ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2014
Rapid learning in practice: a lung cancer survival decision support system in routine patient care data.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 51 citations in OpenAlex.
- Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting.Oncotarget · 2016Trial
- Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication.Scientific reports · 2025Article
- Predicting 2-year survival in stage I-III non-small cell lung cancer: the development and validation of a scoring system from an Australian cohort.Radiation oncology (London, England) · 2022Article
- Narrative review of artificial intelligence in diabetic macular edema: Diagnosis and predicting treatment response using optical coherence tomography.Indian journal of ophthalmology · 2021Review
- Machine learning applications in radiation oncology.Physics and imaging in radiation oncology · 2021Review
- Decision Support Systems in Prostate Cancer Treatment: An Overview.BioMed research international · 2019Review
- Machine learning and modeling: Data, validation, communication challenges.Medical physics · 2018Article
- Regional process redesign of lung cancer care: a learning health system pilot project.Current oncology (Toronto, Ont.) · 2018Article
- The effect of imputing missing clinical attribute values on training lung cancer survival prediction model performance.Health information science and systems · 2017Article
- Radiomics: the bridge between medical imaging and personalized medicine.Nature reviews. Clinical oncology · 2017Review
- Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.Clinical and translational radiation oncology · 2017Article
- Person centered prediction of survival in population based screening program by an intelligent clinical decision support system.Gastroenterology and hepatology from bed to bench · 2017Article
- Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical data.Journal of biomedical informatics · 2016Article
- Personalized radiotherapy: concepts, biomarkers and trial design.The British journal of radiology · 2015Review
- MicroRNA-148b is down-regulated in non-small cell lung cancer and associated with poor survival.International journal of clinical and experimental pathology · 2015Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
background and purposeA rapid learning approach has been proposed to extract and apply knowledge from routine care data rather than solely relying on clinical trial evidence. To validate this in practice we deployed a previously developed decision support system (DSS) in a typical, busy clinic for non-small cell lung cancer (NSCLC) patients. MATERIAL AND
methodsGender, age, performance status, lung function, lymph node status, tumor volume and survival were extracted without review from clinical data sources for lung cancer patients. With these data the DSS was tested to predict overall survival.
results3919 lung cancer patients were identified with 159 eligible for inclusion, due to ineligible histology or stage, non-radical dose, missing tumor volume or survival. The DSS successfully identified a good prognosis group and a medium/poor prognosis group (2 year OS 69% vs. 27/30%, p<0.001). Stage was less discriminatory (2 year OS 47% for stage I-II vs. 36% for stage IIIA-IIIB, p=0.12) with most good prognosis patients having higher stage disease. The DSS predicted a large absolute overall survival benefit (∼40%) for a radical dose compared to a non-radical dose in patients with a good prognosis, while no survival benefit of radical radiotherapy was predicted for patients with a poor prognosis.
conclusionsA rapid learning environment is possible with the quality of clinical data sufficient to validate a DSS. It uses patient and tumor features to identify prognostic groups in whom therapy can be individualized based on predicted outcomes. Especially the survival benefit of a radical versus non-radical dose predicted by the DSS for various prognostic groups has clinical relevance, but needs to be prospectively validated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.