Literature Review on Stages of Colorectal Cancer

Kalpana K; Dr. G. N. K. Suresh Babu

doi:10.32628/IJSRST24116172

Authors

Kalpana K Research Scholar, Sristhi College of Science and Management, Bangalore, India Author
Dr. G. N. K. Suresh Babu Professor-CSE, Research Supervisor( University of Mysore), Sristhi College of Science and Management, Bangalore, India Author

DOI:

https://doi.org/10.32628/IJSRST24116172

Keywords:

Colorectal Cancer, Polyps, Adenomas, Early Detection, Metastasis

Abstract

Colorectal cancer, primarily originating from polyps in the colon or rectum, represents a significant health challenge due to its potential progression to malignancy. Polyps, abnormal tissue growths in the inner lining of the colon or rectum, often start as benign adenomas. Over time, these adenomas may undergo cellular mutations, transforming into cancerous tumors. Early detection and removal of polyps during screening significantly reduce the likelihood of cancer development. If left untreated, colorectal cancer can invade deeper layers of the colon wall, metastasize to nearby lymph nodes, and spread to distant organs, commonly the liver, peritoneum, or lungs. Colon cancer typically originates in the mucosa, the innermost lining of the colon, and progresses through the tissue and muscle layers over a span of approximately 10 years. The anatomy of the colon and rectum, consisting of the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum, plays a critical role in understanding the disease's progression. Advances in screening methods and treatments have significantly improved outcomes, emphasizing the importance of early intervention in reducing colorectal cancer mortality.

Downloads

Download data is not yet available.

References

Egeblad, M.; Nakasone, E.S.; Werb, Z. Tumors as Organs: Complex Tissues that Interface with the Entire Organism. Dev. Cell 2010, 18, 884–901. DOI: https://doi.org/10.1016/j.devcel.2010.05.012

Gurcan, M.N.; Boucheron, L.E.; Can, A.; Madabhushi, A.; Rajpoot, N.M.; Yener, B. Histopathological Image Analysis: A Review. IEEE Rev. Biomed. Eng. 2009, 2, 147–171. DOI: https://doi.org/10.1109/RBME.2009.2034865

Janowczyk, A.; Madabhushi, A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J. Pathol. Inform. 2016, 7, 29. DOI: https://doi.org/10.4103/2153-3539.186902

Kather, J.N.; Krisam, J.; Charoentong, P.; Luedde, T.; Herpel, E.; Weis, C.-A.; Gaiser, T.; Marx, A.; Valous, N.A.; Ferber, D.; et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med. 2019, 16, e1002730. DOI: https://doi.org/10.1371/journal.pmed.1002730

Kather, J.N.; Weis, C.-A.; Bianconi, F.; Melchers, S.M.; Schad, L.R.; Gaiser, T.; Marx, A.; Zöllner, F. Multi-class texture analysis in colorectal cancer histology. Sci. Rep. 2016, 6, 27988. DOI: https://doi.org/10.1038/srep27988

Korbar, B.; Olofson, A.M.; Miraﬂor, A.P.; Nicka, C.M.; Suriawinata, M.A.; Torresani, L.; Suriawinata, A.A.; Hassanpour, S. Deep learning for classiﬁcation of colorectal polyps on whole-slide images. J. Pathol. Inform. 2017, DOI: https://doi.org/10.4103/jpi.jpi_34_17

M. A. Fahami, M. Roshanzamir, N. H. Izadi, V. Keyvaniand R. Alizadehsani, “Detection of effective genes in colon cancer: A machine learning approach”, vol. 24, Jan. 2021. DOI: https://doi.org/10.1016/j.imu.2021.100605

M. Liu, J. Jiang and Z. Wang, "Colonic Polyp Detection in Endoscopic Videos With Single Shot Detection Based Deep Convolutional Neural Network," in IEEE Access, vol. 7, pp. 75058-75066, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2921027

M. S. Kwak et al., “Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images,” Frontiers in Oncology, vol. 10, Jan. 2021. DOI: https://doi.org/10.3389/fonc.2020.619803

S. Mobilia, B. Sirkeci-Mergen, J. Deal, T. C. Rich and S. J. Leavesley, "Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks," 2019 IEEE Data Science Workshop (DSW), Minneapolis, MN, USA, 2019, pp. 232-236. DOI: https://doi.org/10.1109/DSW.2019.8755582

S. Poudel, Y. J. Kim, D. M. Vo and S. -W. Lee, "Colorectal Disease Classification Using Efficiently Scaled Dilation in Convolutional Neural Network," in IEEE Access, vol. 8, pp. 99227-99238, 2020, DOI: https://doi.org/10.1109/ACCESS.2020.2996770

T. Yang, “Intelligent Imaging Technology in Diagnosis of Colorectal Cancer Using Deep Learning”, vol. 7, Dec. 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2958124

Tsai, M.J.; Tao, Y.H. Machine Learning Based Common Radiologist-Level Pneumonia Detection on Chest X-rays. In Proceedings of the 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, Australia, 16–18 December 2019. DOI: https://doi.org/10.1109/ICSPCS47537.2019.9008684

Wunsch G, Gourbin C. “Mortality, morbidity and health in developed societies: a review of data sources.” Genus. 2018;74(1):2. DOI: https://doi.org/10.1186/s41118-018-0027-9

X. Yang, Q. Wei, C. Zhang, K. Zhou, L. Kong and W. Jiang, "Colon Polyp Detection and Segmentation Based on Improved MRCNN," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-10, 2021, Art no. 4501710. DOI: https://doi.org/10.1109/TIM.2020.3038011

Xu, J.; Luo, X.; Wang, G.; Gilmore, H.; Madabhushi, A. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 2016, 191, 214–223. DOI: https://doi.org/10.1016/j.neucom.2016.01.034

Zhang, X.; Su, H.; Yang, L.; Zhang, S. Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7–12 June 2015; pp. 5361–5368. DOI: https://doi.org/10.1109/CVPR.2015.7299174

Cancer. Understanding Cancer Risk. Available online: https://www.cancer.net/navigating-cancer-care/prevention-and-healthy-living/understanding-cancer-risk (accessed on 15 March 2023).

Borkowski, A.A.; Wilson, C.P.; Borkowski, S.A.; Thomas, L.B.; Deland, L.A.; Mastorides, S.M. Apple machine learning algorithms successfully detect colon cancer but fail to predict KRAS mutation status. arXiv 2018, arXiv:1812.04660