Abstract
A nomogram is a useful graphical tool for presenting a risk prediction and prognosis prediction in medical research. Intraductal papillary mucinous neoplasm (IPMN) is the premalignant lesions of the pancreas. Among the IPMN, branch duct (BD) IPMN is hard to determine whether progress to an invasive tumor or not. Surgery on the pancreas part is likely to lower the quality of life of the patient, so avoiding surgery to remove IPMN tissue of the patients with low risk should be carefully decided. In this study, we introduce the process of constructing a nomogram and illustrate it with a prediction model to predict malignancy of IPMN.
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References
Liebing G (1966) On nomography in epidemiology. Z Gesamte Hyg 12(2):151–154
Radford EP, Ferris BG, Kriete BC (1954) Clinical use of a nomogram to estimate proper ventilation during artificial respiration. New Engl J Med 251(22):877–884
Van Zee KJ, Manasseh DM, Bevilacqua JL, Boolbol SK, Fey JV, Tan LK, Borgen PI, Cody HS 3rd, Kattan MW (2003) A nomogram for predicting the likelihood of additional nodal metastases in breast cancer patients with a positive sentinel node biopsy. Ann Surg Oncol 10(10):1140–1151
Jang JY, Park T, Lee S, Kim Y, Lee SY, Kim SW, Kim SC, Song KB, Yamamoto M, Hatori T, Hirono S, Satoi S, Fujii T, Hirano S, Hashimoto Y, Shimizu Y, Choi DW, Choi SH, Heo JS, Motoi F, Matsumoto I, Lee WJ, Kang CM, Han HS, Yoon YS, Sho M, Nagano H, Honda G, Kim SG, Yu HC, Chung JC, Nagakawa Y, Seo HI, Yamaue H (2017) Proposed nomogram predicting the individual risk of malignancy in the patients with branch duct type intraductal papillary mucinous neoplasms of the pancreas. Ann Surg 266(6):1062–1068. https://doi.org/10.1097/SLA.0000000000001985
Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15(4):361–387. https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
Simon RM, Subramanian J, Li MC, Menezes S (2011) Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinform 12(3):203–214. https://doi.org/10.1093/bib/bbr001
Lambert J, Halfon P, Penaranda G, Bedossa P, Cacoub P, Carrat F (2008) How to measure the diagnostic accuracy of noninvasive liver fibrosis indices: the area under the ROC curve revisited. Clin Chem 54(8):1372–1378. https://doi.org/10.1373/clinchem.2007.097923
Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21(20):3940–3941. https://doi.org/10.1093/bioinformatics/bti623
Yamaoka K, Nakagawa T, Uno T (1978) Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J Pharmacokinet Biopharm 6(2):165–175
Allen AS, Collins JS, Rathouz PJ, Selander CL, Satten GA (2003) Bootstrap calibration of TRANSMIT for informative missingness of parental genotype data. BMC Genet 4(Suppl 1):S39. https://doi.org/10.1186/1471-2156-4-S1-S39
Efron B, Halloran E, Holmes S (1996) Bootstrap confidence levels for phylogenetic trees. Proc Natl Acad Sci U S A 93(14):7085–7090
Kundu S, Aulchenko YS, van Duijn CM, Janssens AC (2011) PredictABEL: an R package for the assessment of risk prediction models. Eur J Epidemiol 26(4):261–264. https://doi.org/10.1007/s10654-011-9567-4
Harrell FE Jr, Lee KL, Califf RM, Pryor DB, Rosati RA (1984) Regression modelling strategies for improved prognostic prediction. Stat Med 3(2):143–152
Van Belle V, Van Calster B, Van Huffel S, Suykens JA, Lisboa P (2016) Explaining support vector machines: a color based nomogram. PLoS One 11(10):e0164568. https://doi.org/10.1371/journal.pone.0164568
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Kim, Y., Park, T. (2019). Develop Nomogram to Predict Malignancy of Intraductal Papillary Mucinous Neoplasm. In: Su, G. (eds) Pancreatic Cancer. Methods in Molecular Biology, vol 1882. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8879-2_3
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DOI: https://doi.org/10.1007/978-1-4939-8879-2_3
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