Optimizing Screening for Colorectal Cancer: An Algorithm Combining Fecal Immunochemical Test,Blood-Based Cancer-Associated Proteins and Demographics to Reduce Colonoscopy Burden |
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Institution: | 1. Gastro Unit, Hvidovre Hospital, Hvidovre, Denmark;2. Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark;3. Department of Surgery, Nordsjællands Hospital, Hillerød, Denmark;4. Digestive Disease Center, Bispebjerg Hospital, Copenhagen, Denmark;5. Gastro Unit, Section for Gastroenterology, Herlev Hospital, Herlev, Denmark;6. Department of Surgery, Herning Hospital, Herning, Denmark;7. Department of Surgery, Horsens Hospital, Horsens, Denmark;8. Department of Public Health Programmes and University Research Clinic for Cancer Screening, Randers Regional Hospital, Randers, Denmark;9. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark;10. Department of Surgery, Viborg Hospital, Viborg, Denmark;11. Abbott Laboratories, Abbott Diagnostics Division, Abbott Park, IL;12. Endocrine Laboratory, Department of Clinical Chemistry, Amsterdam UMC, AMC & VUMC, Amsterdam, The Netherlands |
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Abstract: | BackgroundFecal 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 methodsFrom 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.ResultsThe 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.ConclusionA 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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Keywords: | Early detection Logistic regression modeling Minimal invasive liquid biopsy AUC"} {"#name":"keyword" "$":{"id":"pc_l1kS12ox4Q"} "$$":[{"#name":"text" "_":"Area under the ROC curve B2M"} {"#name":"keyword" "$":{"id":"pc_fR8Xqe0bw8"} "$$":[{"#name":"text" "_":"Beta-2 Microglobulin CEA"} {"#name":"keyword" "$":{"id":"pc_gfSpgW8JLZ"} "$$":[{"#name":"text" "_":"Carcinoembryonic antigen CRC"} {"#name":"keyword" "$":{"id":"pc_gPgn8sMVi7"} "$$":[{"#name":"text" "_":"Colorectal cancer CyFra21-1"} {"#name":"keyword" "$":{"id":"pc_4Chy9JIlUS"} "$$":[{"#name":"text" "_":"Cytokeratin 19 fragment FIT"} {"#name":"keyword" "$":{"id":"pc_fXFfOQhH80"} "$$":[{"#name":"text" "_":"Fecal immunochemical test HE4"} {"#name":"keyword" "$":{"id":"pc_IZIWXGz4XD"} "$$":[{"#name":"text" "_":"Human epididymis protein 4 HRA"} {"#name":"keyword" "$":{"id":"pc_bCPzaVOX4i"} "$$":[{"#name":"text" "_":"High risk adenoma hsCRP"} {"#name":"keyword" "$":{"id":"pc_znaUHvH0Ks"} "$$":[{"#name":"text" "_":"High-sensitivity C-reactive protein LRA"} {"#name":"keyword" "$":{"id":"pc_ROrFEPAAer"} "$$":[{"#name":"text" "_":"Low risk adenoma MRA"} {"#name":"keyword" "$":{"id":"pc_2lRMMd6mxS"} "$$":[{"#name":"text" "_":"Medium risk adenoma TIMP-1"} {"#name":"keyword" "$":{"id":"pc_SZRzLO25m1"} "$$":[{"#name":"text" "_":"Tissue inhibitor of metalloproteinase-1 ROC"} {"#name":"keyword" "$":{"id":"pc_XkyWoiA8vm"} "$$":[{"#name":"text" "_":"Receiver operating characteristic |
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