Optimizing Biomarker Models for Biologically Heterogeneous Cancers: A Nested Model Approach for Lung Cancer.

Background: The heterogeneous biology of cancer subtypes, especially in lung cancer, poses significant challenges for biomarker development. Standard model building techniques often fall short in accurately incorporating various histologic subtypes because of their diverse biological characteristics. This study explores a nested biomarker model to address this issue, aiming to improve lung cancer early detection.

Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules.

Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bronchoscopically biopsied pulmonary nodules. Materials and Methods This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose CT, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. The area under the receiver operating characteristic curve of eight validated models, including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence (AI) on chest CT scans, longitudinal imaging AI, and multimodal approaches for prediction of lung cancer risk was assessed in nine cohorts ( = 898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions.

Leveraging Artificial Intelligence as a Safety Net for Incidentally Identified Lung Nodules at a Tertiary Center.

Background: Artificial intelligence (AI)-powered platforms may be used to ensure that clinically significant lung nodules receive appropriate management. We studied the impact of a commercially available AI natural language processing tool on detection of clinically significant indeterminate pulmonary nodules (IPNs) based on radiology reports and provision of guideline-consistent care.

Study Design: All computed tomography (CT) scans performed at a single tertiary care center in the outpatient or emergency room setting between 20-Feb-2024 and 20-March-2024 were processed by the AI natural language processing algorithm.

Outcomes of a novel double-stapled anastomotic technique in esophagectomy.

The double-stapled () anastomotic technique associates with lower odds of anastomotic leak and stricture. , Single posterior stapled.

Potential for trans-pulmonary tumor markers in the early diagnosis of lung cancer: a case report.

Background: Measurement of tumor markers from peripheral venous blood is an emerging tool to assist in the early diagnosis of lung cancer. Samples from the pulmonary artery and pulmonary artery wedge position (trans-pulmonary samples) are accessible via right-heart catheterization and, by virtue of their proximity to lung tumors, may increase diagnostic yield.

Case Presentation: We report a case of a 64 year-old woman from whom trans-pulmonary samples were obtained and who was diagnosed 16 months later with recurrent metastatic small cell lung cancer.