Citations

ABSTRACT Objective This study aims to identify both fluid and neuroimaging biomarkers for CSF1R‐RD that can inform the optimal timing of treatment administration to maximize therapeutic benefit, while also providing sensitive quantitative measurements to monitor disease progression. Methods Our study compared neuroimaging and fluid (plasma and cerebrospinal fluid (CSF)) biomarkers across three distinct populations: asymptomatic CSF1R pathogenic variant carriers ( N = 14), symptomatic CSF1R pathogenic variant carriers ( N = 17), and healthy controls ( N = 30). We evaluated biomarker correlations with both an established (Montreal Cognitive Assessment (MoCA)) and a novel (CSF1R Clinical Severity Score (CCSS)) clinical diagnostic scale to investigate potential clinical utility. Additionally, we tested the relationship between select biomarkers and cortical thickness using 3D T1‐weighted MPRAGE scans, providing a highly valuable physiological component to our analyses. Results Our results demonstrate that while plasma glial fibrillary acidic protein (GFAP) displays a high sensitivity for distinguishing early‐stage CSF1R‐RD patients from healthy controls, plasma neurofilament light chain (NfL) is more effective for tracking disease progression following the onset of symptoms. Interpretation Overall, our study provides evidence for plasma NfL and GFAP as valuable biomarkers of earliest symptom onset and disease progression for CSF1R‐RD.

PURPOSE Tumor treating fields (TTFields) use alternating electric fields to disrupt cancer cell proliferation. Feasibility of TTFields therapy with gemcitabine/nab-paclitaxel was previously demonstrated in patients with advanced pancreatic adenocarcinoma. PANOVA-3 was designed to confirm safety and efficacy of TTFields in patients with unresectable locally advanced pancreatic adenocarcinoma (LA-PAC). METHODS In this global phase III trial, 571 patients with newly diagnosed LA-PAC were randomly assigned to receive gemcitabine 1,000 mg/m 2 and nab-paclitaxel 125 mg/m 2 by intravenous infusion once a day on days 1, 8, and 15 of a 28-day cycle with or without TTFields. The primary end point was overall survival (OS). Secondary end points included progression-free survival (PFS), local PFS, pain-free survival, and overall response rate (ORR). Distant PFS was analyzed post hoc. RESULTS OS was significantly prolonged using TTFields with gemcitabine/nab-paclitaxel versus gemcitabine/nab-paclitaxel (median, 16.2 months [95% CI, 15.0 to 18.0] v 14.2 months [95% CI, 12.8 to 15.4]; hazard ratio [HR], 0.82 [95% CI, 0.68 to 0.99]; P = .039). PFS, local PFS, and ORR were not improved. Pain-free survival was significantly prolonged with TTFields with gemcitabine/nab-paclitaxel (median, 15.2 months [95% CI, 10.3 to 22.8] v 9.1 months [95% CI, 7.4 to 12.7]; HR, 0.74 [95% CI, 0.56 to 0.97]; P = .027), as was distant PFS (median, 13.9 months [95% CI, 12.2 to 16.8] v 11.5 months [95% CI, 10.4 to 12.9]; HR, 0.74 [95% CI, 0.57 to 0.96]; P = .022). Device-related skin adverse events (AEs) were experienced by 76.3% of patients. Most device-related skin AEs were mild to moderate, with 7.7% of patients reporting a grade 3 AE. CONCLUSION This study demonstrated significant OS, pain-free survival, and distant PFS benefits for TTFields with gemcitabine/nab-paclitaxel versus gemcitabine/nab-paclitaxel in patients with unresectable LA-PAC, with no additive systemic toxicity.

To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; −2000, 2000) in Bland–Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.