Search

Purpose: The survival outcomes of patients with primary uveal melanomas based on Medicaid status have not been previously discussed in the literature., Methods: The Surveillance, Epidemiology, and End Results Medicaid database were utilized to identify patients with primary uveal melanomas diagnosed between 2006 and 2013. The Kaplan-Meier method was utilized to construct 5-year survival curves in adult, non-elderly patients. Log-rank testing was used to determine differences in survival rates, and multivariate Cox proportional hazards modeling was utilized to perform adjusted survival analysis., Results: A total of 1,765 patients were included (Medicaid: 81, non-Medicaid: 1684). A total of 1683 (95.4%) were White. The average age was 51.75 years (SD = 9.5 years). Medicaid patients were more likely to be unmarried, live in a high poverty neighborhood, and live in a rural area (all p  < .001). We observed no significant difference in 5-year survival rates between those enrolled in Medicaid (86.6%, 95% CI: 79.1%1-94.7%) and those not enrolled in Medicaid (85.5, 95% CI: 83.8%-87.2%) ( p  = .80). After controlling for socioeconomic and clinical factors, Medicaid enrollment was not associated with an increased risk of mortality compared to non-Medicaid enrollment. Age (aHR: 1.04, 95% CI: 1.02-1.06, p  < .001) and tumor size >10 mm (aHR: 3.04, 95% CI: 1.49-6.21, p  = .002) were associated with an increased risk of mortality., Conclusion: Medicaid enrollment was not associated with worse cancer-specific 5-year survival. Further research needs to be elicited to better understand the role of Medicaid enrollment in patients with primary uveal melanoma.

Background: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19., Methods: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists., Findings: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001)., Interpretation: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19., Funding: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health., Competing Interests: Declaration of interests HXB reports grants from Brown University, Amazon Web Service, Radiological Society of North America, and National Cancer Institute of the National Institute of Health, during the conduct of the study. XF currently works in Carina Medical, a for-profit organisation that develops clinical products, outside of the submitted work. KC reports grants from National Institute of Biomedical Imaging and Bioengineering and National Cancer Institute of the National Institute of Health, during the conduct of the study. YF reports grants from National Institute of Health, during the conduct of the study. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Large-scale cancer sequencing data enable discovery of rare germline cancer susceptibility variants. Here we systematically analyse 4,034 cases from The Cancer Genome Atlas cancer cases representing 12 cancer types. We find that the frequency of rare germline truncations in 114 cancer-susceptibility-associated genes varies widely, from 4% (acute myeloid leukaemia (AML)) to 19% (ovarian cancer), with a notably high frequency of 11% in stomach cancer. Burden testing identifies 13 cancer genes with significant enrichment of rare truncations, some associated with specific cancers (for example, RAD51C, PALB2 and MSH6 in AML, stomach and endometrial cancers, respectively). Significant, tumour-specific loss of heterozygosity occurs in nine genes (ATM, BAP1, BRCA1/2, BRIP1, FANCM, PALB2 and RAD51C/D). Moreover, our homology-directed repair assay of 68 BRCA1 rare missense variants supports the utility of allelic enrichment analysis for characterizing variants of unknown significance. The scale of this analysis and the somatic-germline integration enable the detection of rare variants that may affect individual susceptibility to tumour development, a critical step toward precision medicine.