Le lymphome de Hodgkin : stratégies thérapeutiques actuelles et futures

Macrophage activation syndrome revealing Hodgkin's lymphoma: a case report

Macrophage activation syndrome (MAS) is a rare immunologic syndrome, rapidly fatal in the absence of specific etiological treatment. It is defined by clinical, biological and cyto-histological criteria. Numerous etiologies have been described in MAS, the association with Hodgkin lymphoma (LH) is exceptional. We report the case of a young woman in whom a macrophage activation syndrome reveals a Hodgkin's lymphoma.

Prolonged Remission by Pembrolizumab and Brentuximab-Vedotin Combination Therapy in Heavily-Pretreated Relapsed/Refractory Hodgkin's Lymphoma

Hodgkin’s lymphoma (HL) is usually sensitive and curative to multi-agent chemotherapy, but may become refractory disease in a subset of relapsed patients. Recent novel agents, brentuximab-vedotin (BV) and immune checkpoint inhibitors have significantly improve the treatment outcome. We report the outcome by combination of BV with pembrolizumab in a patient with a relapsed/refractory HL in a remarkable and durable response, even previously failed to multiple lines of chemotherapy, or brentuximab-vedotin/pembrolizumab monotherapy. Further investigation of immunotherapy combination in relapsed/refractory HL is needed.

Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy

PURPOSE

New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing treatment with immune checkpoint blockers (ICB). We aimed to develop and validate an imaging-based decision-making algorithm for use by the clinician that helps differentiate pituitary metastasis (PM) from ICB-induced autoimmune hypophysitis (HP).

MATERIALS AND METHODS

A systematic search was performed in the MEDLINE and EMBASE databases up to October 2018 to identify studies concerning PM and HP in patients treated with cytotoxic T-lymphocyte-associated protein 4 and programmed cell death (ligand) 1. The reference standard for diagnosis was confirmation by histology or response on follow-up imaging. Patients from included studies were randomly assigned to the training set or the validation set. Using machine learning (random forest tree algorithm) with the most-described six imaging and three clinical features, a multivariable prediction model (the signature) was developed and validated for diagnosing PM. Signature performance was evaluated using area under a receiver operating characteristic curves (AUCs).

RESULTS

Out of 3174 screened articles, 65 were included totalising 122 patients (HP: 60 pts, PM: 62 pts). Complete radiological data were available in 82 pts (Training: 62 pts, Validation: 20 pts). The signature reached an AUC = 0.91 (0.82, 1.00), P < 10^-8 in the training set and AUC = 0.94 (0.80, 1.00), P = 0.001 in the validation set. The signature predicted PM in lesions either ≥ 2 cm in size or < 2 cm if associated with heterogeneous contrast enhancement and cavernous extension.

CONCLUSION

An image-based signature was developed with machine learning and validated for differentiating PM from HP. This tool could be used by clinicians for enhanced decision-making in cancer patients undergoing ICB treatment with new-onset, concerning lesions of the pituitary gland.

Detection of immune-related adverse events by medical imaging in patients treated with anti-programmed cell death 1

BACKGROUND

Programmed death receptor-1 blocking antibodies (anti-PD1) are a new standard of care in many cancer types. Patients benefit from improved survival but have the risk of immune-related adverse events (irAE). We evaluated if medical imaging procedures, used for anti-tumour response assessment, can detect irAEs.

MATERIALS AND METHODS

All consecutive patients treated with anti-PD1 and with a medical imaging acquisition performed within 2 weeks with irAEs ≥2 were retrospectively included. Data were gathered from June 2014 to February 2017, and a central review was performed. The primary and secondary end-points were i) to evaluate the overall detection rate of irAEs by medical imaging and ii) to provide a comprehensive radiological description of irAEs.

RESULTS

Fifty-three patients (31 women, 22 men; average age: 61 years) were included. The primary tumour was melanoma (n = 32), lung cancer (n = 18) and other (n = 3). Patients were treated with nivolumab (n = 27) or pembrolizumab (n = 26). Of 74 medical imaging procedures analysed (ratio = 1.4 medical imaging per patient), 55 irAE were detected. The detection rate was overall: 74% (95 confidence interval: 63-84%), positron emission tomography with 18F-fludeoxyglucose integrated with computed tomography (18F-FDG PET/CT): 83% (n = 10/12), magnetic resonance imaging: 83% (n = 5/6), computed tomography scan: 79% (n = 19/24), ultrasonography: 70% (n = 19/27), standard X-rays: 40% (n = 2/5), lung/mediastinum: 100% (n = 7/7), enterocolitis: 100% (n = 8/8), hypophysitis: 100% (n = 3/3), thyroiditis: 75% (n = 15/20), hepatitis: 67% (n = 2/3), arthralgia or arthritis: 40% (n = 2/5) and pancreas: 28% (n = 2/7).

CONCLUSION

Medical imaging detected 74% of irAE in patients treated with anti-PD1. Beyond response assessment, medical imaging can detect irAE and guide towards specific management. We described the most frequent sites and patterns of imaging findings.