| 1. |
- Van Calster, Ben, et al.
(författare)
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A Novel Approach to Predict the Likelihood of Specific Ovarian Tumor Pathology Based on Serum CA-125: A Multicenter Observational Study.
- 2011
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Ingår i: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. - 1538-7755. ; 20, s. 2420-2428
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The CA-125 tumor marker has limitations when used to distinguish between benign and malignant ovarian masses. We therefore establish likelihood curves of six subgroups of ovarian pathology based on CA-125 and menopausal status.METHODS: This cross-sectional study conducted by the International Ovarian Tumor Analysis group involved 3,511 patients presenting with a persistent adnexal mass that underwent surgical intervention. CA-125 distributions for six tumor subgroups (endometriomas and abscesses, other benign tumors, borderline tumors, stage I invasive cancers, stage II-IV invasive cancers, and metastatic tumors) were estimated using kernel density estimation with stratification for menopausal status. Likelihood curves for the tumor subgroups were derived from the distributions.RESULTS: Endometriomas and abscesses were the only benign pathologies with median CA-125 levels above 20 U/mL (43 and 45, respectively). Borderline and invasive stage I tumors had relatively low median CA-125 levels (29 and 81 U/mL, respectively). The CA-125 distributions of stage II-IV invasive cancers and benign tumors other than endometriomas or abscesses were well separated; the distributions of the other subgroups overlapped substantially. This held for premenopausal and postmenopausal patients. Likelihood curves and reference tables comprehensibly show how subgroup likelihoods change with CA-125 and menopausal status.Conclusions and Impact: Our results confirm the limited clinical value of CA-125 for preoperative discrimination between benign and malignant ovarian pathology. We have shown that CA-125 may be used in a different way. By using likelihood reference tables, we believe clinicians will be better able to interpret preoperative serum CA-125 results in patients with adnexal masses. Cancer Epidemiol Biomarkers Prev; ©2011 AACR.
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| 2. |
- 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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| 3. |
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| 4. |
- Van Belle, Vanya M. C. A., et al.
(författare)
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A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology
- 2012
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 7:3
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Tidskriftsartikel (refereegranskat)abstract
- Background: Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients. Methods and Findings: We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems. Conclusions: The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data.
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| 5. |
- 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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| 6. |
- Van Calster, Ben, et al.
(författare)
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Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models
- 2010
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Background: Hitherto, risk prediction models for preoperative ultrasound-based diagnosis of ovarian tumors were dichotomous (benign versus malignant). We develop and validate polytomous models (models that predict more than two events) to diagnose ovarian tumors as benign, borderline, primary invasive or metastatic invasive. The main focus is on how different types of models perform and compare. Methods: A multi-center dataset containing 1066 women was used for model development and internal validation, whilst another multi-center dataset of 1938 women was used for temporal and external validation. Models were based on standard logistic regression and on penalized kernel-based algorithms (least squares support vector machines and kernel logistic regression). We used true polytomous models as well as combinations of dichotomous models based on the 'pairwise coupling' technique to produce polytomous risk estimates. Careful variable selection was performed, based largely on cross-validated c-index estimates. Model performance was assessed with the dichotomous c-index (i.e. the area under the ROC curve) and a polytomous extension, and with calibration graphs. Results: For all models, between 9 and 11 predictors were selected. Internal validation was successful with polytomous c-indexes between 0.64 and 0.69. For the best model dichotomous c-indexes were between 0.73 (primary invasive vs metastatic) and 0.96 (borderline vs metastatic). On temporal and external validation, overall discrimination performance was good with polytomous c-indexes between 0.57 and 0.64. However, discrimination between primary and metastatic invasive tumors decreased to near random levels. Standard logistic regression performed well in comparison with advanced algorithms, and combining dichotomous models performed well in comparison with true polytomous models. The best model was a combination of dichotomous logistic regression models. This model is available online. Conclusions: We have developed models that successfully discriminate between benign, borderline, and invasive ovarian tumors. Methodologically, the combination of dichotomous models was an interesting approach to tackle the polytomous problem. Standard logistic regression models were not outperformed by regularized kernel-based alternatives, a finding to which the careful variable selection procedure will have contributed. The random discrimination between primary and metastatic invasive tumors on temporal/external validation demonstrated once more the necessity of validation studies.
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| 7. |
- Van Calster, Ben, et al.
(författare)
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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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Ingår i: NEURAL COMPUTING & APPLICATIONS. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 17:5-6, s. 489-500
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Konferensbidrag (refereegranskat)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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| 8. |
- Van Holsbeke, Caroline, et al.
(författare)
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External Validation of Diagnostic Models to Estimate the Risk of Malignancy in Adnexal Masses
- 2012
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 18:3, s. 815-825
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses. Experimental Design: We externally validated the diagnostic performance of 11 models developed by the International Ovarian Tumor Analysis (IOTA) group and 12 other (non-IOTA) models on 997 prospectively collected patients. The non-IOTA models included the original risk of malignancy index (RMI), three modified versions of the RMI, six logistic regression models, and two artificial neural networks. The ability of the models to discriminate between benign and malignant adnexal masses was expressed as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and likelihood ratios (LR+, LR-). Results: Seven hundred and forty-two (74%) benign and 255 (26%) malignant masses were included. The IOTA models did better than the non-IOTA models (AUCs between 0.941 and 0.956 vs. 0.839 and 0.928). The difference in AUC between the best IOTA and the best non-IOTA model was 0.028 [95% confidence interval (CI), 0.011-0.044]. The AUC of the RMI was 0.911 (difference with the best IOTA model, 0.044; 95% CI, 0.024-0.064). The superior performance of the IOTA models was most pronounced in premenopausal patients but was also observed in postmenopausal patients. IOTA models were better able to detect stage I ovarian cancer. Conclusion: External validation shows that the IOTA models outperform other models, including the current reference test RMI, for discriminating between benign and malignant adnexal masses. Clin Cancer Res; 18(3); 815-25. (C)2011 AACR.
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| 9. |
- 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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| 10. |
- Van Holsbeke, Caroline, et al.
(författare)
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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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Ingår i: Clinical Cancer Research. - 1078-0432. ; 15:2, s. 684-691
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Tidskriftsartikel (refereegranskat)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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