RT Journal Article
SR Electronic
T1 Distinction of lymphoma from sarcoidosis at FDG PET/CT - evaluation of radiomic-feature guided machine learning versus human reader performance
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP jnumed.121.263598
DO 10.2967/jnumed.121.263598
A1 Pierre Lovinfosse
A1 Marta Ferreira
A1 Nadia Withofs
A1 Alexandre Jadoul
A1 Celine Derwael
A1 Anne-Noelle Frix
A1 Julien Guiot
A1 Claire Bernard
A1 Anh Nguyet Diep
A1 Anne-Francoise 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/early/2022/05/19/jnumed.121.263598.abstract
AB Sarcoidosis and lymphoma often share common features on 18F-FDG PET/CT, such as intense hypermetabolic lesions of lymph nodes and multiple organs. We aimed at developing and validating radiomics signatures to differentiate sarcoidosis from Hodgkin (HL) and diffuse large B-cell (DLBCL) lymphoma. Methods: We retrospectively collected 420 patients (169 sarcoidosis, 140 HL and 111 DLBCL) who underwent a pretreatment 18F-FDG PET/CT at the University Hospital of Liege. The studies were randomly distributed to 4 physicians who gave their diagnostic suggestion between the 3 diseases. Individual and pooled performances of physicians were then calculated. The inter-observer variability was evaluated using a sample of 34 studies interpreted by all physicians. Volumes of interest (VOI) were delineated over the lesions and the liver using MIM software, and 215 radiomic features were extracted using Radiomics toolbox. Models were developed combining clinical data (age, gender 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 vs. sarcoidosis, physicians’ pooled sensitivity, specificity, area under the curve (AUC) and accuracy were 0.99 (CI95%:0.97-1.00), 0.75 (CI95%: 0.68-0.81), 0.87 (CI95%: 0.84-0.90) and 89.3%, respectively, whereas for identifying HL in the tumor population, it was 0.58 (CI95%: 0.49-0.66), 0.82 (CI95%: 0.74-0.89), 0.70 (CI95%: 0.64-0.75) and 68.5%, respectively. A moderate agreement was found between observers for the diagnosis of lymphoma vs. sarcoidosis and HL vs. DLBCL with Fleiss kappa values of 0.66 (CI95%: 0.45-0.87) and 0.69 (CI95%: 0.45-0.93), respectively. The best ML models for identifying lymphoma vs. sarcoidosis showed AUC of 0.94 (CI95%: 0.93-0.95) and 0.85 (CI95%: 0.82-0.88) in lesion- and patient-based approaches, respectively, using TLR radiomics (+ age for the second). To differentiate HL and DLBCL, we obtained AUC of 0.95 (CI95%: 0.93-0.96) in lesion-based approach using TLR radiomics, and 0.86 (CI95%: 0.80-0.91) in patient-based using original radiomics and age. Conclusion: Characterization of sarcoidosis and lymphoma lesions is feasible using ML and radiomics, with very good to excellent performances, equivalent or better than those of doctors who showed significant interobserver variability in their assessment.