2026/05/05 by Jonas Willmann, Edward Christopher Dee, Lizza E.L. Hendriks +20 · 1 voice
Medicine · Biochemistry, Genetics and Molecular Biology · #Radiomics and Machine Learning in Medical Imaging #Lung Cancer Diagnosis and Treatment #Cancer Genomics and Diagnostics
paper · doi:10.1200/jco-25-01958
openalex publication_date 2026/05/05 · openalex created_date 2026/05/06 · openalex updated_date 2026/07/27
Advances in systemic therapy have improved outcomes for metastatic non–small cell lung cancer (NSCLC), yet resistance and progression remain nearly universal. Local therapies such as radiotherapy, surgery, and image-guided ablation can extend disease control in selected patients, but existing classifications—including dynamic models of oligometastatic disease—assign a single state per patient and do not capture lesion-level heterogeneity. We introduce the concept of metastatic trajectories —the spatiotemporal dynamics of response and progression across lesions, organs, and patients—as a framework to characterize intrapatient heterogeneity and inform adaptive treatment strategies. Dimensions of metastatic trajectories include the magnitude and homogeneity of response, mechanisms of resistance, organotropism, and the pattern, site, extent, and pace of progression. This framework shifts the focus from overall disease states to individual lesion behavior over time, enabling reactive strategies based on observed trajectories and anticipatory strategies based on predicted ones. We review the biological foundations of intrapatient heterogeneity—including genomic diversification, nongenetic plasticity, and tumor-microenvironmental adaptation—that drive divergent lesion evolution and treatment response. Emerging biomarkers such as circulating tumor DNA and radiomic signatures, together with integrative genomic and functional imaging approaches, may allow tracking and prediction of trajectory evolution. Standardized reporting parameters are proposed to ensure consistent documentation and facilitate validation across studies. Integrating trajectory-based assessment into clinical practice could refine patient selection for local and systemic therapy, enable biology-informed adaptation of treatment timing and intensity, and provide a foundation for next-generation clinical trials aimed at precision management of metastatic NSCLC.