Review Article

Machine learning application in cancer research: Mini Review

Ghazaleh Niknam

Ghazaleh Niknam
Department of Network Science and Technologies, University of Tehran, Tehran, Iran. Email: gh.niknam@alumni.ut.ac.ir
Online First: July 25, 2019 | Cite this Article
Niknam, G. 2019. Machine learning application in cancer research: Mini Review. The Cancer Press 5(1, 2, 3, 4). DOI:10.15562/tcp.76


Nowadays, due to the significant growth of medical data production, utilization of interdisciplinary science, such as data mining, is also increasing. In order to discover the knowledge, form an enormous quantity of medical data, data mining would be helpful tools. One of the common data mining techniques is machine learning. This approach is the ability of learning without being explicitly programmed by computers through sets of algorithms. In the past few years, many researches have been carried out the machine learning algorithms utilization in cancer research. In this mini-review, besides defining the concepts of machine learning, the application of machine learning on cancer data also has been reviewed. The repeated studies are divided into four categories, including Identification of high-risk people, Prediction of cancer staging, Prediction of cancer clinical outcomes and Medical image analysis. Studies show that the use of machine learning in medical fields is increasing and there is a promising progress in this area.

References

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.

J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.

S. J. a. p. a. Kharya, "Using data mining techniques for diagnosis and prognosis of cancer disease," 2012.

D. Arnott and G. Pervan, "A critical analysis of decision support systems research revisited: the rise of design science," in Enacting Research Methods in Information Systems: Springer, 2016, pp. 43-103.

K. Sharma, J. J. I. J. o. A. C. Virmani, and Intelligence, "A decision support system for classification of normal and medical renal disease using ultrasound images: A decision support system for medical renal diseases," vol. 8, no. 2, pp. 52-69, 2017.

L. A. Torre, R. L. Siegel, E. M. Ward, A. J. C. E. Jemal, and P. Biomarkers, "Global cancer incidence and mortality rates and trends—an update," vol. 25, no. 1, pp. 16-27, 2016.

Ö. J. A. c. i. Terzi and s. computing, "Monthly rainfall estimation using data-mining process," vol. 2012, p. 20, 2012.

K. Konstantina, T. Exarchos, K. Exarchos, M. Karamouzis, D. J. C. Fotiadis, and s. b. journal, "Machine learning applications in cancer prognosis and prediction," vol. 13, pp. 8-17, 2015.

Y. Wang et al., "Gene selection from microarray data for cancer classification—a machine learning approach," vol. 29, no. 1, pp. 37-46, 2005.

D. Wang, J.-R. Li, Y.-H. Zhang, L. Chen, T. Huang, and Y.-D. J. G. Cai, "Identification of differentially expressed genes between original breast cancer and xenograft using machine learning algorithms," vol. 9, no. 3, p. 155, 2018.

S. Huang, N. Cai, P. P. Pacheco, S. NARRANDES, Y. Wang, and W. J. C. G.-P. Xu, "Applications of support vector machine (SVM) learning in cancer genomics," vol. 15, no. 1, pp. 41-51, 2018.

P. B. Bach et al., "Variations in lung cancer risk among smokers," vol. 95, no. 6, pp. 470-478, 2003.

S. M. Domchek, A. Eisen, K. Calzone, J. Stopfer, A. Blackwood, and B. L. J. J. o. C. O. Weber, "Application of breast cancer risk prediction models in clinical practice," vol. 21, no. 4, pp. 593-601, 2003.

F. Gasco, M. Valle, R. Martos, M. Zafra, R. Morales, and M. J. E. j. o. c. p. Castano, "Childhood obesity and hormonal abnormalities associated with cancer risk," vol. 13, no. 3, pp. 193-197, 2004.

G. Opinto et al., "Hierarchical clustering analysis identifies metastatic colorectal cancers patients with more aggressive phenotype," vol. 8, no. 50, p. 87782, 2017.

K. P. Exarchos, Y. Goletsis, and D. I. J. I. T. o. I. T. i. B. Fotiadis, "Multiparametric decision support system for the prediction of oral cancer reoccurrence," vol. 16, no. 6, pp. 1127-1134, 2011.

A. K. Kangi and A. J. A. P. j. o. c. p. A. Bahrampour, "Predicting the survival of gastric cancer patients using artificial and bayesian neural networks," vol. 19, no. 2, p. 487, 2018.

L. E. Wroblewski, R. M. Peek, and K. T. J. C. m. r. Wilson, "Helicobacter pylori and gastric cancer: factors that modulate disease risk," vol. 23, no. 4, pp. 713-739, 2010.

Y. Kinar et al., "Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer," vol. 12, no. 2, p. e0171759, 2017.

M. C. Hornbrook et al., "Early colorectal cancer detected by machine learning model using gender, age, and complete blood count data," vol. 62, no. 10, pp. 2719-2727, 2017.

Y. Kinar et al., "Development and validation of a predictive model for detection of colorectal cancer in primary care by analysis of complete blood counts: a binational retrospective study," vol. 23, no. 5, pp. 879-890, 2016.

T. Ayer, O. Alagoz, J. Chhatwal, J. W. Shavlik, C. E. Kahn Jr, and E. S. J. C. Burnside, "Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration," vol. 116, no. 14, pp. 3310-3321, 2010.

J. Kehoe and V. P. J. S. O. C. Khatri, "Staging and prognosis of colon cancer," vol. 15, no. 1, pp. 129-146, 2006.

E. A. Arena and A. J. J. A. i. s. Bilchik, "What is the optimal means of staging colon cancer?," vol. 47, p. 199, 2013.

S. Pourahmad, S. Pourhashemi, and M. J. A. P. J. o. C. P. Mohammadianpanah, "Colorectal Cancer Staging Using Three Clustering Methods Based on Preoperative Clinical Findings," vol. 17, no. 2, pp. 823-827, 2016.

O. Regnier-Coudert, J. McCall, R. Lothian, T. Lam, S. McClinton, and J. J. A. i. i. m. N’Dow, "Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers," vol. 55, no. 1, pp. 25-35, 2012.

J. Gründner, H.-U. Prokosch, M. Stürzl, R. Croner, J. Christoph, and D. Toddenroth, "Predicting Clinical Outcomes in Colorectal Cancer Using Machine Learning," in MIE, 2018, pp. 101-105.

L. Zhang, H. Liu, J. Meng, X. Wang, Y. Chen, and Y. Huangi, "Integration of gene expression, genome wide DNA methylation, and gene networks for clinical outcome prediction in ovarian cancer," in 2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013, pp. 535-538: IEEE.

R. Al-Bahrani, A. Agrawal, and A. Choudhary, "Colon cancer survival prediction using ensemble data mining on SEER data," in 2013 IEEE international conference on Big Data, 2013, pp. 9-16: IEEE.

Z.-H. Zhou and Y. J. I. T. o. i. T. i. B. Jiang, "Medical diagnosis with C4. 5 rule preceded by artificial neural network ensemble," vol. 7, no. 1, pp. 37-42, 2003.

D. Delen, G. Walker, and A. J. A. i. i. m. Kadam, "Predicting breast cancer survivability: a comparison of three data mining methods," vol. 34, no. 2, pp. 113-127, 2005.

Y.-C. Chen, W.-C. Ke, H.-W. J. C. i. b. Chiu, and medicine, "Risk classification of cancer survival using ANN with gene expression data from multiple laboratories," vol. 48, pp. 1-7, 2014.

M. I. Razzak, S. Naz, and A. Zaib, "Deep learning for medical image processing: Overview, challenges and the future," in Classification in BioApps: Springer, 2018, pp. 323-350.

A. Madabhushi and G. Lee, "Image analysis and machine learning in digital pathology: Challenges and opportunities," ed: Elsevier, 2016.

T. Itoh, H. Kawahira, H. Nakashima, and N. J. E. i. o. Yata, "Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images," vol. 6, no. 02, pp. E139-E144, 2018.

A. S. Lundervold and A. J. Z. f. M. P. Lundervold, "An overview of deep learning in medical imaging focusing on MRI," 2018.

X.-Z. Chen et al., "Association of helicobacter pylori infection and chronic atrophic gastritis with risk of colonic, pancreatic and gastric cancer: A ten-year follow-up of the ESTHER cohort study," vol. 7, no. 13, p. 17182, 2016.

D. Bychkov et al., "Deep learning based tissue analysis predicts outcome in colorectal cancer," vol. 8, no. 1, p. 3395, 2018.


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