Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods

Ultrasound in Obstetrics and Gynecology

Volumn 29, Issue 5, 2007, Pages 496-504

  Van Calster, B ^a   Timmerman, D ^b   Lu, C ^c   Suykens, J A K ^a   Valentin, L ^d   Van Holsbeke, C ^b   Amant, F ^b   Vergote, I ^b   Van Huffel, S ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY HOSPITALS LEUVEN   (Belgium)

^c ABERYSTWYTH UNIVERSITY   (United Kingdom)

^d LUND UNIVERSITY HOSPITAL   (Sweden)

Author keywords

Bayesian evidence framework; Least squares support vector machines; Logistic regression; Ovarian tumor classification; Relevance vector machines; Ultrasound

Indexed keywords

ADOLESCENT; ADULT; AGED; ARTICLE; BAYES THEOREM; CLINICAL FEATURE; CONTROLLED STUDY; DATA BASE; DIAGNOSTIC ACCURACY; FEMALE; HISTOPATHOLOGY; HUMAN; MAJOR CLINICAL STUDY; OVARY CANCER; OVARY TUMOR; PREOPERATIVE PERIOD; PRIORITY JOURNAL;

ADNEXA UTERI; ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; AREA UNDER CURVE; BAYES THEOREM; DATABASES, FACTUAL; FEMALE; HUMANS; LEAST-SQUARES ANALYSIS; MIDDLE AGED; MODELS, STATISTICAL; OVARIAN NEOPLASMS; OVARY; PREOPERATIVE CARE; SENSITIVITY AND SPECIFICITY;

EID: 34249740082     PISSN: 09607692     EISSN: 14690705     Source Type: Journal    
DOI: 10.1002/uog.3996     Document Type: Article

References (35)

1. Jemal A, Siegel R, Ward E, Murray T, Xu J, Smigal C, Thun MJ. Cancer statistics, 2006. CA-Cancer J Clin 2006; 56: 106-130.
2. Vergote I, De Brabanter J, Fyles A, Bertelsen K, Einhorn N, Sevelda P, Gore ME, Karn J, Verrelst H, Sjovall K, Timmerman D, Vandewalle J, Van Gramberen M, Tropé CG. Prognostic importance of degree of differentiation and cyst rupture in stage I invasive epithelial ovarian carcinoma. Lancet 2001; 357: 176-182.
3. American Cancer Society. Cancer facts and figures 2006. American Cancer Society: Atlanta, 2006.
4. Bailey J, Murdoch J, Anderson R, Weeks J, Foy C. Stage III and IV ovarian cancer in the South West of England: Five-year outcome analysis for cases treated in 1998. Int J Gynecol Cancer 2006; 16: 25-29.
5. Jacobs I, Oram D, Fairbanks J, Turner J, Frost C, Grudzinskas JG. A risk of malignancy index incorporating CA 125, ultrasound and menopausal status for the accurate preoperative diagnosis of ovarian cancer. Br J Obstet Gynaecol 1990; 97: 922-929.
6. Lerner JP, Timor-Tritsch IE, Federman A, Abramovich G. Transvaginal ultrasonographic characterization of ovarian masses with an improved, weighted scoring system. Am J Obstet Gynecol 1994; 170: 81-85.
7. Tailor A, Jurkovic D, Bourne TH, Collins WP, Campbell S. Sonographic prediction of malignancy in adnexal masses using multivariate logistic regression analysis. Ultrasound Obstet Gynecol 1997; 10: 41-47.
8. Tailor A, Jurkovic D, Bourne TH, Collins WP, Campbell S. Sonographic prediction of malignancy in adnexal masses using an artificial neural network. Br J Obstet Gynaecol 1999; 106: 21-30.
9. Timmerman D, Bourne TH, Tailor A, Collins WP, Verrelst H, Vandenberghe K, Vergote I. A comparison of methods for preoperative discrimination between malignant and benign adnexal masses: The development of a new logistic regression model. Am J Obstet Gynecol 1999; 181: 57-65.
10. Timmerman D, Verrelst H, Bourne TH, De Moor B, Collins WP, Vergote I, Vandewalle J. Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses. Ultrasound Obstet Gynecol 1999; 13: 17-25.
11. Lu C, Van Gestel T, Suykens JAK, Van Huffel S, Vergote I, Timmerman D. Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines. Artif Intell Med 2003; 28: 281-306.
12. Timmerman D. The use of mathematical models to evaluate pelvic masses; can they beat an expert operator? Best Pract Res Clin Obstet Gynaecol 2004; 18: 91-104.
13. Timmerman D. Lack of standardization in gynecological ultrasonography (editorial). Ultrasound Obstet Gynecol 2000; 16: 395-398.
14. Ferrazzi E, Zanetta G, Dordoni D, Berlanda N, Mezzopane R, Lissoni G. Transvaginal ultrasonographic characterization of ovarian masses: comparison of five scoring systems in a multicenter study. Ultrasound Obstet Gynecol 1997; 10: 192-197.
15. Morgante G, la Marca A, Ditto A, De Leo V. Comparison of two malignancy risk indices based on serum CA125, ultrasound score and menopausal status in the diagnosis of ovarian masses. Br J Obstet Gynaecol 1999; 106: 524-527.
16. Aslam N, Banerjee S, Carr JV, Savvas M, Hooper R, Jurkovic D. Prospective evaluation of logistic regression models for the diagnosis of ovarian cancer. Obstet Gynecol 2000; 96: 75-80.
17. Mol BWJ, Boll D, De Kanter M, Heintz APM, Sijmons EA, Oei SG, Bal H, Brölmann HAM. Distinguishing the benign and malignant adnexal mass: An external validation of prognostic models. Gynecol Oncol 2001; 80: 162-167.
18. Valentin L, Hagen B, Tingulstad S, Eik-Nes S. Comparison of 'pattern recognition' and logistic regression models for discrimination between benign and malignant pelvic masses: A prospective cross validation. Ultrasound Obstet Gynecol 2001; 18: 357-365.
19. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J. Least squares support vector machines. World Scientific: Singapore, 2002.
20. Tipping ME. Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 2001; 1: 211-244.
21. Timmerman D, Valentin L, Bourne TH, Collins WP, Verrelst H, Vergote I. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: A consensus opinion from the International Ovarian Tumor Analysis (IOTA) group. Ultrasound Obstet Gynecol 2000; 16: 500-505.
22. th IEEE Symposium on Computed Based Medical Systems, Kokol P, Stiglic B, Zorman M, Zazula D (eds). IEEE Computer Society Press: Los Alamitos, 2002; 282-287.
23. Heintz APM, Odicino F, Maisonneuve P, Beller U, Benedet JL, Creasman WT, Ngan HY, Pecorelli S. Carcinoma of the ovary. Int J Gynaecol Obstet 2003; 83 (Suppl 1): 135-166.
24. Timmerman D, Testa AC, Bourne T, Ferrazzi E, Ameye L, Konstantinovic ML, Van Calster B, Collins WP, Vergote I, Van Huffel S, Valentin L. Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: A multicenter study by the International Ovarian Tumor Analysis Group. J Clin Oncol 2005; 23: 8794-8801.
25. Vapnik V. The nature of statistical learning theory. Springer: New York, 1995.
26. Pochet NLMM, Suykens JAK. Support vector machines versus logistic regression: improving prospective performance in clinical decision-making. Ultrasound Obstet Gynecol 2006; 27: 607-608.
27. Hanley JA, McNeil B. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29-36.
28. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988; 44: 837-845.
29. De Smet F, De Brabanter J, Van Den Bosch T, Pochet N, Amant F, Van Holsbeke C, Moerman P, De Moor B, Vergote I, Timmerman D. New models to predict depth of infiltration in endometrial carcinoma based on transvaginal ultrasonography. Ultrasound Obstet Gynecol 2006; 27: 664-671.
30. Bowd C, Medeiros FA, Zhang Z, Zangwill LM, Hao J, Lee T, Sejnowski TJ, Weinreb RN, Goldbaum MH. Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. Invest Ophthalmol Vis Sci 2005; 46: 1322-1329.
31. Majumder SK, Ghosh N, Gupta PK. Relevance vector machine for optical diagnosis of cancer. Lasers Surg Med 2005; 36: 323-333.
32. Suykens JAK, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett 1999; 9: 293-300.
33. MacKay DJC. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks. Netw-Comput Neural Syst 1995; 6: 469-505.
34. Van Gestel T, Suykens JAK, Lanckriet G, Lambrechts A, De Moor B, Vandewalle J. A Bayesian framework for least squares support vector machine classifiers. Neural Comput 2002; 15: 1115-1148.
35. Pelckmans K, Goethals I, De Brabanter J, Suykens JAK, De Moor B. Componentwise least squares support vector machines. In Support vector machines: Theory and applications, Wang L (ed.). Springer: Berlin, 2005; 77-98.