Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.

Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.

The continually evolving landscape of novel therapies in oncogene-driven advanced non-small-cell lung cancer.

Non-small-cell lung cancer (NSCLC) is a highly heterogeneous disease that is frequently associated with a host of known oncogenic alterations. Advances in molecular diagnostics and drug development have facilitated the targeting of novel alterations such that the majority of NSCLC patients have driver mutations that are now clinically actionable. The goal of this review is to gain insights into clinical research and development principles by summary, analysis, and discussion of data on agents targeting known alterations in oncogene-driven, advanced NSCLC beyond those in the and the .

Analysis of outcomes in resected early-stage NSCLC with rare targetable driver mutations.

Background: Given advancements in adjuvant treatments for non-small-cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK)-targeted therapies, it is important to consider postoperative targeted therapies for other early-stage oncogene-addicted NSCLC. Exploring baseline outcomes for early-stage NSCLC with these rare mutations is crucial.

Objectives: This study aims to assess relapse-free survival (RFS) and overall survival (OS) in patients with resected early-stage NSCLC with rare targetable driver mutations.