| 1. |
- Timmerman, Dirk, et al.
(författare)
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Inclusion of CA-125 does not improve mathematical models developed to distinguish between benign and malignant adnexal tumors
- 2007
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Ingår i: Journal of Clinical Oncology. - 1527-7755. ; 25:27, s. 4194-4200
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Tidskriftsartikel (refereegranskat)abstract
- Purpose To test the value of serum CA-125 measurements alone or as part of a multimodal strategy to distinguish between malignant and benign ovarian tumors before surgery based on a large prospective multicenter study (International Ovarian Tumor Analysis). Patients and Methods Patients with at least one persistent ovarian mass preoperatively underwent transvaginal ultrasonography using gray scale imaging to assess tumor morphology and color Doppler imaging to obtain indices of blood flow. Results Data from 809 patients recruited from nine centers were included in the analysis; 567 patients (70%) had benign tumors and 242 (30%) had malignant tumors - of these 152 were primary invasive (62.8%), 52 were borderline malignant (21.5%), and 38 were metastatic (15.7%). A logistic regression model including CA-125 (M2) resulted in an area under the receiver operating characteristic curve (AUC) of 0.934 and did not outperform a published (M1) without serum CA-125 information (AUC, 0.936). Specifically designed new models including CA-125 for premenopausal women (M3) and for postmenopausal women (M4) did not perform significantly better than the model without CA-125 ( M1; AUC, 0.891 v AUC, 0.911 and AUC, 0.975 v AUC, 0.949, respectively). In postmenopausal patients, serum CA-125 alone (AUC, 0.920) and the risk of malignancy index (AUC, 0.924) performed very well. Results were very similar when the models were prospectively tested on a group of 345 new patients with adnexal masses of whom 126 had malignant tumors (37%). Conclusion Adding information on CA-125 to clinical information and ultrasound information does not improve discrimination of mathematical models between benign and malignant adnexal masses.
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| 2. |
- Van Calster, Ben, et al.
(författare)
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Discrimination between benign and malignant adnexal masses by specialist ultrasound examination versus serum CA-125
- 2007
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 1460-2105 .- 0027-8874. ; 99:22, s. 1706-1714
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Tidskriftsartikel (refereegranskat)abstract
- Background Subjective evaluation of gray-scale and Doppler ultrasound findings (i. e., pattern recognition) by an experienced examiner and preoperative serum levels of CA-125 can both discriminate benign from malignant adnexal ( i. e., ovarian, paraovarian, or tubal) masses. We compared the diagnostic performance of these methods in a large multicenter study. Methods In a prospective multicenter study-the International Ovarian Tumor Analysis-1066 women with a persistent adnexal mass underwent transvaginal gray-scale and color Doppler ultrasound examinations by an experienced examiner within 120 days of surgery. Pattern recognition was used to classify a mass as benign or malignant. Of these women, 809 also had blood collected preoperatively for measurement of serum CA-125. Various levels of CA-125 were used as cutoffs to classify masses. Results from both assays were then compared with histologic findings after surgery. Results Pattern recognition correctly classified 93% (95% confidence interval [CI]=90.9% to 94.6%) of the tumors as benign or malignant. Serum CA-125 correctly classified at best 83% ( 95% CI=80.3% to 85.6%) of the masses. Histologic diagnoses that were most often misclassified by CA-125 were fibroma, endometrioma, and abscess ( false-positive results) and borderline tumor ( false-negative results). Pattern recognition correctly classified 86% ( 95% CI=81.1% to 90.4%) of masses of these four histologic types as being benign or malignant, whereas a serum CA-125 at a cutoff of 30 U/mL correctly classified 41% ( 95% CI=34.4% to 47.5%) of them. Pattern recognition assigned a correct specific histologic diagnosis to 333 (59%, 95% CI=54.5% to 62.8%) of the 567 benign lesions. Conclusion Pattern recognition was superior to serum CA-125 for discrimination between benign and malignant adnexal masses.
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| 3. |
- Van Holsbeke, Caroline, et al.
(författare)
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External validation of mathematical models to distinguish between benign and malignant adnexal tumors: A multicenter study by the International Ovarian Tumor Analysis group
- 2007
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 13:15, s. 4440-4447
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Several scoring systems have been developed to distinguish between benign and malignant adnexal tumors. However, few of them have been externally validated in new populations. Our aim was to compare their performance on a prospectively collected large multicenter data set. Experimental Design: In phase I of the International Ovarian Tumor Analysis multicenter study, patients with a persistent adnexal mass were examined with transvaginal ultrasound and color Doppler imaging. More than 50 end point variables were prospectively recorded for analysis. The outcome measure was the histologic classification of excised tissue as malignant or benign. We used the International Ovarian Tumor Analysis data to test the accuracy of previously published scoring systems. Receiver operating characteristic curves were constructed to compare the performance of the models. Results: Data from 1,066 patients were included; 800 patients (75%) had benign tumors and 266 patients (25%) had malignant tumors. The morphologic scoring system used by Lerner gave an area under the receiver operating characteristic curve (AUC) of 0.68, whereas the multimodal risk of malignancy index used by Jacobs gave an AUC of 0.88. The corresponding values for logistic regression and artificial neural network models varied between 0.76 and 0.91 and between 0.87 and 0.90, respectively. Advanced kernel-based classifiers gave an AUC of up to 0.92. Conclusion: The performance of the risk of malignancy index was similar to that of most logistic regression and artificial neural network models. The best result was obtained with a relevance vector machine with radial basis function kernel. Because the models were tested on a large multicenter data set, results are likely to be generally applicable.
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