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A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0002-3398-2296
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0001-8218-4306
Department of Oncology‐Pathology Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital Stockholm Sweden.
Department of Oncology‐Pathology Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital Stockholm Sweden.
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2022 (English)In: Clinical and Translational Science, ISSN 1752-8054, E-ISSN 1752-8062, Vol. 15, no 10, p. 2437-2447Article in journal (Refereed) Published
Abstract [en]

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans’ Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. 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-word 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 (n = 636) with stage IIIA–IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance 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.

Place, publisher, year, edition, pages
Wiley , 2022. Vol. 15, no 10, p. 2437-2447
Keywords [en]
C reactive protein; carboplatin; cisplatin; etoposide; irinotecan; lactate dehydrogenase; platinum complex
National Category
Medical and Health Sciences Cancer and Oncology
Identifiers
URN: urn:nbn:se:kth:diva-321440DOI: 10.1111/cts.13371ISI: 000832654100001PubMedID: 35856401Scopus ID: 2-s2.0-85135121261OAI: oai:DiVA.org:kth-321440DiVA, id: diva2:1710821
Funder
Swedish Cancer Society, CAN2021/1469 Pj01Swedish Cancer Society, CAN 2018/597KTH Royal Institute of Technology
Note

QC 20221115

Available from: 2022-11-14 Created: 2022-11-14 Last updated: 2022-11-16Bibliographically approved

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Marzano, LucaDarwich, Adam S.Raghothama, JayanthMeijer, Sebastiaan

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