Vanya Van Belle

Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study.

Objectives To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours. Design Observational diagnostic study using prospectively collected clinical and ultrasound data. Setting 24 ultrasound centres in 10 countries. Participants Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients. Main outcome measures Histological classification and surgical staging of the mass. Results The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate. Conclusions The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology.

Plasma MMP1 and MMP8 expression in breast cancer: protective role of MMP8 against lymph node metastasis.

BackgroundElevated levels of matrix metalloproteinases have been found to associate with poor prognosis in various carcinomas. This study aimed at evaluating plasma levels of MMP1, MMP8 and MMP13 as diagnostic and prognostic markers of breast cancer.MethodsA total of 208 breast cancer patients, of which 21 with inflammatory breast cancer, and 42 healthy controls were included. Plasma MMP1, MMP8 and MMP13 levels were measured using ELISA and correlated with clinicopathological characteristics.ResultsMedian plasma MMP1 levels were higher in controls than in breast cancer patients (3.45 vs. 2.01 ng/ml), while no difference was found for MMP8 (10.74 vs. 10.49 ng/ml). ROC analysis for MMP1 revealed an AUC of 0.67, sensitivity of 80% and specificity of 24% at a cut-off value of 4.24 ng/ml. Plasma MMP13 expression could not be detected. No correlation was found between MMP1 and MMP8 levels. We found a trend of lower MMP1 levels with increasing tumour size (p = 0.07); and higher MMP8 levels with premenopausal status (p = 0.06) and NPI (p = 0.04). The median plasma MMP1 (p = 0.02) and MMP8 (p = 0.007) levels in the non-inflammatory breast cancer patients were almost twice as high as those found in the inflammatory breast cancer patients. Intriguingly, plasma MMP8 levels were positively associated with lymph node involvement but showed a negative correlation with the risk of distant metastasis. Both controls and lymph node negative patients (pN0) had lower MMP8 levels than patients with moderate lymph node involvement (pN1, pN2) (p = 0.001); and showed a trend for higher MMP8 levels compared to patients with extensive lymph node involvement (pN3) and a strong predisposition to distant metastasis (p = 0.11). Based on the hypothesis that blood and tissue protein levels are in reverse association, these results suggest that MMP8 in the tumour may have a protective effect against lymph node metastasis.ConclusionIn summary, we observed differences in MMP1 and MMP8 plasma levels between healthy controls and breast cancer patients as well as between breast cancer patients. Interestingly, our results suggest that MMP8 may affect the metastatic behaviour of breast cancer cells through protection against lymph node metastasis, underlining the importance of anti-target identification in drug development.

Support vector methods for survival analysis: a comparison between ranking and regression approaches

OBJECTIVE To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. METHODS The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. RESULTS We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the models discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. CONCLUSIONS This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods including only regression or both regression and ranking constraints on clinical data. On high dimensional data, the former model performs better. However, this approach does not have a theoretical link with standard statistical models for survival data. This link can be made by means of transformation models when ranking constraints are included.

Qualitative Assessment of the Progesterone Receptor and HER2 Improves the Nottingham Prognostic Index Up to 5 Years After Breast Cancer Diagnosis

PURPOSE To investigate whether the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) can improve the Nottingham Prognostic Index (NPI) in the classification of patients with primary operable breast cancer for disease-free survival (DFS). PATIENTS AND METHODS The analysis is based on 1,927 patients with breast cancer treated between 2000 and 2005 at the University Hospitals, Leuven. We compared performances of NPI with and without ER, PR and/or HER2. Validation was done on two external data sets containing 862 and 2,805 patients from Oslo (Norway) and Auckland (New Zealand), respectively. RESULTS In the Leuven cohort, median follow-up was 66 months, and 13.7% of patients experienced a breast cancer-related event. Positive staining for ER, PR, and HER2 was detected, respectively, in 86.9%, 75.5%, and 11.9% of patients. Based on multivariate Cox regression modeling, the improved NPI (iNPI) was derived as NPI - PR positivity + HER2 positivity. Validation results showed a risk group reclassification of 20% to 30% of patients when using iNPI with its optimal risk boundaries versus NPI, in a majority of patients to more appropriate risk groups. An additional 10% of patients were classified into the extreme risk groups, where clinical actions are less ambiguous. Survival curves of reclassified patients resembled more closely those for patients in the same iNPI group than those for patients in the same NPI group. CONCLUSION The addition of PR and HER2 to NPI increases its 5-year prognostic accuracy. The iNPI can be considered as a clinically useful tool for stratification of patients with breast cancer receiving standard of care.

Triple negative breast cancer: a study from the point of view of basal CK5/6 and HER-1

Aim: Basal-like breast tumours, as defined by microarrays, carry a poor prognosis and therapeutic options are limited to date. Often, these tumours are defined as oestrogen receptor (ER) negative/progesterone receptor (PR) negative/human epidermal growth factor receptor 2 (HER-2) negative (triple negative) by immunohistochemistry (IHC), but a more complete definition should include expression of basal cytokeratins (CK5/6, CK14 or CK17) and/or human epidermal growth factor receptor 1 (HER-1). The aim of this study was to investigate to what extent CK5/6 and HER-1 characterise the group of triple negative breast cancers. Methods: Expression of CK5/6 and HER-1 was studied by IHC in 25 triple negative breast carcinomas and 32 grade-matched, non-triple-negative controls. All 57 cases were further subjected to fluorescence in situ hybridisation to investigate HER-1 gene copy number. Results: CK5/6 and HER-1 expression was most frequent in triple negative tumours: 22 out of 25 cases (88.0%) expressed at least one of these markers (60.0% CK5/6 positive and 52.0% HER-1 positive). In the control group, CK5/6 and HER-1 expression was found in ER-negative but not in ER-positive tumours (ER negative/PR negative/HER-2 positive tumours: 20.0% CK5/6 positive and 46.7% HER-1 positive). HER-1 gene amplification was found in five cases only: four triple negative (16.0%) and one ER-negative control (ER negative/PR negative/HER-2 positive, 6.7%). Of interest, all five HER-1 amplified cases showed a remarkably homogeneous HER-1 expression pattern. Conclusion: Expression of CK5/6 and HER-1 is frequent in ER-negative breast cancers, in triple negative and in non-triple negative tumours. In a minority of cases, HER-1 overexpression may be caused by HER-1 gene amplification. Further studies are needed to investigate whether such cases might benefit from anti-HER-1 therapy

Extending the c-statistic to nominal polytomous outcomes: the Polytomous Discrimination Index.

Diagnostic problems in medicine are sometimes polytomous, meaning that the outcome has more than two distinct categories. For example, ovarian tumors can be benign, borderline, primary invasive, or metastatic. Extending the main measure of binary discrimination, the c-statistic or area under the ROC curve, to nominal polytomous settings is not straightforward. This paper reviews existing measures and presents the polytomous discrimination index (PDI) as an alternative. The PDI assesses all sets of k cases consisting of one case from each outcome category. For each category i (i = 1, … ,k), it is assessed whether the risk of category i is highest for the case from category i. A score of 1∕k is given per category for which this holds, yielding a set score between 0 and 1 to indicate the level of discrimination. The PDI is the average set score and is interpreted as the probability to correctly identify a case from a randomly selected category within a set of k cases. This probability can be split up by outcome category, yielding k category-specific values that result in the PDI when averaged. We demonstrate the measures on two diagnostic problems (residual mass histology after chemotherapy for testicular cancer; diagnosis of ovarian tumors). We compare the behavior of the measures on theoretical data, showing that PDI is more strongly influenced by simultaneous discrimination between all categories than by partial discrimination between pairs of categories. In conclusion, the PDI is attractive because it better matches the requirements of a measure to summarize polytomous discrimination.

A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology

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.

Assessing the discriminative ability of risk models for more than two outcome categories

The discriminative ability of risk models for dichotomous outcomes is often evaluated with the concordance index (c-index). However, many medical prediction problems are polytomous, meaning that more than two outcome categories need to be predicted. Unfortunately such problems are often dichotomized in prediction research. We present a perspective on the evaluation of discriminative ability of polytomous risk models, which may instigate researchers to consider polytomous prediction models more often. First, we suggest a “discrimination plot” as a tool to visualize the model’s discriminative ability. Second, we discuss the use of one overall polytomous c-index versus a set of dichotomous measures to summarize the performance of the model. Third, we address several aspects to consider when constructing a polytomous c-index. These involve the assessment of concordance in pairs versus sets of patients, weighting by outcome prevalence, the value related to models with random performance, the reduction to the dichotomous c-index for dichotomous problems, and interpretation. We illustrate these issues on case studies dealing with ovarian cancer (four outcome categories) and testicular cancer (three categories). We recommend the use of a discrimination plot together with an overall c-index such as the Polytomous Discrimination Index. If the overall c-index suggests that the model has relevant discriminative ability, pairwise c-indexes for each pair of outcome categories are informative. For pairwise c-indexes we recommend the ‘conditional-risk’ method which is consistent with the analytical approach of the multinomial logistic regression used to develop polytomous risk models.

Discrimination ability of prediction models for ordinal outcomes: Relationships between existing measures and a new measure

In this paper, we focus on measures to evaluate discrimination of prediction models for ordinal outcomes. We review existing extensions of the dichotomous c-index-which is equivalent to the area under the receiver operating characteristic (ROC) curve--suggest a new measure, and study their relationships. The volume under the ROC surface (VUS) scores sets of cases including one case from each outcome category. VUS considers sets as either correctly or incorrectly ordered by the model. All other existing measures assess pairs of cases. We propose an ordinal c-index (ORC) that is set-based but, contrary to VUS, scores sets more gradually by indicating the closeness of the model-based ordering to the perfect ordering. As a result, the ORC does not decrease rapidly as the number of outcome categories increases. It turns out that the ORC can be rewritten as the average of pairwise c-indexes. Hence, the ORC has both a set- and pair-based interpretation. There are several relationships between the existing measures, leading to only two types of existing measures: a prevalence-weighted average of pairwise c-indexes and the VUS. Our suggested measure ORC positions itself in between as it is set-based but turns out to equal an unweighted average of pairwise c-indexes. The measures are demonstrated through a case study on the prediction of six-month outcome after traumatic brain injury. In conclusion, the set-based nature and graded scoring system make the ORC an attractive measure with a simple interpretation, together with its prevalence-independence that is a natural property of a discrimination measure.

White box radial basis function classifiers with component selection for clinical prediction models

OBJECTIVE To propose a new flexible and sparse classifier that results in interpretable decision support systems. METHODS Support vector machines (SVMs) for classification are very powerful methods to obtain classifiers for complex problems. Although the performance of these methods is consistently high and non-linearities and interactions between variables can be handled efficiently when using non-linear kernels such as the radial basis function (RBF) kernel, their use in domains where interpretability is an issue is hampered by their lack of transparency. Many feature selection algorithms have been developed to allow for some interpretation but the impact of the different input variables on the prediction still remains unclear. Alternative models using additive kernels are restricted to main effects, reducing their usefulness in many applications. This paper proposes a new approach to expand the RBF kernel into interpretable and visualizable components, including main and two-way interaction effects. In order to obtain a sparse model representation, an iterative l1-regularized parametric model using the interpretable components as inputs is proposed. RESULTS Results on toy problems illustrate the ability of the method to select the correct contributions and an improved performance over standard RBF classifiers in the presence of irrelevant input variables. For a 10-dimensional x-or problem, an SVM using the standard RBF kernel obtains an area under the receiver operating characteristic curve (AUC) of 0.947, whereas the proposed method achieves an AUC of 0.997. The latter additionally identifies the relevant components. In a second 10-dimensional artificial problem, the underlying class probability follows a logistic regression model. An SVM with the RBF kernel results in an AUC of 0.975, as apposed to 0.994 for the presented method. The proposed method is applied to two benchmark datasets: the Pima Indian diabetes and the Wisconsin Breast Cancer dataset. The AUC is in both cases comparable to those of the standard method (0.826 versus 0.826 and 0.990 versus 0.996) and those reported in the literature. The selected components are consistent with different approaches reported in other work. However, this method is able to visualize the effect of each of the components, allowing for interpretation of the learned logic by experts in the application domain. CONCLUSIONS This work proposes a new method to obtain flexible and sparse risk prediction models. The proposed method performs as well as a support vector machine using the standard RBF kernel, but has the additional advantage that the resulting model can be interpreted by experts in the application domain.