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Background: Lung nodule incidence is increasing. Many nodules require biopsy to discriminate between benign and malignant causes. The gold standard for minimally invasive biopsy, CT scan-guided transthoracic needle biopsy (CT-TTNB), has not been compared directly with navigational bronchoscopy, a method that recently has seen rapid technological innovation and is associated with improving diagnostic yield and lower complication rates. Current estimates of the diagnostic usefulness of both methods are based largely on noncomparative data with significant risk for selection, referral, and publication biases. Research Question: Is contemporary navigational bronchoscopy noninferior to CT scan-guided transthoracic needle biopsy for the diagnosis of indeterminate pulmonary nodules? Study Design and Methods: Navigation Endoscopy to Reach Indeterminate Lung Nodules vs Transthoracic Needle Aspiration, a Randomized Controlled Study, is a multicenter, 1:1 randomized, parallel-group trial designed to ascertain whether electromagnetic navigational bronchoscopy with integrated digital tomosynthesis is noninferior to CT-TTNB for the diagnosis of peripheral lung nodules 10 to 30 mm in diameter with before testing probability of malignancy of at least 10%. The primary end point is diagnostic accuracy through 12 months of follow-up. Secondary end points include diagnostic yield, complication rate, procedure duration, need for additional invasive diagnostic procedures, and radiation exposure. Results: This article describes the protocol and rationale for the Navigation Endoscopy to Reach Indeterminate Lung Nodules vs Transthoracic Needle Aspiration, a Randomized Controlled Study, designed to answer the study question. Interpretation: The results of this rigorously designed trial will provide high-quality data regarding the management of lung nodules, a common clinical entity that often represents the earliest and most treatable stage of lung cancer. Several design challenges are described. Notably, all nodules are reviewed centrally by an independent interventional pulmonology and radiology adjudication panel relying on prespecified exclusions to ensure enrolled nodules are amenable to sampling by both methods while protecting against selection bias favoring either method. Conservative diagnostic yield and accuracy definitions with prespecified criteria for what nonmalignant findings may be considered diagnostic were chosen to avoid inflation of estimates of diagnostic usefulness. Trial Registry: ClinicalTrials.gov; No.: NCT04250194; URL: www.clinicaltrials.gov

Objective: To investigate whether an artificial intelligence (AI)–based model can predict tumor invasiveness in patients with multifocal lung adenocarcinoma (MFLA). Patients and Methods: Patients with MFLA who underwent surgical resection were enrolled to a prospective registry trial (NCT01946100). Each identified nodule underwent retrospective computer-aided nodule assessment and risk yield (CANARY)–based AI to determine a quantitative degree of invasiveness. Data regarding age, sex, medical and surgical management, and survival were collected and analyzed. Pathologic review was performed by a pulmonary pathologist with comprehensive histologic subtyping. Results: From January 1, 2013, through December 31, 2018, 68 patients with MFLA underwent at least 1 surgical resection. Five-year survival for the cohort was 91%, and 10-year survival was 73.6%. No significant differences in survival were observed when separated by sex, number, or size of the nodules. A 10-year survival trend was seen when comparing patients with unilateral (100% survival) vs bilateral disease (66%). Retrospective CANARY-based AI analysis demonstrated that the majority of the nodules present at the time of diagnosis (229/302; 75.8%) were classified good, with an average score of 0.19, suggesting indolent clinical behavior and noninvasive pathology. However, AI-CANARY scores of the surgically removed nodules were significantly higher compared with those of the nonresected nodules (P=.001). Conclusion: The long-term survival for patients with N0, M0 MFLA who have undergone surgical resection may approach those of stage I non–small cell lung cancer. CANARY-based AI has the potential to stratify individual nodules to help guide surgical intervention versus observation of nodules. Trial Registration: clinicaltrials.gov Identifier: NCT01946100

Introduction: Lung cancer is the deadliest cancer in the United States and worldwide, and lung adenocarcinoma (LUAD) is the most prevalent histologic subtype in the United States. LUAD exhibits a wide range of aggressiveness and risk of recurrence, but the biological underpinnings of this behavior are poorly understood. Past studies have focused on the biological characteristics of the tumor itself, but the ability of the immune response to contain tumor growth represents an alternative or complementary hypothesis. Emerging technologies enable us to investigate the spatial distribution of specific cell types within the tumor nest and characterize this immune response. This study aimed to investigate the association between immune cell density within the primary tumor and recurrence-free survival (RFS) in stage I and II LUAD. Methods: This study is a prospective collection with retrospective evaluation. A total of 100 patients with surgically resected LUAD and at least 5-year follow-ups, including 69 stage I and 31 stages II tumors, were enrolled. Multiplexed immunohistochemistry panels for immune markers were used for measurement. Results: Cox regression models adjusted for sex and EGFR mutation status revealed that the risk of recurrence was reduced by 50% for the unit of one interquartile range (IQR) change in the tumoral T-cell (adjusted hazard ratio per IQR increase = 0.50, 95% confidence interval: 0.27–0.93) and decreased by 64% in mast cell density (adjusted hazard ratio per IQR increase = 0.36, confidence interval: 0.15–0.84). The analyses were reported without the type I error correction for the multiple types of immune cell testing. Conclusions: Analysis of the density of immune cells within the tumor and surrounding stroma reveals an association between the density of T-cells and RFS and between mast cells and RFS in early-stage LUAD. This preliminary result is a limited study with a small sample size and a lack of an independent validation set.