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©The Author(s) 2022.
World J Gastrointest Oncol. Jan 15, 2022; 14(1): 124-152
Published online Jan 15, 2022. doi: 10.4251/wjgo.v14.i1.124
Published online Jan 15, 2022. doi: 10.4251/wjgo.v14.i1.124
Type of study | Ref. | Method | Conclusion |
Case control study | Zhang et al[128], 2017 | Heterogeneous ensemble learning model | Heterogeneous ensemble learning model could use big data to identify high-risk groups of CRC patients |
Retrospective study | Morgado et al[129], 2017 | Decision support system | Decision support system could evaluate the risk of CRC by processing incomplete, unknown, or even contradictory data |
Case control study | Anand et al[131], 1999 | Intelligent hybrid system | Each AI technology produced a different set of important attributes. Intelligent hybrid system would be the trend of prognosis evaluation in the future |
Case control study | Gupta et al[132], 2019 | ML | ML could help to predict tumor stage and survival period |
Case control study | Li et al[133], 2018 | ML | Combining ML and database, clinicians might add race factor to evaluate prognosis |
Case control study | Barsainya et al[134], 2018 | Decision tree classifier | Decision tree classifier could predict recurrence and death according to various influencing factors |
Cohort study | Dimitriou et al[135], 2018 | ML | A framework for accurate prognosis prediction of CRC based on ML datasets |
Case control study | Popovici et al[136], 2017 | SVM | The accuracy of using SVM to distinguish CRC subtypes was very high |
Experimental study | Hoogendoorn et al[137], 2016 | AI | AI helped doctors to extract useful predictors from non-coding medical records |
Experimental study | Kop et al[138], 2016 | ML | The combination of ML and electronic medical records could help early detection and intervention |
Case control study | Geessink et al[139], 2015 | Supervised learning | Supervised learning helped to predict the survival ability of tumor, so as to accurately stratify the prognosis of tumor patients |
Review | Wright et al[140], 2014 | RF | RF could reduce the workload of pathologists by automatically calculating the area ratio of each slide |
Meta-analysis | Wang et al[141], 2019 | A two-stage ML model | Compared with the single-stage regression model, the two-stage model could obtain more accurate prediction results |
Experimental study | Oliveira et al[142], 2013 | CDSS | CDSS based on cancer patients records and knowledge could provide support for surgeons |
Meta-analysis | Lo et al[143], 2000 | CDSS | CDSS could select the appropriate treatment from the aspects of curative effect, overall survival rate, and side effect rate |
Case control study | Harrington et al[144], 2018 | ML | ML could be used to predict the risk of recurrence of colon polyps and cancer based on the pathological characteristics of medical records |
Case control study | Xie et al[145], 2018 | RF model | RF model helped to speculate the influencing factors of early CRC in China |
Retrospective study | Bokhorst et al[146], 2018 | DL | DL helped reduce FP by detecting tumor bud |
Cohort study | Zhao et al[147], 2020 | DL | The method allowed objective and standardized application while reducing the workload of pathologists |
Retrospective study | Syafiandini et al[148], 2016 | DBM | DBM helped to predict the survival time of cancer patients |
Retrospective study | Roadknight et al[149], 2013 | ML | ML helped predict the prognosis of patients according to the immune status and other information |
Case control study | Cui et al[150], 2013 | SSL | SSL improved the accuracy of predicting clinical results according to gene expression profile |
Retrospective study | Park et al[151], 2014 | SSL | SSL could improve the accuracy of predicting cancer recurrence |
Retrospective study | Du et al[152], 2014 | Supervised learning | Supervised learning could help to improve the accuracy of identifying cancer-related mutations |
Case control study | Chi et al[153], 2019 | Semi-supervised logistic regression method | Semi-supervised logistic regression method had better clinical prediction effect than supervised learning method |
Review | Ong et al[154], 1997 | CARES system | CARES system helped early detection of cancer recurrence in high-risk patients |
Case control study | Reichling et al[155], 2020 | DGMate | DGMate could judge the prognosis of tumor by detecting immunophenotype |
Experimental study | Chowdhury et al[156], 2011 | Crane algorithm | Crane algorithm helped to describe the coordination of multiple genes and effectively predicted the metastasis of CRC |
Review | Mohamad et al[157], 2019 | Nominal group technique | Nominal group technique was used in the content development of mobile app and the app used as a tool for CRC screening education |
Retrospective study | Hacking et al[158], 2020 | AI | AI could improve the prognosis of patients by increasing the diagnostic accuracy of slide images |
- Citation: Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14(1): 124-152
- URL: https://www.wjgnet.com/1948-5204/full/v14/i1/124.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v14.i1.124