Immunotherapies that employ immune checkpoint modulators (ICMs) have emerged as an effective treatment for a variety of solid cancers, as well as a paradigm shift in the treatment of cancers. Despite this breakthrough, the median survival time of glioblastoma patients has remained at about 2 years. Therefore, the safety and anti-cancer efficacy of combination therapies that include ICMs are being actively investigated. Because of the distinct mechanisms of ICMs, which restore the immune system’s anti-tumor capacity, unconventional immune-related phenomena are increasingly being reported in terms of tumor response and progression, as well as adverse events. Indeed, immunotherapy response assessments for neuro-oncology (iRANO) play a central role in guiding cancer patient management and define a “wait and see strategy” for patients treated with ICMs in monotherapy with progressive disease on MRI. This article deciphers emerging research trends to ameliorate four challenges unaddressed by the iRANO criteria: (1) patient selection, (2) identification of immune-related phenomena other than pseudoprogression (i.e., hyperprogression, the abscopal effect, immune-related adverse events), (3) response assessment in combination therapies including ICM, and (4) alternatives to MRI. To this end, our article provides a structured approach for standardized selection and reporting of imaging modalities to enable the use of precision medicine by deciphering the characteristics of the tumor and its immune environment. Emerging preclinical or clinical innovations are also discussed as future directions such as immune-specific targeting and implementation of artificial intelligence algorithms.

The incidence of primary central nervous system lymphoma has increased over the past two decades in immunocompetent patients and the prognosis remains poor. A diagnosis and complete evaluation of the patient is needed without delay, but histologic evaluation is not always available and PCNSL can mimic a variety of brain lesions on MRI. In this article, we review the potential role of ¹⁸F-FDG PET for the diagnosis of PCNSL in immunocompetent and immunocompromised patients. Its contribution to systemic assessment at the time of diagnosis has been well established by expert societies over the past decade. In addition, ¹⁸F-FDG provides valuable information for differential diagnosis and outcome prediction. The literature also shows the potential role of ¹⁸F-FDG as a therapeutic evaluation tool during the treatment and the end of the treatment. Finally, we present several new radiotracers that may have a potential role in the management of PCNSL in the future.

Objective: Skull base osteomyelitis (SBO) is a rare but life‐threatening disease observed in elderly diabetic patients, with high risk of recurrence and difficult therapeutic management. The diagnosis is ascertained from a set of clinical, biological, and imaging findings. CT and MRI allow initial diagnosis, but are not accurate to affirm healing at the end of therapy. 99mTc‐HMPAO‐Leucocyte Scintigraphy (LS) is highly sensitive and specific for the detection of infection. The aim of this study was to evaluate LS i) for initial diagnosis, and ii) to confirm healing at the end of antibiotherapy in SBO.Study design: We retrospectively reviewed from November 2011 to September 2015 all patients with confirmed SBO who underwent LS twice, at diagnosis and at the end of antibiotic therapy in our nuclear medicine department (n = 27).Methods: Clinical, biological, CT, LS, and follow‐up data were recorded in all patients. LS images (planar and tomographic performed 4 hours and 24 hours after intravenous injection of autologous Tc‐99m‐HMPAO‐leucocytes) were visually assessed and quantified.Results: At initial diagnosis, 25 of 27 patients had a positive LS. At the end of antibiotic therapy (3 ± 1 months duration), 26 of 27 patients had a negative LS. During subsequent follow‐up (= or >6 months), the disease recurred in four patients including three with a negative postantibiotherapy LS scan.Conclusion: In this retrospective study, LS was powerful for initial diagnostic of SBO and for healing assessment at the end of antibiotic therapy. We conclude it is a useful technique for therapeutic monitoring of SBO.Level of Evidence: 4

This study evaluates the relevance of ¹⁸F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic ¹⁸F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features—radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both—in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBR_mean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values.