Current lung cancer screening guidelines in the United States fail to identify many individuals at risk of developing the disease. Additionally, existing healthcare infrastructure has been leveraged to establish IPN clinics, a promising approach to addressing the limitations of current screening guidelines. Early-stage lung cancer is frequently diagnosed because of the incidental detection of pulmonary nodules on clinically indicated chest CT scans, particularly in the absence of formal screening programs. While artificial intelligence (AI) systems for lung cancer detection have demonstrated significant advancements in medicine, their clinical validation in screening settings remains limited. This review will discuss the pivotal trials underpinning the United States Preventive Services Task Force (USPSTF) recommendations for lung cancer screening, which have shaped the current guidelines for at-risk populations. We will explore recent studies investigating the role of AI in enhancing lung cancer screening efforts, highlighting how AI has the potential to improve early detection, streamline workflows, and reduce false positives and negatives in screening processes. This review will present the lung cancer screening rates at our institution, with a specific focus on the validation and integration of AI-driven technologies into our established screening programs. Using AI algorithms, we have validated enhanced detection capabilities through retrospective analysis of historical patient data, demonstrating significant improvements in identifying high-risk individuals and early-stage malignancies. These AI models, validated through rigorous cross-validation methods and clinical trials, have proven to outperform traditional screening approaches in sensitivity and specificity. The integration of these AI technologies within the lung cancer screening framework not only optimizes existing programs but also expands access to screening, improving early detection rates and ultimately leading to better patient outcomes. Through continuous validation and refinement, we aim to solidify AI’s role in transforming lung cancer detection and patient care. Through ongoing validation and implementation, AI can play a crucial role in transforming lung cancer screening practices, ultimately contributing to earlier diagnosis and improved patient survival.

The proto-oncogene Rearranged During Transfection (RET) encodes a receptor tyrosine kinase that is essential for neural, renal, and thyroid development. Pathogenic RET alterations, including mutations and fusions, drive oncogenesis, most notably medullary and papillary thyroid carcinomas and non-small cell lung cancer, by constitutively activating downstream RAS–MAPK, PI3K–AKT, and JAK–STAT signaling. Early multi-kinase inhibitors such as vandetanib and cabozantinib demonstrated modest efficacy with significant toxicity, whereas the selective RET inhibitors selpercatinib and pralsetinib have achieved improved response rates and tolerability. However, resistance remains a key clinical challenge, arising from secondary RET mutations and bypass signaling via MET or EGFR pathways. Continued investigation into next-generation inhibitors and rational combination therapies aims to overcome resistance and optimize treatment sequencing, advancing precision oncology for RET-altered malignancies. Nonetheless, resistance, driven by secondary mutations and bypass signaling, presents a major therapeutic challenge. Ongoing development of next-generation inhibitors and combination strategies aims to overcome resistance and improve patient outcomes.

The treatment landscape of oncogene-driven non-small-cell lung cancer (NSCLC) has evolved substantially with the development of targeted therapies directed against actionable molecular alterations. Tyrosine kinase inhibitors (TKIs) remain the cornerstone of treatment for many driver-defined subsets; however, acquired resistance, central nervous system progression, and tumor heterogeneity continue to limit long-term disease control. This review examines the mechanistic foundations, clinical evidence, resistance patterns, and emerging therapeutic roles of TKIs, antibody–drug conjugates (ADCs), and bispecific antibodies (bsAbs) in oncogene-driven NSCLC. Relevant preclinical studies, clinical trials, and recent therapeutic advances across major actionable driver alterations were reviewed and compared. TKIs provide potent and selective inhibition of oncogenic signaling and remain the preferred frontline therapy in most molecular subgroups, whereas ADCs offer targeted payload delivery that may overcome diverse resistance mechanisms, and bsAbs provide dual-target blockade and immune-mediated antitumor activity. Emerging evidence supports the expanding role of ADCs and bsAbs in post-TKI settings and selected biomarker-defined populations. Resistance mechanisms differ across therapeutic classes and include secondary target alterations, bypass pathway activation, antigen loss, payload resistance, and receptor adaptation. Collectively, these modalities are increasingly being integrated into biomarker-guided treatment strategies, with future management likely to rely on rational sequencing and combination approaches tailored to resistance mechanisms, target expression, central nervous system involvement, and tumor heterogeneity.

There was an error in the original publication