Predicting survival in metastatic non‐small cell lung cancer patients with poor ECOG‐PS: A single‐arm prospective study

• Published in: Cancer medicine (Malden, MA) MA), 2023-02, Vol.12 (4), p.5099-5109
• Main Authors: Cunha, Mateus Trinconi, Souza Borges, Ana Paula, Carvalho Jardim, Vinicius, Fujita, André, Castro, Gilberto
• Format: Article
• Language: English
• Subjects: Body fat; Cachexia; Carcinoma, Non-Small-Cell Lung - pathology; Classification; Datasets; Discriminant analysis; Humans; Laboratories; Learning algorithms; Life span; Lung cancer; Lung Neoplasms - pathology; Machine learning; Metastases; Metastasis; Methods; Missing data; network analysis; Non-small cell lung carcinoma; non‐small cell lung cancer; Patients; Performance evaluation; poor performance status; prognostic markers; Prospective Studies; Quality of Life; Sampling techniques; Small cell lung carcinoma; Survival analysis
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.5254

Background Patients with advanced non‐small cell lung cancer (NSCLC) are a heterogeneous population with short lifespan. We aimed to develop methods to better differentiate patients whose survival was >90 days. Methods We evaluated 83 characteristics of 106 treatment‐naïve, stage IV NSCLC patients with Eastern Cooperative Oncology Group Performance Status (ECOG‐PS) >1. Automated machine learning was used to select a model and optimize hyperparameters. 100‐fold bootstrapping was performed for dimensionality reduction for a second (“lite”) model. Performance was measured by C‐statistic and accuracy metrics in an out‐of‐sample validation cohort. The “lite” model was validated on a second independent, prospective cohort (N = 42). Network analysis (NA) was performed to evaluate the differences in centrality and connectivity of features. Results The selected method was ExtraTrees Classifier, with C‐statistic of 0.82 (p