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Posterior reversible encephalopathy syndrome (PRES) is a rare neurological complication that occurs following a sudden blood pressure increase. We report the case of a 64-year-old patient presenting PRES several hours after the administration of a combination of chemotherapy and a checkpoint inhibitor (carboplatin-etoposide-atezolizumab) for small-cell lung cancer. He presented consciousness disorders associated with partial epileptic seizure secondarily generalized. His arterial blood pressure was elevated and brain imaging showed multiple bilateral subcortical parietal, temporal, occipital and cerebellar T2 high signals, predominantly in the posterior region. There were no abnormal T1 signals nor bleeding but a left apparent diffusion coefficient restriction was noted. On arterial spin labelling perfusion sequences, there was an increased perfusion within the left temporo-parieto-occipital, left thalamic and right cerebellar regions. Finally, the neurological symptoms completely regressed after several days of optimal antihypertensive and antiepileptic treatment. The clinical context and radiological features, as well as the progressive resolution of the neurological symptoms, were all in favor of PRES. PRES can occur after the administration of chemotherapy and/or immunotherapy. Prompt diagnosis is crucial through a spectrum of suspicious clinical and radiological characteristics that must be rapidly recognized to quickly anticipate the optimal therapeutic strategy and avoid unnecessary complications.

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population.

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.