- Volume 17 Issue 2
Determination of the colorectal cancer stage is possible only after surgery based on pathology results. However, sometimes this may prove impossible. The aim of the present study was to determine colorectal cancer stage using three clustering methods based on preoperative clinical findings. All patients referred to the Colorectal Research Center of Shiraz University of Medical Sciences for colorectal cancer surgery during 2006 to 2014 were enrolled in the study. Accordingly, 117 cases participated. Three clustering algorithms were utilized including k-means, hierarchical and fuzzy c-means clustering methods. External validity measures such as sensitivity, specificity and accuracy were used for evaluation of the methods. The results revealed maximum accuracy and sensitivity values for the hierarchical and a maximum specificity value for the fuzzy c-means clustering methods. Furthermore, according to the internal validity measures for the present data set, the optimal number of clusters was two (silhouette coefficient) and the fuzzy c-means algorithm was more appropriate than the k-means clustering approach by increasing the number of clusters.
Colorectal cancer;clinical tumor staging;cluster analysis
- Bataineh K, Naji M, Saqer M (2011). A comparison study between various fuzzy clustering algorithms. Editorial Board, 5, 335.
- Belhassen S, Zaidi H (2010). A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Medical physics, 37, 1309-24. https://doi.org/10.1118/1.3301610
- Bunyak F, Hafiane A, Palaniappan K (2011). Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. In 'Software tools and algorithms for biological systems', Eds Springer, 413-24
- Cirocco WC (2000). Complex decisions after local (endoscopic) resection of early rectal cancer: opening Pandora's box of staging and treatment options. Gastrointest Endosc, 52, 309-10. https://doi.org/10.1016/S0016-5107(00)70333-6
- Clifford H, Wessely F, Pendurthi S, et al (2011). Comparison of clustering methods for investigation of genome-wide methylation array data. Frontiers in genetics, 2.
- Colon and Rectum (2010). In 'American joint committee on cancer, AJCC cancer staging manual', Eds Springier, New York, 143.
- Ekong V, Onibere E, Imianvan A (2011). Fuzzy cluster means system for the diagnosis of liver diseases. Int J Computer Science Technol, 2, 205-9.
- Fallahi A, Pooyan M, Ghanaati H, et al (2011). Uterine segmentation and volume measurement in uterine fibroid patients' MRI using fuzzy C-mean algorithm and morphological operations. Iranian J Radiol, 8, 150. https://doi.org/10.5812/kmp.iranjradiol.17351065.3142
- Hari DM, Leung AM, Lee JH, et al (2013). AJCC Cancer Staging Manual 7th edition criteria for colon cancer: do the complex modifications improve prognostic assessment? J Am Coll Surg, 217, 181-90. https://doi.org/10.1016/j.jamcollsurg.2013.04.018
- Hirsch O, Bösner S, Hullermeier E, et al (2011). Multivariate modeling to identify patterns in clinical data: the example of chest pain. BMC Med Res Methodol, 11, 155. https://doi.org/10.1186/1471-2288-11-155
- Hoseini S, Moaddabshoar L, Hemati S, et al (2014). An Overview of Clinical and Pathological Characteristics and Survival Rate of Colorectal Cancer in Iran. Ann Colorectal Res, 2, 17264.
- Jee Y, Oh CM, Shin A (2015). recent decrease in colorectal cancer mortality rate is affected by birth cohort in Korea. Asian Pac J Cancer Prev, 16, 3951-5. https://doi.org/10.7314/APJCP.2015.16.9.3951
- Jemal A, Center MM, DeSantis C, et al (2010). Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol Biomarkers Prev, 19, 1893-907. https://doi.org/10.1158/1055-9965.EPI-10-0437
- Karemore G, Mullick JB, Sujatha R, et al (2010). Classification of protein profiles using fuzzy clustering techniques: an application in early diagnosis of oral, cervical and ovarian cancer. Conf Proc IEEE Eng Med Biol Soc, 2010, 6361-4.
- Keller B, Nathan D, Wang Y, et al (2011). Adaptive multi-cluster fuzzy C-means segmentation of breast parenchymal tissue in digital mammography. In 'Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011', Eds Springer, 562-9
- Kijima S, Sasaki T, Nagata K, et al (2014). Preoperative evaluation of colorectal cancer using CT colonography, MRI, and PET/CT. World J Gastroenterol, 20, 16964-75. https://doi.org/10.3748/wjg.v20.i45.16964
- Lee PH (2014). Association between adolescents' physical activity and sedentary behaviors with change in BMI and risk of type 2 diabetes. PLoS One, 9, 110732. https://doi.org/10.1371/journal.pone.0110732
- Li L, Chen S, Wang K, et al (2015). Diagnostic Value of Endorectal Ultrasound in Preoperative Assessment of Lymph Node Involvement in Colorectal Cancer: a Meta-analysis. Asian Pac J Cancer Prev, 16, 3485-91. https://doi.org/10.7314/APJCP.2015.16.8.3485
- Ludwig JA, Weinstein JN (2005). Biomarkers in cancer staging, prognosis and treatment selection. 5, 845-56. https://doi.org/10.1038/nrc1739
- Maeda Y, Sadahiro S, Suzuki T, et al (2015). Significance of the mucinous component in the histopathological classification of colon cancer. Surg Today.
- Nguyen HT, Jia G, Pohar KS, et al (2014). Improving Bladder Cancer Staging by using quantitative DCE-MRI with k-means clustering.
- Omidvari S, Hamedi SH, Mohammadianpanah M, et al (2013). Comparison of abdominoperineal resection and low anterior resection in lower and middle rectal cancer. J Egypt Natl Canc Inst, 25, 151-60. https://doi.org/10.1016/j.jnci.2013.06.001
- Omidvari S, Zohourinia S, Ansari M, et al (2015). Efficacy and safety of low-dose-rate endorectal brachytherapy as a boost to neoadjuvant chemoradiation in the treatment of locally advanced distal rectal cancer: A Phase-II Clinical Trial. Ann Coloproctol, 31, 123-30. https://doi.org/10.3393/ac.2015.31.4.123
- Pang Y, Li L, Hu W, et al (2012). Computerized segmentation and characterization of breast lesions in dynamic contrastenhanced MR images using fuzzy c-means clustering and snake algorithm. Comput Math Methods Med, 2012.
- Petersen RK, Hess S, Alavi A, et al (2014). Clinical impact of FDG-PET/CT on colorectal cancer staging and treatment strategy. Am J Nucl Med Mol Imaging, 4, 471-82.
- Roder D, Karapetis CS, Wattchow D, et al (2015). Colorectal cancer treatment and survival: the experience of major public hospitals in south Australia over three decades. Asian Pac J Cancer Prev, 16, 2431-40. https://doi.org/10.7314/APJCP.2015.16.6.2431
- Ryali S, Chen T, Padmanabhan A, et al (2015). Development and validation of consensus clustering-based framework for brain segmentation using resting fMRI. J Neurosci Methods, 240, 128-40. https://doi.org/10.1016/j.jneumeth.2014.11.014
- Sarkar A, Maulik U (2015). Gene microarray data analysis using parallel point-symmetry-based clustering. Int J Data Min Bioinform, 11, 277-300. https://doi.org/10.1504/IJDMB.2015.067320
- Shahrbanian S, Duquette P, Kuspinar A, et al (2015). Contribution of symptom clusters to multiple sclerosis consequences. Quality Life Res, 24, 617-29. https://doi.org/10.1007/s11136-014-0804-7
- Xu R, Wunsch D (2005). Survey of clustering algorithms. Neural Networks, IEEE Transactions on, 16, 645-78. https://doi.org/10.1109/TNN.2005.845141
- Xu Z, Allen WM, Baucom RB, et al (2013). Texture analysis improves level set segmentation of the anterior abdominal wall. Medical physics, 40, 121901. https://doi.org/10.1118/1.4828791
- Yang G, Raschke F, Barrick TR, et al (2015). Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering. Magn Reson Med, 74, 868-78. https://doi.org/10.1002/mrm.25447
Supported by : Shiraz University of Medical Sciences