ArticleMedical care2017
Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.
Article in Medical care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data.Journal of the National Cancer Institute · 2020Pooled it
- Rule-Based Algorithm to Identify Recurrent Non-Hodgkin Lymphoma in Electronic Health Data.JCO clinical cancer informatics · 2026Article
- Risk of late recurrence of colorectal and breast cancer among older long-term cancer survivors.JNCI cancer spectrum · 2026Article
- Article
- Multimodal Integration in Health Care: Development With Applications in Disease Management.Journal of medical Internet research · 2025Review
- Using Electronic Health Records to Classify Cancer Site and Metastasis.Applied clinical informatics · 2025Article
- Patterns of recurrence among adults diagnosed with screen-detected lung cancer.Cancer epidemiology · 2025Article
- Refined Algorithm for Identifying Recurrence Among Patients with Non-Metastatic Colorectal Cancer Based on Danish National Health Data Registries.Clinical epidemiology · 2025Article
- Racial comparisons in treatment of rectal adenocarcinoma and survival in the military health system.JNCI cancer spectrum · 2024Article
- Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data.Journal of biomedical informatics · 2024Article
- Association of platinum-based chemotherapy with live birth and infertility in female survivors of adolescent and young adult cancer.Fertility and sterility · 2024Article
- Evaluation of machine learning methods for the retrospective detection of ovarian cancer recurrences from chemotherapy data.ESMO real world data and digital oncology · 2024Article
- Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records.JCO clinical cancer informatics · 2024Article
- LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.Patterns (New York, N.Y.) · 2024Article
- Estimated incidence of disruptions to event-free survival from non-metastatic cancers in New South Wales, Australia - a population-wide epidemiological study of linked cancer registry and treatment data.Frontiers in oncology · 2024Article
- Overall Survival Among Patients With De Novo Stage IV Metastatic and Distant Metastatic Recurrent Non-Small Cell Lung Cancer.JAMA network open · 2023Article
- Using Machine Learning to Leverage Biomarker Change and Predict Colorectal Cancer Recurrence.JCO clinical cancer informatics · 2023Article
- Semisupervised Calibration of Risk with Noisy Event Times (SCORNET) using electronic health record data.Biostatistics (Oxford, England) · 2023Article
- Racial and Ethnic Bias in Risk Prediction Models for Colorectal Cancer Recurrence When Race and Ethnicity Are Omitted as Predictors.JAMA network open · 2023Article
- Racial Disparities in the Ascertainment of Cancer Recurrence in Electronic Health Records.JCO clinical cancer informatics · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
introductionRecurrent cancer is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and tumor registries contain vast amounts of data regarding community-based patients, but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations.
methodsWe developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and colorectal cancer using 2 data sources that contain a widely available type of structured data (claims or electronic health record encounters) linked to gold-standard recurrence status: Medicare claims linked to the Cancer Care Outcomes Research and Surveillance study, and the Cancer Research Network Virtual Data Warehouse linked to registry data. Twelve potential indicators of recurrence were used to develop separate models for each cancer in each data source. Detection models maximized area under the ROC curve (AUC); timing models minimized average absolute error. Algorithms were compared by cancer type/data source, and contrasted with an existing binary detection rule.
resultsDetection model AUCs (>0.92) exceeded existing prediction rules. Timing models yielded absolute prediction errors that were small relative to follow-up time (<15%). Similar covariates were included in all detection and timing algorithms, though differences by cancer type and dataset challenged efforts to create 1 common algorithm for all scenarios.
conclusionsValid and reliable detection of recurrence using big data is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for lung and colorectal cancer patients and those who develop recurrence.
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