RT Journal Article SR Electronic T1 Distinction of Lymphoma from Sarcoidosis on 18F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance JF Journal of Nuclear Medicine JO J Nucl Med FD Society of Nuclear Medicine SP 1933 OP 1940 DO 10.2967/jnumed.121.263598 VO 63 IS 12 A1 Pierre Lovinfosse A1 Marta Ferreira A1 Nadia Withofs A1 Alexandre Jadoul A1 Céline Derwael A1 Anne-Noelle Frix A1 Julien Guiot A1 Claire Bernard A1 Anh Nguyet Diep A1 Anne-Françoise Donneau A1 Marie Lejeune A1 Christophe Bonnet A1 Wim Vos A1 Patrick E. Meyer A1 Roland Hustinx YR 2022 UL http://jnm.snmjournals.org/content/63/12/1933.abstract AB Sarcoidosis and lymphoma often share common features on 18F-FDG PET/CT, such as intense hypermetabolic lesions in lymph nodes and multiple organs. We aimed at developing and validating radiomics signatures to differentiate sarcoidosis from Hodgkin lymphoma (HL) and diffuse large B-cell lymphoma (DLBCL). Methods: We retrospectively collected 420 patients (169 sarcoidosis, 140 HL, and 111 DLBCL) who underwent pretreatment 18F-FDG PET/CT at the University Hospital of Liege. The studies were randomly distributed to 4 physicians, who gave their diagnostic suggestion among the 3 diseases. The individual and pooled performance of the physicians was then calculated. Interobserver variability was evaluated using a sample of 34 studies interpreted by all physicians. Volumes of interest were delineated over the lesions and the liver using MIM software, and 215 radiomics features were extracted using the RadiomiX Toolbox. Models were developed combining clinical data (age, sex, and weight) and radiomics (original and tumor-to-liver TLR radiomics), with 7 different feature selection approaches and 4 different machine-learning (ML) classifiers, to differentiate sarcoidosis and lymphomas on both lesion-based and patient-based approaches. Results: For identifying lymphoma versus sarcoidosis, physicians’ pooled sensitivity, specificity, area under the receiver-operating-characteristic curve (AUC), and accuracy were 0.99 (95% CI, 0.97–1.00), 0.75 (95% CI, 0.68–0.81), 0.87 (95% CI, 0.84–0.90), and 89.3%, respectively, whereas for identifying HL in the tumor population, it was 0.58 (95% CI, 0.49–0.66), 0.82 (95% CI, 0.74–0.89), 0.70 (95% CI, 0.64–0.75) and 68.5%, respectively. Moderate agreement was found among observers for the diagnosis of lymphoma versus sarcoidosis and HL versus DLBCL, with Fleiss κ-values of 0.66 (95% CI, 0.45–0.87) and 0.69 (95% CI, 0.45–0.93), respectively. The best ML models for identifying lymphoma versus sarcoidosis showed an AUC of 0.94 (95% CI, 0.93–0.95) and 0.85 (95% CI, 0.82–0.88) in lesion- and patient-based approaches, respectively, using TLR radiomics (plus age for the second). To differentiate HL from DLBCL, we obtained an AUC of 0.95 (95% CI, 0.93–0.96) in the lesion-based approach using TLR radiomics and 0.86 (95% CI, 0.80–0.91) in the patient-based approach using original radiomics and age. Conclusion: Characterization of sarcoidosis and lymphoma lesions is feasible using ML and radiomics, with very good to excellent performance, equivalent to or better than that of physicians, who showed significant interobserver variability in their assessment.