Molecular Testing of Atypical Thyroid Nodules with Corresponding Surgical Correlation: Five-Year Retrospective Review in Veterans Population.

Objective We report the results of a retrospective five-year study within a veteran population aimed at correlating abnormal thyroid fine-needle aspiration (FNA) diagnosis with associated molecular testing to the histology of the surgical resection. Methods A retrospective analysis of abnormal thyroid FNAs with associated molecular testing and surgical outcome was conducted from January 1, 2015 to December 31, 2020. Aspirates were classified using the Bethesda system for reporting thyroid cytopathology, including atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), follicular neoplasm/suspicious for follicular neoplasm (FN/SFN), suspicious for malignancy (SM), and malignant. Pertinent data, including patient demographics, imaging, and ancillary testing were reviewed. A thyroid cancer mutation panel assessing the most common mutations and rearrangements associated with neoplasia was utilized. The results of molecular testing were directly compared and correlated with final cytological and histological diagnosis. Results A total of 1850 thyroid aspirates were performed, 200 of which were given an abnormal cytologic diagnosis. Thirty-six samples were submitted for molecular testing and subsequent surgical follow-up. Four were called malignant on cytology. 32 were placed in an indeterminate category (89%). Within indeterminate cases: 53% exhibited positive molecular mutations (n=17), 34% no mutation detected (n=11), and 13% insufficient quantity for testing (n=4). Upon surgical resection in the mutation-positive group: 18% had no malignancy (n=3), and the remaining 82% were positive for malignancy (n=14). Mutations in the histologically malignant group included: 57% BRAF (n=8), 21% NRAS (n=3), 7% HRAS (n=1), 7% KRAS (n=1), and 7% PAX8/PPAR gamma (n=1). In indeterminate cases with no mutation detected, 10 cases were found to be benign, and one case of malignancy was diagnosed. The probability of indeterminate diagnosis in combination with no mutation yielded a 91% chance of benign entity and 9% chance of malignancy. We demonstrated 93% sensitivity and 91% negative predictive value (NPV) for the risk of malignancy in indeterminate cytology specimens with ancillary molecular testing. There was 77% specificity and 82% positive predictive value (PPV) for our data set. Conclusions In indeterminate samples, the detection of a mutation was highly predictive of malignancy and a strong indicating factor for surgery with a high sensitivity and NPV. Molecular testing refined or established the diagnosis in 89% of the cases. Our results indicate that molecular testing of thyroid nodules enhances the accuracy of FNA cytology and the subsequent surgical outcome.

Cervical Cancer Screening in South Florida Veteran Population, 2014 to 2020: Cytology and High-Risk Human Papillomavirus Correlation and Epidemiology.

Objective This project aims to use our robust women's health patient data to analyze the correlation between cytology and high-risk human papillomavirus (Hr-HPV) testing, study the performance of Hr-HPV testing for detecting cytology lesions, and examine epidemiologic measures of human papillomavirus (HPV) infections in the women's veteran population. Methods We collected patient data from 2014 to 2020 from our computerized patient record system. We performed HPV assays using the cobas® 4800 system (Roche Diagnostics, Basel, Switzerland). The cobas HPV assay detects HPV 16, HPV 18, and 12 other HPV types (31, 33, 35, 39, 45, 51, 56, 58, 59, 66, and 68). We organized cytology results and Hr-HPV assays with Microsoft Access and Microsoft Excel (Microsoft Corporation, Washington, USA) for analysis. Results A total of 9437 cervical specimens were co-tested. High-grade cytology lesions - high-grade intraepithelial lesion (HSIL) or higher and atypical squamous cells, cannot exclude HSIL (ASC-H) - were overwhelmingly positive for Hr-HPV (94.1% and 87.2%, respectively). Low-grade cytology lesions - low-grade squamous intraepithelial lesion ((LSIL) and atypical squamous cells of undetermined significance (ASC-US) - were positive for Hr-HPV in lower percentages (72.6% and 54.9%, respectively). Hr-HPV testing had a sensitivity of 91.3%, a specificity of 93.1%, a positive predictive value of 16.4%, and a negative predictive value of 99.8% for detecting high-grade cytology lesions. Hr-HPV testing had a lower performance for detecting low-grade cytology lesions. Ten cases had high-grade cytology and negative Hr-HPV test. Out of 10 such patients, nine showed no dysplasia (six) or low-grade dysplasia (three) on subsequent biopsy. Overall, 14.4% of tests were positive for Hr-HPV. The highest positive Hr-HPV test rates were in the third and eighth decades of life, 25.1% and 22.0%, respectively. However, the eighth decade consisted of a small sample of only 50 women. In women over 30 years of age with Hr-HPV infections, HPV types 16 and 18 were present in 11.7% and 6.4% of tests, respectively. Other HPV types were present in 82.3% of tests. Conclusions Hr-HPV testing has a high performance in detecting high-grade cytology lesions and a lower performance for detecting low-grade cytology lesions. However, studies show that LSIL rarely progresses to cervical intraepithelial neoplasia grade 3 or higher (CIN3+), suggesting minimal to no impact on cervical cancer screening. We believe our findings are in accordance with recent studies and affirm the guidelines that recommend primary Hr-HPV testing as the preferred screening method. The percentage of positive Hr-HPV tests and rates for age and HPV types 16 and 18 in our women's veteran population suggest similar HPV prevalence to that of the general US population.

Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.

BACKGROUND: Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. In only a few months, it has had a dramatic impact on society and world economies. COVID-19 has presented numerous challenges to all aspects of health care, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications for health care. Machine learning is a subset of AI that uses deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than can humans. METHODS: In this article, we explore the potential for the simple and widely available chest X-ray (CXR) to be used with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. RESULTS: Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. CONCLUSIONS: We have used a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. The findings have implications for screening and triage, initial diagnosis, monitoring disease progression, and identifying patients at increased risk of morbidity and mortality. Based on the data, a website was created to demonstrate how such technologies could be shared and distributed to others to combat entities such as COVID-19 moving forward.

Time is Precious: person-centred end of life care in an emergency department. A quality improvement project.

Supporting and managing end of life in emergency departments (EDs) is often difficult and is becoming increasingly commonplace. Patients who present at the end of life are often triaged as low priority as their signs and symptoms are not considered life-threatening and they are often exposed to unnecessary and inappropriate tests and investigations. This results in increased stress and distress for patients and their family and carers in an environment that is not suited to this type of care. There are few specified palliative care pathways that provide the level of care required by these patients. This article describes the Time is Precious (TiP) project, the development of a palliative care decision-making framework to support and address the needs of patients who present to an ED at end of life, in a timely and appropriate manner. It also reports findings of an evaluation of TiP that show patients are identified more quickly and cared for more appropriately as nursing and medical care can be tailored to meet their needs.

Comparing Artificial Intelligence Platforms for Histopathologic Cancer Diagnosis.

Two machine learning platforms were successfully used to provide diagnostic guidance in the differentiation between common cancer conditions in veteran populations.

Circulating long noncoding RNA GAS5 levels are correlated to prevalence of type 2 diabetes mellitus.

BACKGROUND: Diabetes mellitus (DM), a metabolic disease, is characterized by impaired fasting glucose levels. Type 2 DM is adult onset diabetes. Long non-coding RNAs (lncRNAs) regulate gene expression and multiple studies have linked lncRNAs to human diseases. METHODS: Serum samples obtained from 96 participating veterans at JAH VA were deposited in the Research Biospecimen Repository. We used a two-stage strategy to identify an lncRNA whose levels correlated with T2DM. Initially we screened five serum samples from diabetic and non-diabetic individuals using lncRNA arrays. Next, GAS5 lncRNA levels were analyzed in 96 serum samples using quantitative PCR. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cutoff GAS5 for diagnosis of DM. RESULTS: Our results demonstrate that decreased GAS5 levels in serum were associated with diabetes in a cohort of US military veterans. The ROC analysis revealed an optimal cutoff GAS5 value of less than or equal to 10. qPCR results indicated that individuals with absolute GAS5 < 10 ng/µl have almost twelve times higher odds of having diabetes (Exact Odds Ratio [OR] = 11.79 (95% CI: 3.97, 37.26), p < 0.001). Analysis indicated area under curve (AUC) of ROC of 0.81 with 85.1% sensitivity and 67.3% specificity in distinguishing non-diabetic from diabetic subjects. The positive predictive value is 71.4%. CONCLUSION: lncRNA GAS5 levels are correlated to prevalence of T2DM. GENERAL SIGNIFICANCE: Assessment of GAS5 in serum along with other parameters offers greater accuracy in identifying individuals at-risk for diabetes.