Surgeons overestimate the risk of malignancy in thyroid nodules, evaluation of subjective estimates using a bayesian analysis
Keywords:
thyroid nodule, Bayes theorem, thyroid neoplasm, models, statistical, probability, health knowledge, attitudes, practice
Abstract
Introduction: Thyroid nodules are the most common endocrine condition treated by surgeons. The main purpose of the evaluation of a thyroid nodule is to rule out a carcinoma. Medical decisions concerning thyroid nodules are highly influenced by subjective beliefs.
Objective: To assess the subjective probabilities of malignancy that are assigned to the clinical characteristics of a patient with a thyroid nodule in order to determine the degree of influence that these probabilities have on the final clinical suspicion of malignancy compared with objective data.
Material and methods: A bayesian analysis was designed to predict the risk of malignancy of a thyroid nodule based on the causal relationship between the demographic and clinical risk factors that are detected during the first consultation. A model with demographic and clinical variables using general surgeons as experts was developed.
Results: The highest probability of malignancy (94%) was assigned to the pooled case of a male who was older than 60 years, with dysphonia, dysphagia, accelerated growth rate of the nodule and previous neck radiotherapy and who had a relative with thyroid cancer as well as multiple nodules that were larger than 1 cm and with hard consistency and palpable neck lymph nodes. For low risk cases in which the nodule characteristics are not suggestive of malignancy, the probability of malignancy assigned by clinicians was 33.59%; for high risk cases this was 75.54%.
Conclusion: Surgeons make diagnostic decisions based on subjective beliefs that do not necessarily correspond to the objective measures of the characteristics of the nodules.
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References
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28. Hoshi K, Kawakami J, Sato W, Sato K, Sugawara A,Saito Y, et al. Assisting the diagnosis of thyroid diseases with Bayesian-type and SOM-type neural networks making use of routine test data. Chem Pharm Bull (Tokyo). 2006;54:1162-9.
29. Zhang G, Berardi VL. An investigation of neural networks in thyroid function diagnosis. Health Care Manag Sci. 1998;1:29-37.
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33. Sequist TD, Gandhi TK, Karson AS, Fiskio JM, Bugbee D, Sperling M, et al. A randomized trial of electronic clinical reminders to improve quality of care for diabetes and coronary artery disease. J Am Med Inform Assoc. 2005;12:431-7.
34. Ravitch MM. Subjectivity in decision making: common problems and limitations. World J Surg. 1989;13:281-6.
35. Murray E, Pollack L, White M, Lo B. Clinical decisionmaking: Patients' preferences and experiences. Patient Educ Couns. 2007
2. Tan GH, Gharib H. Thyroid incidentalomas: management approaches to nonpalpable nodules discovered incidentally on thyroid imaging. Ann Intern Med. 1997;126:226-31.
3. Wiest PW, Hartshorne MF, Inskip PD, Crooks LA, Vela BS, Telepak RJ, et al. Thyroid palpation versus highresolution thyroid ultrasonography in the detection of nodules. J Ultrasound Med. 1998;17:487-96.
4. McCaffrey TV. Evaluation of the thyroid nodule. Cancer Control. 2000;7:223-8.
5. Mehta MP, Goetowski PG, Kinsella TJ. Radiation induced thyroid neoplasms 1920 to 1987: a vanishing problem? Int J Radiat Oncol Biol Phys. 1989;16:1471-5.
6. Rios A, Rodriguez JM, Canteras M, Galindo PJ, Balsalobre MD, Parrilla P. Risk factors for malignancy in multinodular goiters. Eur J Surg Oncol. 2004;30:58-62.
7. Raza SN, Shah MD, Palme CE, Hall FT, Eski S, Freeman JL. Risk factors for well-differentiated thyroid carcinoma in patients with thyroid nodular disease. Otolaryngol Head Neck Surg. 2008;139:21-6.
8. Tuttle RM, Lemar H, Burch HB. Clinical features associated with an increased risk of thyroid malignancy in patients with follicular neoplasia by fine-needle aspiration. Thyroid. 1998;8:377-83.
9. Druzdzel M, van der Gaag L. Elicitation of probabilities for belief networks: Combining qualitative and quantitative information. Utrecht, The Netherlands: Utrecht University; 1995.p.141-8.
10. Davies L, Welch HG. Increasing incidence of thyroid cancer in the United States, 1973-2002. JAMA. 2006; 295:2164-7.
11. Savage LJ. Elicitation of personal probabilities and expectations. J Am Stat Assoc. 1971;66:783.
12. Burnside ES. Bayesian networks: computer-assisted diagnosis support in radiology. Acad Radiol. 2005;12:422-30.
13. Walker K. Use of expert judgment in exposure assessment: Part 2. Calibration of expert judgments about personal exposures to benzene. J Expo Anal Environ Epidemiol. 2003;13:1-16.
14. Gurleyik E, Coskun O, Aslaner A. Clinical importance of solitary solid nodule of the thyroid in endemic goiter region. Indian J Med Sci. 2005;59:388-95.
15. bu-Eshy SA, Khan AR, Khan GM, al-Humaidi MA, al-Shehri MY, Malatani TS. Thyroid malignancy in multinodular goiter and solitary nodule. JR Coll Surg Edinb. 1995;40:310-2.
16. Mihailescu DV, Schneider AB. Size, number, and distribution of thyroid nodules and the risk of malignancy in radiation-exposed patients who underwent surgery. J Clin Endocrinol Metab. 2008;93:2188-93.
17. Raber W, Kaserer K, Niederle B, Vierhapper H. Risk factors for malignancy of thyroid nodules initially identified as follicular neoplasia by fine-needle aspiration: results of a prospective study of one hundred twenty patients. Thyroid. 2000;10:709-12.
18. Kumar H, Daykin J, Holder R, Watkinson JC, Sheppard MC, Franklyn JA. Gender, clinical findings, and serum thyrotropin measurements in the prediction of thyroid neoplasia in 1005 patients presenting with thyroid enlargement and investigated by fine-needle aspiration
cytology. Thyroid. 1999;9:1105-9. 19. Belfiore A, La Rosa GL, La Porta GA, Giuffrida D, Milazzo G, Lupo L, et al. Cancer risk in patients with cold thyroid nodules: relevance of iodine intake, sex, age, and multinodularity. Am J Med. 1992;93:363-9.
20. Pelizzo MR, Bernante P, Toniato A, Fassina A. Frequency of thyroid carcinoma in a recent series of 539 consecutive thyroidectomies for multinodular goiter. Tumori. 1997;83:653-5.
21. Koh KB, Chang KW. Carcinoma in multinodular goiter. Br J Surg. 1992;79:266-7.
22. Liu YI, Kamaya A, Desser TS, Rubin DL. A bayesian network for differentiating benign from malignant thyroid nodules using sonographic and demographic features. AJR Am J Roentgenol. 2011;196:W598-W605.
23. Sato W, Hoshi K, Kawakami J, Sato K, Sugawara A, Saito Y, et al. Assisting the diagnosis of Graves' hyperthyroidism with Bayesian-type and SOM-type neural networks by making use of a set of three routine tests and their correlation with free T4. Biomed Pharmacother. 2010;64:7-15.
24. Stojadinovic A, Peoples GE, Libutti SK, Henry LR, Eberhardt J, Howard RS, et al. Development of a clinical decision model for thyroid nodules. BMC Surg. 2009;9:12.
25. Liu YI, Kamaya A, Desser TS, Rubin DL. A controlled vocabulary to represent sonographic features of the thyroid and its application in a Bayesian network to predict thyroid nodule malignancy. Summit on Translat Bioinforma. 2009;2009:68-72.
26. Liu YI, Kamaya A, Desser TS, Rubin DL. A Bayesian classifier for differentiating benign versus malignant thyroid nodules using sonographic features. AMIA Annu Symp Proc. 2008;419-23.
27. Daskalakis A, Kostopoulos S, Spyridonos P, Glotsos D, Ravazoula P, Kardari M, et al. Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely H&E-stained cytological images. Comput Biol Med. 2008;38:196-203.
28. Hoshi K, Kawakami J, Sato W, Sato K, Sugawara A,Saito Y, et al. Assisting the diagnosis of thyroid diseases with Bayesian-type and SOM-type neural networks making use of routine test data. Chem Pharm Bull (Tokyo). 2006;54:1162-9.
29. Zhang G, Berardi VL. An investigation of neural networks in thyroid function diagnosis. Health Care Manag Sci. 1998;1:29-37.
30. Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA, et al. Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282:1458-65.
31. Davis DA, Taylor-Vaisey A. Translating guidelines into practice. A systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. CMAJ. 1997;157:408-16.
32. Rollman BL, Hanusa BH, Lowe HJ, Gilbert T, Kapoor WN, Schulberg HC. A randomized trial using computerized decision support to improve treatment of major depression in primary care. J Gen Intern Med. 2002;17:493-503.
33. Sequist TD, Gandhi TK, Karson AS, Fiskio JM, Bugbee D, Sperling M, et al. A randomized trial of electronic clinical reminders to improve quality of care for diabetes and coronary artery disease. J Am Med Inform Assoc. 2005;12:431-7.
34. Ravitch MM. Subjectivity in decision making: common problems and limitations. World J Surg. 1989;13:281-6.
35. Murray E, Pollack L, White M, Lo B. Clinical decisionmaking: Patients' preferences and experiences. Patient Educ Couns. 2007
How to Cite
1.
Alfonso E, Sanabria Álvaro, Castillo M. Surgeons overestimate the risk of malignancy in thyroid nodules, evaluation of subjective estimates using a bayesian analysis. biomedica [Internet]. 2011 Jun. 30 [cited 2024 May 18];31(4):590-8. Available from: https://revistabiomedica.org/index.php/biomedica/article/view/450
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