Computer-Aided Diagnosis in Colorectal Cancer: Current Concepts and Future Prospects

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Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers’ focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, different computational models as well as their benchmarks expressed through measurements such as positive prediction value and accuracy of detection, and the classification of colorectal polyps. Introducing computer-aided diagnostic tools can help clinicians obtain results with a high degree of confidence when performing colonoscopies. The growing field of machine learning in medicine will have a big impact on patient management in the future.

1. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359-E386.

2. Bloom DE, Cafiero ET, Jané-Llopis E, et al. The global economic burden of non-communicable diseases. Geneva: World Economic Forum, 2011. Available at:

3. Wiegering A, Ackermann S, Riegel J, et al. Improved survival of patients with colon cancer detected by screening colonoscopy. Int J Colorectal Dis. 2016;31:1039-1045.

4. Regge D, Hassan C, Pickhardt PJ, et al. Impact of computer-aided detection on the cost-effectiveness of CT colonography. Radiology. 2009;250:488-497.

5. Dolgobrodov SD, Marshall R, Moore P, Bittern R, Steele RJC, Cuschieri A. e-Science and artificial neural networks in cancer management. Concurrency Computat Pract Exper. 2007;19:251-263.

6. Tjoa MP, Krishnan SM. Feature extraction for the analysis of colon status from the endoscopic images. Biomed Eng Online. 2003;8;2:9.

7. Selaru FM, Xu Y, Yin J, et al. Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions. Gastroenterology. 2002;122:606-613.

8. Djemal K, Cocquerez JP, Precioso F. Visual feature extraction and description. In: Benois-Pineau J, Precioso F, Cord M, eds. Visual Indexing and Retrieval. 1st ed. New York: Springer, 2012; p. 5-20.

9. Igelnik B. Computational Modeling and Simulation of Intellect: Current State and Future Perspectives. 1st ed. Hershey: IGI Global, 2011.

10. Naguib RNG, Sherbet GV. Introduction to artificial neural networks and their use in cancer diagnosis, prognosis, and patient management. In: Naguib RNG, Sherbet GV, eds. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management. Boca Raton: CRC Press, 2001; p. 1-8.

11. da Silva IN, Hernane Spatti D, Andrade Flauzino R, Liboni LHB, dos Reis Alves SF. Introduction. In: Artificial Neural Networks: A Practical Course. Cham: Springer International Publishing, 2017; p. 3-19.

12. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20:273-297.

13. Maroulis DE, Iakovidis DK, Karkanis SA, Karras DA. CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames. Comput Methods Programs Biomed. 2003;70:151-166.

14. Oliva JT, Lee HT, Spolaôr N, Coy CSR, Wu FC. Prototype system for feature extraction, classification and study of medical images. Expert Syst Appl. 2016;63:267-283.

15. Tjoa MP, Krishnan SM. Feature extraction for the analysis of colon status from the endoscopic images. Biomed Eng Online. 2003;2:9.

16. Biswas M, Bhattacharya A, Dey D. Classification of various colon diseases in Colonoscopy video using Cross-Wavelet features. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, 2016; p. 2141-2145.

17. Ribeiro E, Uhl A, Häfner M. Colonic Polyp Classification with Convolutional Neural Networks. 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), Dublin, 2016; p. 253-258.

18. Uhl A, Wimmer G, Hafner M. Shape and size adapted local fractal dimension for the classification of polyps in HD colonoscopy. 2014 IEEE International Conference on Image Processing (ICIP), Paris, 2014; p. 2299-2303.

19. Kominami Y, Yoshida S, Tanaka S, et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc. 2016;83:643-649.

20. Tanaka S, Kaltenbach T, Chayama K, Soetikno R. High-magnification colonoscopy (with videos). Gastrointest Endosc. 2006;64:604-613.

21. Tamaki T, Yoshimuta J, Takeda T, et al. A System for Colorectal Tumor Classification in Magnifying Endoscopic NBI Images. In: Kimmel R, Klette R, Sugimoto A, eds. Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Berlin/Heidelberg: Springer, 2011.

22. Tajbakhsh N, Gurudu SR, Liang J. Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information. IEEE Transactions on Medical Imaging. 2016;35:630-644.

23. Yu L, Chen H, Dou Q, Qin J, Heng PA. Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos. IEEE Journal of Biomedical and Health Informatics. 2017;21:65-75.

24. Zhang R, Zheng Y, Wing Chuk Mak T, et al. Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain. IEEE Journal of Biomedical and Health Informatics. 2017;21:41-47.

25. Fu JJ, Yu YW, Lin HM, Chai JW, Chen CC. Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging. Comput Med Imaging Graph. 2014;38:267-275.

26. Streba CT, Ionescu M, Vere CC, Rogoveanu I. Artificial Intelligence and Automatic Image Interpretation in Modern Medicine. In Wei DQ, Ma Y, Cho WCS, Xu Q, Zhou F, eds. Translational Bioinformatics and Its Application. 1st edition. Springer Netherlands, 2017; p. 371-407.

27. Ameling S, Wirth S, Paulus, D, Lacey, G, Vilarino F. Texture-Based Polyp Detection in Colonoscopy. In: Meinzer HP, Deserno TM, Handels H, Tolxdorff T, eds. Bildverarbeitung für die Medizin 2009: Algorithmen – Systeme – Anwendungen. Berlin/Heidelberg: Springer, 2009; p. 346-350.

28. Kim J, Kim BS, Savarese S. Comparing Image Classification Methods: K-nearest-neighbor and Support-vector-machines. In: Proceedings of the 6th WSEAS International Conference on Computer Engineering and Applications, and Proceedings of the 2012 American Conference on Applied Mathematics Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS), 2012; p. 133-138.

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