Circulating tumor cells: clinical validity and utility.

Circulating tumor cells (CTCs) are rare tumor cells and have been investigated as diagnostic, prognostic and predictive biomarkers in many types of cancer. Although CTCs are not currently used in clinical practice, CTC studies have accumulated a high level of clinical validity, especially in breast, lung, prostate and colorectal cancers. In this review, we present an overview of the current clinical validity of CTCs in metastatic and non-metastatic disease, and the main concepts and studies investigating the clinical utility of CTCs. In particular, this review will focus on breast, lung, colorectal and prostate cancer. Three major topics concerning the clinical utility of CTC are discussed-(1) treatment based on CTCs used as liquid biopsy, (2) treatment based on CTC count or CTC variations, and (3) treatment based on CTC biomarker expression. A summary of published or ongoing phase II and III trials is also presented.

Increased Multispectral CT Iodine Concentrations in Patients With Transient Neurological Deterioration Following Endovascular Neurointerventional Procedures: an Argument in Favor of the Elusive Contrast-Induced Encephalopathy?

BACKGROUND AND PURPOSE: So-called contrast-induced encephalopathy (CIE) is a rare but worrying condition occurring after cerebral angiography or neuroendovascular interventions using iodine contrast media. This study aimed to compare cerebral iodine concentrations in patients suspected of having CIE after endovascular procedures to those in matched controls. METHODS: This is a retrospective monocentric study of 25 suspected CIE patients in a tertiary care teaching hospital diagnosed from June 2017 to February 2024. Cerebral multispectral computed tomography (CT) iodine mean concentrations were measured and compared with 1:1 matched controls using the CT constructor's workstation in the whole brain and in specific regions of interest (ROIs) corresponding to a vascular territory downstream of the procedure. Concentration values were compared with paired samples t­test. RESULTS: During the study period, 1097 patients underwent aneurysm embolization and 137 arteriovenous malformation (AVM) embolization procedures. So-called CIE was suspected in 25 patients after aneurysm or AVM embolization (2%). Mean iodine concentrations in the procedure vascular territory ROIs were higher in suspected CIE cases (mean 543 ± 147 µg/cm3) compared to matched controls (mean 463 ± 141 µg/cm3; p = 0.01). Whole brain mean iodine concentrations were modestly higher in CIE patients compared to controls across all subgroups, without reaching statistical significance. CONCLUSIONS: CIE may be associated with modest increase in CT iodine concentration in the procedure vascular territory after neurointerventional procedures. The underlying pathophysiology of this condition remains uncertain and merits further investigation. KEY MESSAGES: Contrast-induced encephalopathy (CIE) is known as a rare neurologic condition following iodine contrast media use in neuroendovascular interventions, with unclear pathophysiology. WHAT THIS STUDY ADDS: This study provides evidence that suspected CIE is associated with higher cerebral iodine concentrations in affected vascular territories, a novel quantifiable change. Implications for research, practice, or policy: These findings suggest the potential for iodine concentration monitoring to refine CIE diagnosis and prevention strategies in clinical practice.

Endovascular therapy in patients with a large ischemic volume at presentation: An aggregate patient-level analysis.

INTRODUCTION: Recently, four randomized controlled trials (RCTs) have demonstrated the benefits of mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) caused by anterior large vessel occlusion (LVO) and a large ischemic core at baseline (LIC). The purpose of this study was to investigate the features influencing the clinical outcome and the benefits of mechanical thrombectomy in this subgroup. METHODS: We conducted a multicenter retrospective aggregate cohort study of patients with AIS-LVO and a LIC, assessed with quantitative core volume measures, treated with MT between 2012 and 2019. The data were queried through four registries, including patients with core volumes ≥50cc. Multivariable logistic regression models were employed to determine factors independently associated with clinical outcomes in patients with successful recanalization (modified-Thrombolysis-in-Cerebral-Infarction-score, mTICI=2b-3) and unsuccessful recanalization group (mTICI=0-2a). The primary endpoint was a favorable functional outcome at day-90, defined as a modified Rankin scale (mRS) of 0-3, accounting for the inherent severity of AIS with baseline LIC. Secondary outcomes included functional independence (mRS 0-2) at day-90, mortality, and symptomatic Intracranial Hemorrhage (sICH). RESULTS: A total of 460 patients were included (mean age 66±14.2 years; 39.6 % females). The mean baseline NIHSS was 20±5.2, and the core volume was 103.2±54.6 ml. Overall, 39.8 % (183/460) of patients achieved a favorable outcome at day-90 (mRS 0-3). Successful recanalization was significantly associated with a more frequent favorable outcome (aOR, 4.79; 95 %CI, 2.73-8.38; P<0.01) and functional independence (P<0.01). This benefit remained significant in older patients and in patients with cores above 100cc. At 90 days, 147/460 patients (32 %) were deceased, with successful recanalization significantly associated with less frequent mortality (OR, 0.34; 95 %CI, 0.22-0.53; P<0.01). The rate of sICH was 17.4 % and did not differ significantly between groups. CONCLUSIONS: In this large, pooled-cohort study of AIS-LVO patients with infarct cores over 50cc at baseline, we demonstrated that successful recanalization was associated with a better functional outcome, lower mortality, and similar rates of symptomatic intracranial hemorrhage for a wide spectrum of patients.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.