Virdee Pradeep S, Collins Kiana K, Smith Claire Friedemann, Yang Xin, Zhu Sufen, Roberts Nia, Oke Jason L, Bankhead Clare, Perera Rafael, Hobbs Fd Richard, Nicholson Brian D
Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 1865617855.
St Edmund Hall, University of Oxford, Oxford, United Kingdom.
JMIR Cancer. 2025 Jun 27;11:e70275. doi: 10.2196/70275.
Blood 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.
Our aim was to critically appraise existing diagnostic prediction models incorporating blood test trends for the risk of cancer.
MEDLINE 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).
We 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.
Our 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.
用于识别未确诊癌症风险增加患者的血液检测通常单独使用，主要是通过监测结果是否超出正常范围。一些预测模型纳入了多次血液检测的变化（或趋势）以改善个体癌症风险识别，因为相关趋势可能局限于正常范围内。
我们的目的是严格评估现有的纳入血液检测趋势以评估癌症风险的诊断预测模型。
检索MEDLINE和EMBASE直至2025年4月3日，查找使用血液检测趋势评估癌症风险的诊断预测模型研究。由4名评审员进行筛选。每篇文章的数据提取由2名评审员独立进行。为了严格评估模型，我们对研究进行了叙述性综合，包括模型构建和验证策略、模型报告以及血液检测趋势的附加价值。我们还审查了每个模型的性能指标，包括区分度和校准度。如果至少有3项研究验证了基于趋势的预测模型，我们对其c统计量进行随机效应荟萃分析。使用PROBAST（预测模型偏倚风险评估工具）评估偏倚风险。
我们纳入了16篇文章，共开发了7个模型和14项外部验证研究。在推导的7个模型中，全血细胞计数（FBC）趋势最常被使用（86%，n = 7个模型）。建模的癌症包括结直肠癌（43%，n = 3）、胃肠道癌（29%，n = 2）、非小细胞肺癌（14%，n = 1）和胰腺癌（14%，n = 1）。总共有2个模型使用统计逻辑回归，2个使用联合建模，1个分别使用XGBoost、决策树和随机森林。模型中纳入的血液检测趋势数量从1到26不等。4个模型中有2个报告了预测风险所需的全套系数，其余模型在其文章中至少排除了一个系数或无法公开获取。在验证研究中，c统计量范围为0.69 - 0.87。使用FBC趋势的ColonFlag模型经常进行外部验证，对于6个月结直肠癌风险，汇总c统计量 = 0.81（95% CI 0.77 - 0.85；n = 4项研究）。模型通常测试不充分，只有一项外部验证研究评估了模型校准。所有16项研究在预测因素和结果细节方面的偏倚风险得分较低。除一项研究外，所有研究在分析领域的偏倚风险得分较高，大多数研究经常在分析中剔除数据缺失的患者或未对推导模型进行过拟合调整。
我们的综述强调血液检测趋势可能为癌症的进一步调查提供信息。然而，大多数癌症部位没有可用的模型，很少进行外部验证，并且在进行外部验证时很少评估校准度。
