Background

Fecal Immunochemical Test (FIT) is widely used in population-based screening for colorectal cancer (CRC). This had led to major challenges regarding colonoscopy capacity. Methods to maintain high sensitivity without compromising the colonoscopy capacity are needed. This study investigates an algorithm that combines FIT result, blood-based biomarkers associated with CRC, and individual demographics, to triage subjects sent for colonoscopy among a FIT positive (FIT⁺) screening population and thereby reduce the colonoscopy burden.

Materials and methods

From the Danish National Colorectal Cancer Screening Program, 4048 FIT⁺ (≥100 ng/mL Hemoglobin) subjects were included and analyzed for a panel of 9 cancer-associated biomarkers using the ARCHITECT i2000. Two algorithms were developed: 1) a predefined algorithm based on clinically available biomarkers: FIT, age, CEA, hsCRP and Ferritin; and 2) an exploratory algorithm adding additional biomarkers: TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, B2M and sex to the predefined algorithm. The diagnostic performances for discriminating subjects with or without CRC in the 2 models were benchmarked against the FIT alone using logistic regression modeling.

Results

The discrimination of CRC showed an area under the curve (AUC) of 73.7 (70.5-76.9) for the predefined model, 75.3 (72.1-78.4) for the exploratory model, and 68.9 (65.5-72.2) for FIT alone. Both models performed significantly better (P < .001) than the FIT model. The models were benchmarked vs. FIT at cutoffs of 100, 200, 300, 400, and 500 ng/mL Hemoglobin using corresponding numbers of true positives and false positives. All performance metrics were improved at all cutoffs.

Conclusion

A screening algorithm including a combination of FIT result, blood-based biomarkers and demographics outperforms FIT in discriminating subjects with or without CRC in a screening population with FIT results above 100 ng/mL Hemoglobin.

中文翻译：

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优化结直肠癌筛查：结合粪便免疫化学测试、血液癌症相关蛋白和人口统计学的算法，以减少结肠镜检查负担

背景

粪便免疫化学检测（FIT）广泛应用于基于人群的结直肠癌（CRC）筛查。这给结肠镜检查能力带来了重大挑战。需要在不影响结肠镜检查能力的情况下保持高灵敏度的方法。^{本研究研究了一种算法，该算法结合了 FIT 结果、与 CRC 相关的血液生物标志物和个人人口统计数据，以在 FIT 阳性 (FIT +} ) 筛查人群中对送去接受结肠镜检查的受试者进行分类，从而减轻结肠镜检查负担。

材料和方法

丹麦国家结直肠癌筛查计划纳入了 4048 名 FIT ⁺（≥100 ng/mL 血红蛋白）受试者，并使用 ARCHITECT i 2000 对一组 9 种癌症相关生物标志物进行了分析。开发了两种算法：1) 预定义算法基于临床可用的生物标志物：FIT、年龄、CEA、hsCRP 和铁蛋白；2) 探索性算法，在预定义算法中添加额外的生物标志物：TIMP-1、Pepsinogen-2、HE4、CyFra21-1、Galectin-3、B2M 和性别。使用逻辑回归模型，以单独的 FIT 为基准，对 2 个模型中区分患有或不患有 CRC 的受试者的诊断性能进行了基准测试。

结果

CRC 的区分度显示，预定义模型的曲线下面积 (AUC) 为 73.7 (70.5-76.9)，探索性模型的曲线下面积 (AUC) 为 75.3 (72.1-78.4)，单独的 FIT 为 68.9 (65.5-72.2)。两种模型的表现均明显优于FIT 模型( P < .001)。使用相应数量的真阳性和假阳性，以 100、200、300、400 和 500 ng/mL 血红蛋白的截止值对模型与 FIT 进行基准测试。所有绩效指标在所有截止点均得到改善。

结论

在 FIT 结果高于 100 ng/mL 血红蛋白的筛查人群中，包括 FIT 结果、血液生物标志物和人口统计数据组合的筛查算法在区分患有或不患有 CRC 的受试者方面优于 FIT。