Small cell transformation in EGFR-mutated non-small cell lung cancer: DLL3 expression and efficacy of immune checkpoint inhibitors or tyrosine kinase inhibitors combined with chemotherapy.

Introduction: Small cell transformation (SCT) is a typical mechanism of adaptive resistance to third generation epidermal growth factor receptor inhibitors (EGFRi) which have become the standard of care for EGFR-driven non-small cell lung cancer (EGFR+ NSCLC). Little is known about the optimal management of SCT patients. This study aimed to compare outcomes under platinum/etoposide chemotherapy alone (chemo) or in combination with EGFR inhibitors (EGFRi+chemo) or immune checkpoint inhibitors (ICI+chemo).

Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients.

This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its application. We emphasize feature engineering and visualization techniques, demonstrating their efficacy in capturing temporal relationships. Applied to an EGFR lung cancer cohort, our approach showcases reliable temporal insights even in a small dataset.