مورد إلكتروني
A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study
| العنوان: | A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study |
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| بيانات النشر: | KTH, Hälsoinformatik och logistik 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 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 Wiley 2022 |
| تفاصيل مُضافة: | Marzano, Luca Darwich, Adam S. Tendler, Salomon Dan, Asaf Lewensohn, Rolf De Petris, Luigi Raghothama, Jayanth Meijer, Sebastiaan |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | 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. QC 20221115 |
| مصطلحات الفهرس: | C reactive protein; carboplatin; cisplatin; etoposide; irinotecan; lactate dehydrogenase; platinum complex, Medical and Health Sciences, Medicin och hälsovetenskap, Cancer and Oncology, Cancer och onkologi, Article in journal, info:eu-repo/semantics/article, text |
| DOI: | 10.1111.cts.13371 |
| URL: | Clinical and Translational Science, 1752-8054, 2022, 15:10, s. 2437-2447 |
| الإتاحة: | Open access content. Open access content info:eu-repo/semantics/openAccess |
| ملاحظة: | application/pdf English |
| أرقام أخرى: | UPE oai:DiVA.org:kth-321440 0000-0002-3398-2296 0000-0001-8218-4306 0000-0002-3416-4535 0000-0003-1126-3781 doi:10.1111/cts.13371 PMID 35856401 ISI:000832654100001 Scopus 2-s2.0-85135121261 1372719721 |
| المصدر المساهم: | UPPSALA UNIV LIBR From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1372719721 |
| قاعدة البيانات: | OAIster |
| DOI: | 10.1111.cts.13371 |
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