatment subgroups: first (ROC AUC, 0.916; P < 0.001) versus second or greater (ROC AUC, 0.842; P < 0.001) line of defense and single treatment (ROC AUC, 0.931; P < 0.001) versus multiple treatments (ROC AUC, 0.824; P < 0.001). Conclusions: We developed and validated a pretherapy PET-based radiomic model to predict response to treatment with ibrutinib in a diverse cohort of lymphoma patients. Abstract : Supplemental digital content is available in the text.