| 1. |
- Timmerman, Dirk, et al.
(author)
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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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In: Journal of Clinical Oncology. - 1527-7755. ; 25:27, s. 4194-4200
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Journal article (peer-reviewed)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. |
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| 3. |
- Valentin, Lil, et al.
(author)
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Ultrasound characteristics of different types of adnexal malignancies.
- 2006
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In: Gynecologic Oncology. - : Elsevier BV. - 1095-6859 .- 0090-8258. ; 102:1, s. 41-48
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Journal article (peer-reviewed)abstract
- Objective. To describe ultrasound characteristics of adnexal malignancies, i.e., borderline ovarian tumors, primary invasive ovarian epithelial cancer stage 1, primary invasive ovarian epithelial cancer stages 2–4, rare types of malignancy, and metastatic tumors. Methods. In a prospective international study involving nine European ultrasound centers, 1066 women with a pelvic mass judged to be of adnexal origin underwent transvaginal gray scale and color Doppler ultrasound examination by a skilled examiner before surgery. A standardized examination technique and predefined definitions of ultrasound characteristics were used. Results. Of 1066 masses, 266 were malignant and are included: 55 ovarian borderline tumors, 144 primary invasive epithelial ovarian cancers (42 stage 1, 102 stages 2–4), 25 rare malignancies, and 42 metastatic tumors. Most (56%) metastatic tumors and most (60%) rare types of tumor were solid and richly vascularized at color Doppler ultrasound examination (on a scale ranging from 1 to 4, color score based on subjective evaluation was 3 or 4 in 88% and 86%, respectively). Borderline ovarian tumors and stage 1 primary invasive ovarian epithelial cancers differed from stages 2–4 primary invasive ovarian epithelial cancers: they were larger (median volume 375 ml and 695 ml vs. 209 ml; P = 0.0213 and 0.0001), a larger proportion contained papillary projections (64% and 67% vs. 41%; P = 0.0072 and 0.0054), they were more often multilocular cysts without solid components (18% and 14% vs. 2%; P < 0.0017 and 0.0204), but they were less often purely solid (5% and 7% vs. 38%; P ≤ 0.0001 and 0.0005). With increasing degree of invasiveness – from borderline epithelial ovarian tumors via stage 1 invasive epithelial ovarian tumors to stages 2–4 invasive epithelial ovarian tumors – ascites became more common (9% vs. 31% vs. 61%; P = 0.0082, <0.0001, and 0.0017), and, among tumors with solid components (n = 179), the proportion of tumor consisting of solid tissue increased (median 2%–10%–34%; P = 0.0212, <0.0001, and 0.0003). Conclusion. Papillary projections are characteristic of borderline tumors and stage 1 primary invasive epithelial ovarian cancer. A small proportion of solid tissue at ultrasound examination makes a malignant mass more likely to be a borderline tumor or a stage 1 epithelial ovarian cancer than an advanced ovarian cancer, a metastasis, or a rare type of tumor.
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| 4. |
- Van Calster, Ben, et al.
(author)
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Discrimination between benign and malignant adnexal masses by specialist ultrasound examination versus serum CA-125
- 2007
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In: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 1460-2105 .- 0027-8874. ; 99:22, s. 1706-1714
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Journal article (peer-reviewed)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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| 5. |
- Van Calster, Ben, et al.
(author)
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Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery
- 2008
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In: NEURAL COMPUTING & APPLICATIONS. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 17:5-6, s. 489-500
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Conference paper (peer-reviewed)abstract
- In this paper, we applied Bayesian multi-layer perceptrons (MLP) using the evidence procedure to predict malignancy of ovarian masses in a large (n = 1,066) multi-centre data set. Automatic relevance determination (ARD) was used to select the most relevant inputs. Fivefold cross-validation (5CV) and repeated 5CV was used to select the optimal combination of input set and number of hidden neurons. Results indicate good performance of the models with area under the receiver operating characteristic curve values of 0.93-0.94 on independent data. Comparison with a linear benchmark model and a previously developed logistic regression model shows that the present problem is very well linearly separable. A resampling analysis further shows that the number of hidden neurons specified in the ARD analyses for input selection may influence model performance. This paper shows that Bayesian MLPs, although not frequently used, are a useful tool for detecting malignant ovarian tumours.
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| 6. |
- Van Holsbeke, Caroline, et al.
(author)
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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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In: Clinical Cancer Research. - 1078-0432. ; 13:15, s. 4440-4447
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Journal article (peer-reviewed)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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| 7. |
- Van Holsbeke, Caroline, et al.
(author)
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Prospective Internal Validation of Mathematical Models to Predict Malignancy in Adnexal Masses: Results from the International Ovarian Tumor Analysis Study
- 2009
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In: Clinical Cancer Research. - 1078-0432. ; 15:2, s. 684-691
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Journal article (peer-reviewed)abstract
- Purpose: To prospectively test the mathematical models for calculation of the risk of malignancy in adnexal masses that were developed on the International Ovarian Tumor Analysis (IOTA) phase 1 data set on a new data set and to compare their performance with that of pattern recognition, our standard method. Methods: Three IOTA centers included 507 new patients who all underwent a transvaginal ultrasound using the standardized IOTA protocol. The outcome measure was the histologic classification of excised tissue. The diagnostic performance of 11 mathematical models that had been developed on the phase 1 data set and of pattern recognition was expressed as area under the receiver operating characteristic curve (AUC) and as sensitivity and specificity when using the cutoffs recommended in the studies where the models had been created. For pattern recognition, an AUC was made based on level of diagnostic confidence, Results: All IOTA models performed very well and quite similarly, with sensitivity and specificity ranging between 92% and 96% and 74% and 84%, respectively, and AUCs between 0.945 and 0.950. A least squares support vector machine with linear kernel and a logistic regression model had the largest AUCs. For pattern recognition, the AUC was 0.963, sensitivity was 90.2%, and specificity was 92.9%. Conclusion: This internal validation of mathematical models to estimate the malignancy risk in adnexal tumors shows that the IOTA models had a diagnostic performance similar to that in the original data set. Pattern recognition used by an expert sonologist remains the best method, although the difference in performance between the best mathematical model is not large.
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