Abstract
Background: Colorectal cancer (CRC) is the third most common cancer worldwide. Although colonoscopy screening has been proven as an effective strategy for preventing CRC unfortunately, even conventional colonoscopy by expert gastroenterologists can miss adenomas or pre-cancerous lesions in up to 25% of cases. This systematic review aimed to classify colorectal polyps (CRP) or CRC in endoscopic clinic settings using a new machine learning method, convolutional neural network (CNN). Methods: We will search PubMed/MEDLINE, Scopus, Web of Science, IEEE, Inspec, ProQuest, Google Scholar, Microsoft Academic Search, ScienceOpen, arXiv, and bioRxiv from 1st January 2010 to the 31th of July 2020. Our search will not be restricted based on language or geographical area. The primary studies will be selected that have observational design (cross-sectional, case control or cohort); the study subjects will be adult patients (>= 18 years old) referred to colonoscopy clinics; and the results of their colonoscopy evaluation will be available in the form of images or videos. The extracted data will be combined using meta-analysis of prediction models. The primary data synthesis will be performed based on area under curve-receiver operating characteristic curve and/or accuracy measures. We will use Stata version 14.2 (Statacorp; College Station, TX) for primary and secondary data synthesis. Conclusion: The inferences of our secondary research will provide evidence to evaluate the prognostic role of CNN in discriminating CRP or CRC in colonoscopy settings.
Highlights
Colorectal cancer (CRC) is the third most common cancer worldwide
During the last two decades, despite efforts made to screen CRC/colorectal polyps (CRP) in target groups, studies have shown that even conventional colonoscopy by expert gastroenterologists can miss adenomas or pre-cancerous lesions in up to 25% of cases[9,10]
convolutional neural network (CNN) is a promising advanced method that can be useful in differentiating CRC from benign lesions, such as polyps
Summary
Colorectal cancer (CRC) is the third most common cancer worldwide. Among the causes of cancer-related death, CRC ranks second[1]. We will aim to design and conduct a systematic review and meta-analysis using a standard and high-quality method to evaluate diagnostic accuracy for discrimination of CRC/CRP by CNN. Protocol This protocol was first designed based on the “priori” approach and registered in Open Science Framework[21]. Primary objective Assessing the accuracy of CNN model (Intervention: I) for discrimination malignancy (cancer) and/or polyp (Outcome: O) from normal colorectal tissue (Comparison: C) in CRC or CRP probable patients (Participants: P) who attended colonoscopic clinics or centers, based on colonoscopic images or videos. Study status This systematic review is currently in the search phase for information sources
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