大肠癌癌症筛查的优化:一种结合粪便免疫化学测试、血液癌症相关蛋白和人口学的算法以减少结肠镜检查负担

IF 3.3 3区 医学 Q2 ONCOLOGY
Mathias M. Petersen , Jakob Kleif , Lars N. Jørgensen , Jakob W. Hendel , Jakob B. Seidelin , Mogens R. Madsen , Jesper Vilandt , Søren Brandsborg , Jørn S. Rasmussen , Lars M. Andersen , Ali Khalid , Linnea Ferm , Susan H. Gawel , Frans Martens , Berit Andersen , Morten Rasmussen , Gerard J. Davis , Ib J. Christensen , Christina Therkildsen
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引用次数: 1

摘要

粪便免疫化学试验(FIT)广泛应用于癌症的人群筛查。这导致了结肠镜检查能力方面的重大挑战。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。这项研究调查了一种算法,该算法结合了FIT结果、与CRC相关的基于血液的生物标志物和个人人口统计数据,在FIT阳性(FIT+)筛查人群中对被送往结肠镜检查的受试者进行分类,从而减少结肠镜检查负担。材料和方法来自丹麦国家癌症筛查计划,纳入4048名FIT+(≥100 ng/mL血红蛋白)受试者,并使用ARCHITECT i2000分析9种癌症相关生物标志物。开发了两种算法:1)基于临床可用的生物标志物的预定义算法:FIT、年龄、CEA、hsCRP和Ferritin;和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、胃蛋白酶原-2、HE4、CyFra21-1、半乳糖凝集素-3、B2M和性别。使用逻辑回归模型,将两个模型中区分患有或不患有CRC的受试者的诊断性能与单独的FIT进行比较。结果CRC的鉴别显示,预定义模型的曲线下面积(AUC)为73.7(70.5-76.9),探索模型为75.3(72.1-78.4),单独FIT为68.9(65.5-72.2)。两个模型的表现都明显好于FIT模型(P<;.001)。在100、200、300、400和500 ng/mL血红蛋白的临界值下,使用相应数量的真阳性和假阳性对模型与FIT进行基准测试。所有性能指标在所有截止点都得到了改进。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、基于血液的生物标志物和人口统计学的筛查算法在区分患有或不患有CRC的受试者方面优于FIT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Screening for Colorectal Cancer: An Algorithm Combining Fecal Immunochemical Test, Blood-Based Cancer-Associated Proteins and Demographics to Reduce Colonoscopy Burden

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.

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来源期刊
Clinical colorectal cancer
Clinical colorectal cancer 医学-肿瘤学
CiteScore
5.50
自引率
2.90%
发文量
64
审稿时长
27 days
期刊介绍: Clinical Colorectal Cancer is a peer-reviewed, quarterly journal that publishes original articles describing various aspects of clinical and translational research of gastrointestinal cancers. Clinical Colorectal Cancer is devoted to articles on detection, diagnosis, prevention, and treatment of colorectal, pancreatic, liver, and other gastrointestinal cancers. The main emphasis is on recent scientific developments in all areas related to gastrointestinal cancers. Specific areas of interest include clinical research and mechanistic approaches; drug sensitivity and resistance; gene and antisense therapy; pathology, markers, and prognostic indicators; chemoprevention strategies; multimodality therapy; and integration of various approaches.
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