ArticleWorld journal of gastrointestinal oncology2020
Clinical value evaluation of serum markers for early diagnosis of colorectal cancer.
Article in World journal of gastrointestinal oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Where do we stand with screening for colorectal cancer and advanced adenoma based on serum protein biomarkers? A systematic review.Molecular oncology · 2024Pooled it
- Transcription factor HOXC10 activates the expression of MTFR2 to regulate the proliferation, invasion and migration of colorectal cancer cells.Molecular medicine reports · 2021Article
- Prognostic Impact of Pretreatment Elevated and Normalized Carcinoembryonic Antigen Levels After Neoadjuvant Chemoradiotherapy in Resected Locally Advanced Rectal Cancer Patients.Cancer management and research · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly screening for colorectal cancer (CRC) is important in clinical practice. However, the currently methods are inadequate because of high cost and low diagnostic value.
aimTo develop a new examination method based on the serum biomarker panel for the early detection of CRC.
methodsThree hundred and fifty cases of CRC, 300 cases of colorectal polyps and 360 cases of normal controls. Combined with the results of area under curve (AUC) and correlation analysis, the binary Logistic regression analysis of the remaining indexes which is in accordance with the requirements was carried out, and discriminant analysis, classification tree and artificial neural network analysis were used to analyze the remaining indexes at the same time.
resultsBy comparison of these methods, we obtained the ability to distinguish CRC from healthy control group, malignant disease group and benign disease group. Artificial neural network had the best diagnostic value when compared with binary logistic regression, discriminant analysis, and classification tree. The AUC of CRC and the control group was 0.992 (0.987, 0.997), sensitivity and specificity were 98.9% and 95.6%. The AUC of the malignant disease group and benign group was 0.996 (0.992, 0.999), sensitivity and specificity were 97.4% and 96.7%.
conclusionArtificial neural network diagnosis method can improve the sensitivity and specificity of the diagnosis of CRC, and a novel assistant diagnostic method was built for the early detection of CRC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.