Small cell lung cancer (SCLC) is an aggressive malignancy with poor prognosis. No validated prognostic score has been established to guide clinical decisions in the extensive stage (ES). This narrative review critically examines the evolution of prognostic models in SCLC. We aim to highlight current gaps and propose directions for the development of clinically actionable tools. We conducted a comprehensive review of the literature on SCLC prognostic models, focusing on historical context, model design, variables used, validation methods, and real-world applicability. Comparative strengths and limitations were analysed across different model types. We analysed early scoring systems, modern nomograms, inflammation-based and nutritional scores, as well as integrative models. Historical tools are often limited to disease stage, performance status, basic laboratory values, most lack external validation, are retrospective, or were developed on chemotherapy-only cohorts. Recent models incorporate broader clinical data and, in some cases, nomograms or web-based calculators. Yet, few have undergone external validation or demonstrated utility in diverse clinical settings. The absence of dynamic, personalized models prevents integration into contemporary practice. Although numerous prognostic tools have been proposed, a reliable, validated tool is still lacking. Future prognostic models must move beyond static clinical parameters. Incorporating molecular biomarkers, real-world data, and machine learning could enable the development of validated, adaptive tools with true clinical relevance. Collaborative, prospective efforts will be critical to achieve this goal.
Cantale et al. (Thu,) studied this question.