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Billiet L, Van Huffel S, Van Belle V. Interval Coded Scoring: a toolbox for interpretable scoring systems. PeerJ Comput Sci 2018; 4:e150. [PMID: 33816804 PMCID: PMC7924521 DOI: 10.7717/peerj-cs.150] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 02/28/2018] [Indexed: 06/12/2023]
Abstract
Over the last decades, clinical decision support systems have been gaining importance. They help clinicians to make effective use of the overload of available information to obtain correct diagnoses and appropriate treatments. However, their power often comes at the cost of a black box model which cannot be interpreted easily. This interpretability is of paramount importance in a medical setting with regard to trust and (legal) responsibility. In contrast, existing medical scoring systems are easy to understand and use, but they are often a simplified rule-of-thumb summary of previous medical experience rather than a well-founded system based on available data. Interval Coded Scoring (ICS) connects these two approaches, exploiting the power of sparse optimization to derive scoring systems from training data. The presented toolbox interface makes this theory easily applicable to both small and large datasets. It contains two possible problem formulations based on linear programming or elastic net. Both allow to construct a model for a binary classification problem and establish risk profiles that can be used for future diagnosis. All of this requires only a few lines of code. ICS differs from standard machine learning through its model consisting of interpretable main effects and interactions. Furthermore, insertion of expert knowledge is possible because the training can be semi-automatic. This allows end users to make a trade-off between complexity and performance based on cross-validation results and expert knowledge. Additionally, the toolbox offers an accessible way to assess classification performance via accuracy and the ROC curve, whereas the calibration of the risk profile can be evaluated via a calibration curve. Finally, the colour-coded model visualization has particular appeal if one wants to apply ICS manually on new observations, as well as for validation by experts in the specific application domains. The validity and applicability of the toolbox is demonstrated by comparing it to standard Machine Learning approaches such as Naive Bayes and Support Vector Machines for several real-life datasets. These case studies on medical problems show its applicability as a decision support system. ICS performs similarly in terms of classification and calibration. Its slightly lower performance is countered by its model simplicity which makes it the method of choice if interpretability is a key issue.
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Affiliation(s)
- Lieven Billiet
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- imec, Leuven, Belgium
| | - Sabine Van Huffel
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- imec, Leuven, Belgium
| | - Vanya Van Belle
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
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Van Belle V, Van Calster B. Risk calculation charts for multiclass prediction models. Arch Public Health 2015. [PMCID: PMC4582204 DOI: 10.1186/2049-3258-73-s1-p28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Abstract
OBJECTIVE Risk prediction models can assist clinicians in making decisions. To boost the uptake of these models in clinical practice, it is important that end-users understand how the model works and can efficiently communicate its results. We introduce novel methods for interpretable model visualization. METHODS The proposed visualization techniques are applied to two prediction models from the Framingham Heart Study for the prediction of intermittent claudication and stroke after atrial fibrillation. We represent models using color bars, and visualize the risk estimation process for a specific patient using patient-specific contribution charts. RESULTS The color-based model representations provide users with an attractive tool to instantly gauge the relative importance of the predictors. The patient-specific representations allow users to understand the relative contribution of each predictor to the patient's estimated risk, potentially providing insightful information on which to base further patient management. Extensions towards non-linear models and interactions are illustrated on an artificial dataset. CONCLUSION The proposed methods summarize risk prediction models and risk predictions for specific patients in an alternative way. These representations may facilitate communication between clinicians and patients.
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Affiliation(s)
- Vanya Van Belle
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- iMinds Medical IT, KU Leuven, Leuven, Belgium
- * E-mail:
| | - Ben Van Calster
- KU Leuven, Department of Development and Regeneration, Leuven, Belgium
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Van Calster B, Van Hoorde K, Valentin L, Testa AC, Fischerova D, Van Holsbeke C, Savelli L, Franchi D, Epstein E, Kaijser J, Van Belle V, Czekierdowski A, Guerriero S, Fruscio R, Lanzani C, Scala F, Bourne T, Timmerman D. 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. BMJ 2014; 349:g5920. [PMID: 25320247 PMCID: PMC4198550 DOI: 10.1136/bmj.g5920] [Citation(s) in RCA: 246] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
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.
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Affiliation(s)
- Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 7003, 3000 Leuven, Belgium
| | - Kirsten Van Hoorde
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium iMinds Medical Information Technologies, KU Leuven, Leuven, Belgium
| | - Lil Valentin
- Department of Obstetrics and Gynaecology, Skåne University Hospital Malmö, Lund University, Malmö, Sweden
| | - Antonia C Testa
- Department of Oncology, Catholic University of the Sacred Heart, Rome, Italy
| | - Daniela Fischerova
- Gynaecological Oncology Center, Department of Obstetrics and Gynaecology, Charles University, Prague, Czech Republic
| | | | - Luca Savelli
- Gynaecology and Reproductive Medicine Unit, S Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy
| | - Dorella Franchi
- Preventive Gynaecology Unit, Division of Gynaecology, European Institute of Oncology, Milan, Italy
| | - Elisabeth Epstein
- Department of Obstetrics and Gynaecology, Karolinska University Hospital, Stockholm, Sweden
| | - Jeroen Kaijser
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 7003, 3000 Leuven, Belgium Department of Obstetrics and Gynaecology, University Hospitals Leuven, Leuven, Belgium
| | - Vanya Van Belle
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium iMinds Medical Information Technologies, KU Leuven, Leuven, Belgium
| | - Artur Czekierdowski
- 1st Department of Gynaecological Oncology and Gynaecology, Medical University in Lublin, Lublin, Poland
| | - Stefano Guerriero
- Department of Obstetrics and Gynaecology, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy
| | - Robert Fruscio
- Clinic of Obstetrics and Gynaecology, University of Milan-Bicocca, San Gerardo Hospital, Monza, Italy
| | - Chiara Lanzani
- Department of Woman, Mother and Neonate, Buzzi Children's Hospital, Biological and Clinical School of Medicine, University of Milan, Milan, Italy
| | - Felice Scala
- Department of Gynaecologic Oncology, Istituto Nazionale Tumori, Naples, Italy
| | - Tom Bourne
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 7003, 3000 Leuven, Belgium Department of Obstetrics and Gynaecology, University Hospitals Leuven, Leuven, Belgium Queen Charlotte's and Chelsea Hospital, Imperial College, London, UK
| | - Dirk Timmerman
- Department of Development and Regeneration, KU Leuven, Herestraat 49 box 7003, 3000 Leuven, Belgium Department of Obstetrics and Gynaecology, University Hospitals Leuven, Leuven, Belgium
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Van Belle V, Lisboa P. White box radial basis function classifiers with component selection for clinical prediction models. Artif Intell Med 2014; 60:53-64. [DOI: 10.1016/j.artmed.2013.10.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Revised: 10/04/2013] [Accepted: 10/08/2013] [Indexed: 10/26/2022]
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Bottomley C, Van Belle V, Kirk E, Van Huffel S, Timmerman D, Bourne T. Accurate prediction of pregnancy viability by means of a simple scoring system. Hum Reprod 2012; 28:68-76. [DOI: 10.1093/humrep/des352] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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Van Calster B, Van Belle V, Vergouwe Y, Timmerman D, Van Huffel S, Steyerberg EW. Extending the c-statistic to nominal polytomous outcomes: the Polytomous Discrimination Index. Stat Med 2012; 31:2610-26. [PMID: 22733650 DOI: 10.1002/sim.5321] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2010] [Accepted: 12/23/2011] [Indexed: 11/06/2022]
Abstract
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.
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Affiliation(s)
- Ben Van Calster
- Department of Reproduction, Development, and Regeneration, KU Leuven - University of Leuven, Leuven, Belgium.
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Van Calster B, Van Belle V, Vergouwe Y, Steyerberg EW. Discrimination ability of prediction models for ordinal outcomes: relationships between existing measures and a new measure. Biom J 2012; 54:674-85. [PMID: 22711459 DOI: 10.1002/bimj.201200026] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2012] [Revised: 04/12/2012] [Accepted: 04/23/2012] [Indexed: 11/08/2022]
Abstract
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.
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Affiliation(s)
- Ben Van Calster
- Department of Development, and Regeneration, KU Leuven--University of Leuven, Herestraat 49 box 7003, B-3000 Leuven, Belgium.
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Van Belle V, Pelckmans K, Van Huffel S, Suykens JAK. Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artif Intell Med 2011; 53:107-18. [PMID: 21821401 DOI: 10.1016/j.artmed.2011.06.006] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2009] [Revised: 05/11/2011] [Accepted: 06/18/2011] [Indexed: 11/28/2022]
Abstract
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 model's 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.
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Affiliation(s)
- Vanya Van Belle
- Department of Electrical Engineering (ESAT), Division SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg, Belgium.
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Van Belle V, Van Calster B, Brouckaert O, Vanden Bempt I, Pintens S, Harvey V, Murray P, Naume B, Wiedswang G, Paridaens R, Moerman P, Amant F, Leunen K, Smeets A, Drijkoningen M, Wildiers H, Christiaens MR, Vergote I, Van Huffel S, Neven P. Qualitative Assessment of the Progesterone Receptor and HER2 Improves the Nottingham Prognostic Index Up to 5 Years After Breast Cancer Diagnosis. J Clin Oncol 2010; 28:4129-34. [DOI: 10.1200/jco.2009.26.4200] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PurposeTo 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 MethodsThe 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.ResultsIn 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.ConclusionThe 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.
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Affiliation(s)
- Vanya Van Belle
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Ben Van Calster
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Olivier Brouckaert
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Isabelle Vanden Bempt
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Saskia Pintens
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Vernon Harvey
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Paula Murray
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Björn Naume
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Gro Wiedswang
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Robert Paridaens
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Philippe Moerman
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Frederic Amant
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Karin Leunen
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Ann Smeets
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Maria Drijkoningen
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Hans Wildiers
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Marie-Rose Christiaens
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Ignace Vergote
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Sabine Van Huffel
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
| | - Patrick Neven
- From the Katholieke Universiteit Leuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven; Virga Jesse Hospital, Hasselt, Belgium; Auckland Breast Cancer Registry, Greenlane Clinical Centre; Regional Cancer Centre, Auckland City Hospital, Auckland, New Zealand; Ullevål University; and Norwegian Radium Hospital, Oslo, Norway
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Van Belle V, Van Calster B, Wildiers H, Van Huffel S, Neven P. Lymph Node Ratio Better Predicts Disease-Free Survival in Node-Positive Breast Cancer Than the Number of Positive Lymph Nodes. J Clin Oncol 2009; 27:e150-1; author reply e152. [DOI: 10.1200/jco.2009.24.0044] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Vanya Van Belle
- Department of Electrical Engineering, Division SCD, KULeuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Electrical Engineering, Division SCD, KULeuven; Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven, Belgium
| | - Hans Wildiers
- Multidisciplinary Breast Centre; Department of General Medical Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering, Division SCD, KULeuven, Leuven, Belgium
| | - Patrick Neven
- Multidisciplinary Breast Centre; Department of Gynaecological Oncology, University Hospitals Leuven, Leuven, Belgium
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Neven P, Brouckaert O, Van Belle V, Vanden Bempt I, Hendrickx W, Cho H, Deraedt K, Van Calster B, Van Huffel S, Moerman P, Amant F, Leunen K, Smeets A, Wildiers H, Paridaens R, Vergote I, Christiaens MR. In early-stage breast cancer, the estrogen receptor interacts with correlation between human epidermal growth factor receptor 2 status and age at diagnosis, tumor grade, and lymph node involvement. J Clin Oncol 2008; 26:1768-9; author reply 1769-71. [PMID: 18519273 DOI: 10.1200/jco.2007.15.6141] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Decock J, Hendrickx W, Vanleeuw U, Van Belle V, Van Huffel S, Christiaens MR, Ye S, Paridaens R. Plasma MMP1 and MMP8 expression in breast cancer: protective role of MMP8 against lymph node metastasis. BMC Cancer 2008; 8:77. [PMID: 18366705 PMCID: PMC2278147 DOI: 10.1186/1471-2407-8-77] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2007] [Accepted: 03/20/2008] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Elevated 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. METHODS A 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. RESULTS Median 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. CONCLUSION In 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.
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Affiliation(s)
- Julie Decock
- Laboratory for Experimental Oncology (LEO), K,U,Leuven, Campus University Hospital Gasthuisberg, O&N1 bus 815, Herestraat 49, 3000 Leuven, Belgium.
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Neven P, Van Belle V, Brouckaert O, Pintens S, Paridaens R, Christiaens MR, Deraedt K, Moerman P. Are gene signatures better than traditional clinical factors? Lancet Oncol 2008; 9:197-8; author reply 198-9. [DOI: 10.1016/s1470-2045(08)70047-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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