Colorectal Cancer Symptoms and Risk Factors - A Review
DOI:
https://doi.org/10.32628/IJSRST24116171Keywords:
Colorectal Cancer, Colon Cancer, Rectal Cancer, Global Burden, Quality of lifeAbstract
Colorectal cancer is a type of cancer that affects the colon (large intestine) or rectum. It is one of the most common types of cancer worldwide. It can cause severe harm and death. The risk of colorectal cancer increases with age. Most cases affect people over 50 years old. Common symptoms include diarrhoea, constipation, blood in the stool, abdominal pain, unexplained weight loss, fatigue, and low iron levels. Many people will not have symptoms in the early stages of the disease. The risk of colorectal cancer can be reduced by eating a healthy diet, staying physically active, not smoking tobacco and limiting alcohol. Regular screenings are crucial for early detection. Colon cancer is the second leading cause of cancer-related deaths worldwide. In 2020, more than 1.9 million new cases of colorectal cancer and more than 930 000 deaths due to colorectal cancer were estimated to have occurred worldwide. Large geographical variations in incidence and mortality rates were observed. The incidence rates were highest in Europe and Australia and New Zealand, and the mortality rates were highest in Eastern Europe. By 2040 the burden of colorectal cancer will increase to 3.2 million new cases per year (an increase of 63%) and 1.6 million deaths per year (an increase of 73%). Incidence rates of colorectal cancer have been decreasing in high-income countries, largely as a result of effective screening programmes. The prognosis for colorectal cancer varies depending on the stage at diagnosis. Early-stage cancers have higher survival rates than advanced-stage cancers. Timely diagnosis, appropriate treatment, and regular follow-up care are important for improving survival rates and quality of life.
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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.
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