Purpose: We conducted a phase II study to assess the efficacy of continuous dosing of sunitinib in patients with flurodeoxyglucose positron emission tomography (FDG-PET)-avid, iodine-refractory well-differentiated thyroid carcinoma (WDTC) and medullary thyroid cancer (MTC) and to assess for early response per FDG-PET.Experimental Design: Patients had metastatic, iodine-refractory WDTC or MTC with FDG-PET-avid disease. Sunitinib was administered at 37.5 mg daily on a continuous basis. The primary end point was response rate per Response Evaluation Criteria in Solid Tumors (RECIST). Secondary end points included toxicity, overall survival, and time to progression. We conducted an exploratory analysis of FDG-PET response after 7 days of treatment.Results: Thirty-five patients were enrolled (7 MTC, 28 WDTC), and 33 patients were evaluable for disease response. The primary end point, objective response rate per RECIST, was 11 patients (31%; 95% confidence interval, 16-

No abstract

The degree of COPD severity, including airflow obstruction, visual emphysema, and respiratory exacerbations, was independently predictive of lung cancer. These risk factors should be further studied as inclusion and exclusion criteria for the survival benefit of lung cancer screening. Studies are needed to determine if reduction in respiratory exacerbations among smokers can reduce the risk of lung cancer.

Purpose: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. Methods: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). Results: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures. Conclusions: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

No abstract

No abstract

No abstract

The treatment of advanced non-small cell lung cancer has been with systemic chemotherapy and usually consists of a platinum doublet chemotherapy. The identification of somatic driver mutations has resulted in new drugs that target these mutations. This report discusses the two most important new targeted therapy drugs for the treatment of advanced non-small cell lung cancer that have these driver mutations.