A Case of Sarcoidosis After Lymphoma

Bando-Delaunay, Aurélie; Luporsi, Marie; Huchet, Virginie; Cassou-Mounat, Thibaut; Jehanno, Nina

doi:10.1097/rlu.0000000000002666

Cited by 2 publications

(1 citation statement)

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“…Semiquantitative measurements such as the maximum standardized uptake value (SUVmax) have not proven to be the answer either (2,3). Moreover, sarcoidosis can develop before (sarcoidosis-lymphoma syndrome) and after lymphoma, and immunotherapy-induced sarcoidlike reactions are increasingly observed (4)(5)(6)(7). No matter the results of the imaging studies, pathological confirmation of the disease is mandatory in all cases prior to initiating treatment.…”

Section: Introductionmentioning

confidence: 99%

Distinction of Lymphoma from Sarcoidosis on¹⁸F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance

et al. 2022

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Sarcoidosis and lymphoma often share common features on 18 F-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 18 F-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.

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Section: Introductionmentioning

confidence: 99%

Distinction of Lymphoma from Sarcoidosis on¹⁸F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance

et al. 2022

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De Novo Presentation of Sarcoidosis on Restaging FDG PET/CT in Rectal Carcinoma With Self-resolution

Somanna¹,

Jeeva²,

Raja

2020

Clin Nucl Med

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Sarcoidosis is a systemic disorder of unknown etiology characterized by development of noncaseating granulomas in more than 1 organ system. Development of sarcoidosis during or immediately after chemotherapy and immunotherapy is not uncommon. We present a 61-year-old woman in whom restaging 18F-FDG PET/CT detected asymptomatic sarcoidosis after neoadjuvant chemoradiotherapy for carcinoma rectum, which resolved spontaneously by the end of adjuvant chemotherapy with no specific treatment. Recognition of anatomic-metabolic pattern of sarcoidosis could prevent erroneous upstaging of the primary malignancy during restaging PET/CT following chemotherapy, and such lesions may show self-resolution.

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A Case of Sarcoidosis After Lymphoma

Cited by 2 publications

References 6 publications

Distinction of Lymphoma from Sarcoidosis on¹⁸F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance

Distinction of Lymphoma from Sarcoidosis on¹⁸F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance

De Novo Presentation of Sarcoidosis on Restaging FDG PET/CT in Rectal Carcinoma With Self-resolution

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A Case of Sarcoidosis After Lymphoma

Cited by 2 publications

References 6 publications

Distinction of Lymphoma from Sarcoidosis on18F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance

Distinction of Lymphoma from Sarcoidosis on18F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance

De Novo Presentation of Sarcoidosis on Restaging FDG PET/CT in Rectal Carcinoma With Self-resolution

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Distinction of Lymphoma from Sarcoidosis on¹⁸F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance

Distinction of Lymphoma from Sarcoidosis on¹⁸F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance