The molecular basis of lung cancer: molecular abnormalities and therapeutic implications

Lung cancer is the number one cause of cancer-related death in the western world. Its incidence is highly correlated with cigarette smoking, and about 10% of long-term smokers will eventually be diagnosed with lung cancer, underscoring the need for strengthened anti-tobacco policies. Among the 10% of patients who develop lung cancer without a smoking history, the environmental or inherited causes of lung cancer are usually unclear. There is no validated screening method for lung cancer even in high-risk populations and the overall five-year survival has not changed significantly in the last 20 years. However, major progress has been made in the understanding of the disease and we are beginning to see this knowledge translated into the clinic. In this review, we will summarize the current state of knowledge regarding the cascade of events associated with lung cancer development. From subclinical DNA damage to overt invasive disease, the mechanisms leading to clinically and molecularly heterogeneous tumors are being unraveled. These lesions allow cells to escape the normal regulation of cell division, apoptosis and invasion. While all subtypes of non-small cell lung cancer have historically been treated the same, stage-for-stage, recent technological advances have allowed a better understanding of the molecular classification of the disease and provide hypotheses for molecular early detection and targeted therapeutic strategies

Thoracic Operations for Pulmonary Nodules Are Frequently Not Futile in Patients with Benign Disease

IntroductionPulmonary nodules often require operative resection to obtain a diagnosis. However, 10 to 30% of operations result in a benign diagnosis. Our purpose was to determine whether negative thoracic operations are futile by describing the pathological diagnoses; determining new diagnoses and treatment changes initiated based on operative findings; and assessing morbidity, mortality, and cost of the procedure.MethodsAt our academic medical center, 278 thoracic operations were performed for known or suspected cancer between January 1, 2005, and April 1, 2009. We collected and summarized data pertaining to preoperative patient and nodule characteristics, pathologic diagnosis, postoperative treatment changes resulting from surgical resection, perioperative morbidity and mortality, and hospital charges for patients with benign pathology.ResultsTwenty-three percent (65/278) of patients who underwent surgical resection for a suspicious nodule had benign pathology. We report granulomatous disease in 57%, benign tumors in 15%, fibrosis in 12%, and autoimmune and vascular diseases in 9%. Definitive diagnosis or treatment changes occurred in 85% of cases. Surgical intervention led to a new diagnosis in 69%, treatment course changes in 68% of benign cases, medication changes in 38%, new consultation in 31%, definitive treatment in 9%, and underlying disease management in 34%. There was no intraoperative, in-hospital, or 30-day mortality. Postoperative in-hospital events occurred in seven patients. The mean total cost was $25,515 with a mean cost per day of$7618.ConclusionsPatients with a benign diagnosis after surgical resection for a pulmonary nodule received a new diagnosis or had a treatment course change in 85% of the cases

Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections

The early identification of bacteremia is critical for ensuring appropriate treatment of nosocomial infections in intensive care unit (ICU) patients. The aim of this study was to use flow cytometric data of myeloid cells as a biomarker of bloodstream infection (BSI). An eight-color antibody panel was used to identify seven monocyte and two dendritic cell subsets. In the learning cohort, immunophenotyping was applied to (1) control subjects, (2) postoperative heart surgery patients, as a model of noninfectious inflammatory responses, and (3) blood culture-positive patients. Of the complex changes in the myeloid cell phenotype, a decrease in myeloid and plasmacytoid dendritic cell numbers, increase in CD14(+)CD16(+) inflammatory monocyte numbers, and upregulation of neutrophils CD64 and CD123 expression were prominent in BSI patients. An extreme gradient boosting (XGBoost) algorithm called the “infection detection and ranging score” (iDAR), ranging from 0 to 100, was developed to identify infection-specific changes in 101 phenotypic variables related to neutrophils, monocytes and dendritic cells. The tenfold cross-validation achieved an area under the receiver operating characteristic (AUROC) of 0.988 (95% CI 0.985–1) for the detection of bacteremic patients. In an out-of-sample, in-house validation, iDAR achieved an AUROC of 0.85 (95% CI 0.71–0.98) in differentiating localized from bloodstream infection and 0.95 (95% CI 0.89–1) in discriminating infected from noninfected ICU patients. In conclusion, a machine learning approach was used to translate the changes in myeloid cell phenotype in response to infection into a score that could identify bacteremia with high specificity in ICU patients

Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT

Background: Limited data are available regarding whether computer-aided diagnosis (CAD) improves assessment of malignancy risk in indeterminate pulmonary nodules (IPNs). Purpose: To evaluate the effect of an artificial intelligence-based CAD tool on clinician IPN diagnostic performance and agreement for both malignancy risk categories and management recommendations. Materials and Methods: This was a retrospective multireader multicase study performed in June and July 2020 on chest CT studies of IPNs. Readers used only CT imaging data and provided an estimate of malignancy risk and a management recommendation for each case without and with CAD. The effect of CAD on average reader diagnostic performance was assessed using the Obuchowski-Rockette and Dorfman-Berbaum-Metz method to calculate estimates of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Multirater Fleiss κ statistics were used to measure interobserver agreement for malignancy risk and management recommendations. Results: A total of 300 chest CT scans of IPNs with maximal diameters of 5-30 mm (50.0% malignant) were reviewed by 12 readers (six radiologists, six pulmonologists) (patient median age, 65 years; IQR, 59-71 years; 164 [55%] men). Readers\u27 average AUC improved from 0.82 to 0.89 with CAD (P \u3c .001). At malignancy risk thresholds of 5% and 65%, use of CAD improved average sensitivity from 94.1% to 97.9% (P = .01) and from 52.6% to 63.1% (P \u3c .001), respectively. Average reader specificity improved from 37.4% to 42.3% (P = .03) and from 87.3% to 89.9% (P = .05), respectively. Reader interobserver agreement improved with CAD for both the less than 5% (Fleiss κ, 0.50 vs 0.71; P \u3c .001) and more than 65% (Fleiss κ, 0.54 vs 0.71; P \u3c .001) malignancy risk categories. Overall reader interobserver agreement for management recommendation categories (no action, CT surveillance, diagnostic procedure) also improved with CAD (Fleiss κ, 0.44 vs 0.52; P = .001). Conclusion: Use of computer-aided diagnosis improved estimation of indeterminate pulmonary nodule malignancy risk on chest CT scans and improved interobserver agreement for both risk stratification and management recommendations