Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal
Abstract BackgroundBlood tests used to identify patients at increased risk of undiagnosed cancer are commonly used in isolation, primarily by monitoring whether results fall outside the normal range. Some prediction models incorporate changes over repeated blood tests (or tren...
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| Language: | English |
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JMIR Publications
2025-06-01
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| Series: | JMIR Cancer |
| Online Access: | https://cancer.jmir.org/2025/1/e70275 |
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| author | Pradeep S Virdee Kiana K Collins Claire Friedemann Smith Xin Yang Sufen Zhu Nia Roberts Jason L Oke Clare Bankhead Rafael Perera FD Richard Hobbs Brian D Nicholson |
| author_facet | Pradeep S Virdee Kiana K Collins Claire Friedemann Smith Xin Yang Sufen Zhu Nia Roberts Jason L Oke Clare Bankhead Rafael Perera FD Richard Hobbs Brian D Nicholson |
| author_sort | Pradeep S Virdee |
| collection | DOAJ |
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Abstract
BackgroundBlood tests used to identify patients at increased risk of undiagnosed cancer are commonly used in isolation, primarily by monitoring whether results fall outside the normal range. Some prediction models incorporate changes over repeated blood tests (or trends) to improve individualized cancer risk identification, as relevant trends may be confined within the normal range.
ObjectiveOur aim was to critically appraise existing diagnostic prediction models incorporating blood test trends for the risk of cancer.
MethodsMEDLINE and EMBASE were searched until April 3, 2025 for diagnostic prediction model studies using blood test trends for cancer risk. Screening was performed by 4 reviewers. Data extraction for each article was performed by 2 reviewers independently. To critically appraise models, we narratively synthesized studies, including model building and validation strategies, model reporting, and the added value of blood test trends. We also reviewed the performance measures of each model, including discrimination and calibration. We performed a random-effects meta-analysis of the c-statistic for a trends-based prediction model if there were at least 3 studies validating the model. The risk of bias was assessed using the PROBAST (prediction model risk of bias assessment tool).
ResultsWe included 16 articles, with a total of 7 models developed and 14 external validation studies. In the 7 models derived, full blood count (FBC) trends were most commonly used (86%, n=7 models). Cancers modeled were colorectal (43%, n=3), gastro-intestinal (29%, n=2), nonsmall cell lung (14%, n=1), and pancreatic (14%, n=1). In total, 2 models used statistical logistic regression, 2 used joint modeling, and 1 each used XGBoost, decision trees, and random forests. The number of blood test trends included in the models ranged from 1 to 26. A total of 2 of 4 models were reported with the full set of coefficients needed to predict risk, with the remaining excluding at least one coefficient from their article or were not publicly accessible. The c-statistic ranged 0.69‐0.87 among validation studies. The ColonFlag model using trends in the FBC was commonly externally validated, with a pooled c-statistic=0.81 (95% CI 0.77-0.85; n=4 studies) for 6-month colorectal cancer risk. Models were often inadequately tested, with only one external validation study assessing model calibration. All 16 studies scored a low risk of bias regarding predictor and outcome details. All but one study scored a high risk of bias in the analysis domain, with most studies often removing patients with missing data from analysis or not adjusting the derived model for overfitting.
ConclusionsOur review highlights that blood test trends may inform further investigation for cancer. However, models were not available for most cancer sites, were rarely externally validated, and rarely assessed calibration when they were externally validated. |
| format | Article |
| id | doaj-art-ee6a820fafcb4d5280bf9a4f12f25dd9 |
| institution | Matheson Library |
| issn | 2369-1999 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Cancer |
| spelling | doaj-art-ee6a820fafcb4d5280bf9a4f12f25dd92025-07-04T20:12:19ZengJMIR PublicationsJMIR Cancer2369-19992025-06-0111e70275e7027510.2196/70275Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical AppraisalPradeep S Virdeehttp://orcid.org/0000-0002-3006-8730Kiana K Collinshttp://orcid.org/0000-0002-3736-6976Claire Friedemann Smithhttp://orcid.org/0000-0001-9934-5882Xin Yanghttp://orcid.org/0000-0001-7951-5055Sufen Zhuhttp://orcid.org/0009-0003-1298-3905Nia Robertshttp://orcid.org/0000-0002-1142-6440Jason L Okehttp://orcid.org/0000-0003-3467-6677Clare Bankheadhttp://orcid.org/0000-0003-1588-3849Rafael Pererahttp://orcid.org/0000-0003-2418-2091FD Richard Hobbshttp://orcid.org/0000-0001-7976-7172Brian D Nicholsonhttp://orcid.org/0000-0003-0661-7362 Abstract BackgroundBlood tests used to identify patients at increased risk of undiagnosed cancer are commonly used in isolation, primarily by monitoring whether results fall outside the normal range. Some prediction models incorporate changes over repeated blood tests (or trends) to improve individualized cancer risk identification, as relevant trends may be confined within the normal range. ObjectiveOur aim was to critically appraise existing diagnostic prediction models incorporating blood test trends for the risk of cancer. MethodsMEDLINE and EMBASE were searched until April 3, 2025 for diagnostic prediction model studies using blood test trends for cancer risk. Screening was performed by 4 reviewers. Data extraction for each article was performed by 2 reviewers independently. To critically appraise models, we narratively synthesized studies, including model building and validation strategies, model reporting, and the added value of blood test trends. We also reviewed the performance measures of each model, including discrimination and calibration. We performed a random-effects meta-analysis of the c-statistic for a trends-based prediction model if there were at least 3 studies validating the model. The risk of bias was assessed using the PROBAST (prediction model risk of bias assessment tool). ResultsWe included 16 articles, with a total of 7 models developed and 14 external validation studies. In the 7 models derived, full blood count (FBC) trends were most commonly used (86%, n=7 models). Cancers modeled were colorectal (43%, n=3), gastro-intestinal (29%, n=2), nonsmall cell lung (14%, n=1), and pancreatic (14%, n=1). In total, 2 models used statistical logistic regression, 2 used joint modeling, and 1 each used XGBoost, decision trees, and random forests. The number of blood test trends included in the models ranged from 1 to 26. A total of 2 of 4 models were reported with the full set of coefficients needed to predict risk, with the remaining excluding at least one coefficient from their article or were not publicly accessible. The c-statistic ranged 0.69‐0.87 among validation studies. The ColonFlag model using trends in the FBC was commonly externally validated, with a pooled c-statistic=0.81 (95% CI 0.77-0.85; n=4 studies) for 6-month colorectal cancer risk. Models were often inadequately tested, with only one external validation study assessing model calibration. All 16 studies scored a low risk of bias regarding predictor and outcome details. All but one study scored a high risk of bias in the analysis domain, with most studies often removing patients with missing data from analysis or not adjusting the derived model for overfitting. ConclusionsOur review highlights that blood test trends may inform further investigation for cancer. However, models were not available for most cancer sites, were rarely externally validated, and rarely assessed calibration when they were externally validated.https://cancer.jmir.org/2025/1/e70275 |
| spellingShingle | Pradeep S Virdee Kiana K Collins Claire Friedemann Smith Xin Yang Sufen Zhu Nia Roberts Jason L Oke Clare Bankhead Rafael Perera FD Richard Hobbs Brian D Nicholson Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal JMIR Cancer |
| title | Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal |
| title_full | Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal |
| title_fullStr | Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal |
| title_full_unstemmed | Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal |
| title_short | Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal |
| title_sort | clinical prediction models incorporating blood test trend for cancer detection systematic review meta analysis and critical appraisal |
| url | https://cancer.jmir.org/2025/1/e70275 |
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