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Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real\u2010word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (<jats:italic>n<\/jats:italic>\u00a0=\u00a0636) with stage IIIA\u2013IVB according to TNM classification. The analysis yielded <jats:italic>k<\/jats:italic>\u00a0=\u00a07 compacted and well\u2010separated clusters of patients. Performance status (Eastern Cooperative Oncology Group\u2010Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.<\/jats:p>","DOI":"10.1111\/cts.13371","type":"journal-article","created":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T10:33:31Z","timestamp":1658313211000},"page":"2437-2447","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A novel analytical framework for risk stratification of real\u2010world data using machine learning: A small cell lung cancer study"],"prefix":"10.1111","volume":"15","author":[{"given":"Luca","family":"Marzano","sequence":"first","affiliation":[{"name":"Division of Health Informatics and Logistics School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of Technology  Huddinge Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam 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