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Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: A multicenter study by the International Ovarian Tumor Analysis Group

Timmerman, D; Testa, AC; Bourne, T; Ferrazzi, E; Ameye, L; Konstantinovic, ML; Van Calster, B; Collins, WP; Vergote, I and Van Huffel, S, et al. (2005) In Journal of Clinical Oncology 23(34). p.8794-8801
Abstract
Purpose To collect data for the development of a more universally useful logistic regression model to distinguish between a malignant and benign adnexal tumor before surgery. Patients and Methods Patients had at least one persistent mass. More than 50 clinical and sonographic end points were defined and recorded for analysis. The outcome measure was the histologic classification of excised tissues as malignant or benign. Results Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent prognostic variables for the logistic regression model were as follows: (1) personal history of ovarian cancer, (2)... (More)
Purpose To collect data for the development of a more universally useful logistic regression model to distinguish between a malignant and benign adnexal tumor before surgery. Patients and Methods Patients had at least one persistent mass. More than 50 clinical and sonographic end points were defined and recorded for analysis. The outcome measure was the histologic classification of excised tissues as malignant or benign. Results Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent prognostic variables for the logistic regression model were as follows: (1) personal history of ovarian cancer, (2) hormonal therapy, (3) age, (4) maximum diameter of lesion, (5) pain, (6) ascites, (7) blood flow within a solid papillary projection, (8) presence of an entirely solid tumor, (9) maximal diameter of solid component, (10) irregular internal cyst walls, (11) acoustic shadows, and (12) a color score of intratumoral blood flow. The model containing all 12 variables (M1) gave an area under the receiver operating characteristic curve of 0.95 for the development data set (n = 754 patients). The corresponding value for the test data set (n = 312 patients) was 0.94; and a probability cutoff value of .10 gave a sensitivity of 93% and a specificity of 76%. Conclusion Because the model was constructed from multicenter data, it is more likely to be generally applicable. The effectiveness of the model will be tested prospectively at different centers. (Less)
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Journal of Clinical Oncology
volume
23
issue
34
pages
8794 - 8801
publisher
American Society of Clinical Oncology
external identifiers
  • pmid:16314639
  • wos:000233690200034
  • scopus:33644836410
ISSN
1527-7755
DOI
10.1200/JCO.2005.01.7632
language
English
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yes
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6db90b10-8be0-462c-abe7-b9f3d71eb35c (old id 894413)
date added to LUP
2008-01-21 12:07:24
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@article{6db90b10-8be0-462c-abe7-b9f3d71eb35c,
  abstract     = {Purpose To collect data for the development of a more universally useful logistic regression model to distinguish between a malignant and benign adnexal tumor before surgery. Patients and Methods Patients had at least one persistent mass. More than 50 clinical and sonographic end points were defined and recorded for analysis. The outcome measure was the histologic classification of excised tissues as malignant or benign. Results Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent prognostic variables for the logistic regression model were as follows: (1) personal history of ovarian cancer, (2) hormonal therapy, (3) age, (4) maximum diameter of lesion, (5) pain, (6) ascites, (7) blood flow within a solid papillary projection, (8) presence of an entirely solid tumor, (9) maximal diameter of solid component, (10) irregular internal cyst walls, (11) acoustic shadows, and (12) a color score of intratumoral blood flow. The model containing all 12 variables (M1) gave an area under the receiver operating characteristic curve of 0.95 for the development data set (n = 754 patients). The corresponding value for the test data set (n = 312 patients) was 0.94; and a probability cutoff value of .10 gave a sensitivity of 93% and a specificity of 76%. Conclusion Because the model was constructed from multicenter data, it is more likely to be generally applicable. The effectiveness of the model will be tested prospectively at different centers.},
  author       = {Timmerman, D and Testa, AC and Bourne, T and Ferrazzi, E and Ameye, L and Konstantinovic, ML and Van Calster, B and Collins, WP and Vergote, I and Van Huffel, S and Valentin, Lil},
  issn         = {1527-7755},
  language     = {eng},
  number       = {34},
  pages        = {8794--8801},
  publisher    = {American Society of Clinical Oncology},
  series       = {Journal of Clinical Oncology},
  title        = {Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: A multicenter study by the International Ovarian Tumor Analysis Group},
  url          = {http://dx.doi.org/10.1200/JCO.2005.01.7632},
  volume       = {23},
  year         = {2005},
}