Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning.

BACKGROUND: Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases. METHODS: Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer. RESULTS: Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83-0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89-0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71-0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state. CONCLUSION: CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018.

Sputum analysis by flow cytometry; an effective platform to analyze the lung environment.

Low dose computed tomography (LDCT) is the standard of care for lung cancer screening in the United States (US). LDCT has a sensitivity of 93.8% but its specificity of 73.4% leads to potentially harmful follow-up procedures in patients without lung cancer. Thus, there is a need for additional assays with high accuracy that can be used as an adjunct to LDCT to diagnose lung cancer. Sputum is a biological fluid that can be obtained non-invasively and can be dissociated to release its cellular contents, providing a snapshot of the lung environment. We obtained sputum from current and former smokers with a 30+ pack-year smoking history and who were either confirmed to have lung cancer or at high risk of developing the disease. Dissociated sputum cells were counted, viability determined, and labeled with a panel of markers to separate leukocytes from non-leukocytes. After excluding debris and dead cells, including squamous epithelial cells, we identified reproducible population signatures and confirmed the samples' lung origin. In addition to leukocyte and epithelial-specific fluorescent antibodies, we used the highly fluorescent meso-tetra(4-carboxyphenyl) porphyrin (TCPP), known to preferentially stain cancer (associated) cells. We looked for differences in cell characteristics, population size and fluorescence intensity that could be useful in distinguishing cancer samples from high-risk samples. We present our data demonstrating the feasibility of a flow cytometry platform to analyze sputum in a high-throughput and standardized matter for the diagnosis of lung cancer.

A Pragmatic Approach to Translating Low- and Very Low-Carbohydrate Diets Into Clinical Practice for Patients With Obesity and Type 2 Diabetes.

Across all eating patterns, individuals demonstrate marked differences in treatment response; some individuals gain weight and others lose weight with the same approach. Policy makers and research institutions now call for the development and use of personalized nutrition counseling strategies rather than one-size-fits-all dietary recommendations. However, challenges persist in translating some evidence-based eating patterns into the clinical practice due to the persistent notion that certain dietary approaches-regardless of individuals' preferences and health outcomes-are less healthy than others. For example, low- and very low-carbohydrate ketogenic diets (VLCKDs)-commonly defined as 10-26% and <10% total daily energy from carbohydrate, respectively-are recognized as viable lifestyle change options to support weight loss, glycemic control, and reduced medication use. Yet, critics contend that such eating patterns are less healthy and encourage general avoidance rather than patient-centered use. As with all medical treatments, the potential benefits and risks must be considered in the context of patient-centered, outcome-driven care; this is the cornerstone of evidence-based medicine. Thus, the critical challenge is to identify and safely support patients who may prefer and benefit from dietary carbohydrate restriction. In this Perspective, we propose a pragmatic, 4-stepped, outcome-driven approach to help health professionals use carbohydrate-restricted diets as one potential tool for supporting individual patients' weight loss and metabolic health.

Oncocytic carcinoma of the nasal septum, a rare cause of unilateral epistaxis

A case of a 74-year-old male with unilateral nasal obstruction, recurrent epistaxis and a right intranasal mass is presented. It was initially diagnosed as hemangioma but final histopathology report revealed oncocytic carcinoma. Oncocytic carcinoma is a rare tumor of the salivary glands with very few reported cases, most of which involve the parotid gland. It has a tendency to recur with inadequate excision. Diagnosis is histopathologic. It is to be emphasized that adequate tissue samples should be taken in order to provide a definite diagnosis from biopsy, and subsequently institute proper definitive management.

Head and neck lymph nodes in children: the spectrum from normal to abnormal.

Lymphadenopathy of the head and neck region is a common finding in children and a very common reason to image the craniocervical region. Enlarged lymph nodes are commonly palpated by the pediatrician in the office and commonly imaged by the pediatric radiologist. The difficult task of the clinician is to determine whether the adenopathy is acute (<3 weeks) or chronic (>6 weeks) and when imaging is indicated. In children, radiation is always a consideration when choosing an imaging modality; thus, US is usually the first imaging study at our institution, and CT the second option, usually reserved for the very ill child or for when there is a high index of suspicion for malignancy. We present the normal anatomy of head and neck lymph nodes and the US, CT, and MRI appearances in normal and pathologic states to help clinicians generate a reasonable differential diagnosis and prevent unnecessary procedures.