1,721,209 research outputs found
Ontwikkelen van evidence-based strategieën voor diagnostiek en klinischhandelen bij eierstoktumoren: resultaten van de International OvarianTumour Analysis (IOTA) studies
No full textWhilst patients with ovarian cancer clearly benefit from centralised, comprehensive care in dedicated cancer centres, unfortunately the majority of them still do not receive appropriate specialist treatment. Any improvement in the accuracy of current triaging and referral pathways whether using new imaging tests or biomarkers would therefore be of value in order to optimise the appropriate selection of patients for such care (Chapter 1).
An analysis of the current evidence shows that such diagnostic tests are now available, but still await recognition, acceptance and widespread adoption. It is therefore to be hoped that present guidance relating to the classification of ovarian masses will soon become more “evidence-based”. These include the International Ovarian Tumour Analysis (IOTA) LR2 risk prediction model and ultrasound-based IOTA Simple Rules (SR) (Chapter 2).
Based on a comprehensive recent meta-analysis both currently offer the optimal “evidence-based” approach to discriminating between cancer and benign conditions in women with adnexal tumours needing surgery (Chapter 3). The IOTA LR2 risk model and SR are reliable diagnostic tests having been shown to maintain a high sensitivity for cancer after independent external validation and both temporal and external validation by the IOTA group in the hands of examiners with various levels of ultrasound expertise (Chapter 4 and 5). Both diagnostic approaches also offer more accurate triage compared to the current standard of care diagnostic test Risk of Malignancy Index (RMI).
The development of the IOTA Assessment of Different NEoplasias in the AdneXa (ADNEX) multiclass risk prediction model represents an important step forward towards more individualised patient care in this area (Chapter 6). The ADNEX model is novel and enables the more specific subtyping of adnexal cancers (i.e. borderline tumours, stage 1 invasive ovarian cancer, stage II-IV invasive ovarian cancer, and secondary metastatic malignant tumours) and shares similar levels of accuracy to IOTA LR2 and SR for basic discrimination between cancer and benign disease. Its use has the potential to further improve and fine-tune management decisions and so reduce the morbidity and mortality associated with adnexal pathology.
Biomarkers are also attractive, popular and recommended tools to support clinical judgment of the nature of an adnexal mass. At present two novel commercial biomarkerbased algorithms have been developed in order to improve the poor accuracy of serum CA125; the Multivariate Index Assay and the ROMA algorithm. The latter utilises levels of CA125 and a new emerging epithelial biomarker human-epididymis-protein-4 (HE4) combined with the patient’s menopausal status to classify patients as at high or low risk for malignancy (Chapter 7). This test rapidly gained widespread attention, as evidenced by its numerous validation studies throughout the world. However, based on the findings of this thesis both commercial tests still seem redundant for preoperative diagnosis if good quality transvaginal ultrasonography (TVS) is available and the IOTA models are used in the correct manner (Chapter 3 and Chapter 7).
TVS is accepted as the most appropriate initial imaging investigation to identify and characterise any mass if present in women suspected of having adnexal pathology (chapter 8). Other imaging modalities such as computed-tomography (CT) and [18F]-fluorodeoxyglucose positron emission tomography ([18F]-FDG-PET) both lack accuracy for preoperative diagnosis and are more useful tools for staging of malignant disease and the assessment of ovarian cancer recurrence. Magnetic resonance imaging (MRI) may have a limited role to play in characterising so-called “difficult masses” after ultrasound review. The IOTA prediction models and rules offer new criteria that we can use to clearly define complex or “difficult to classify” adnexal masses to focus the role for second-line imaging tests such as conventional MRI combined with dynamic contrastenhanced (DCE) or diffusion-weighted (DWI) sequences on masses where further tests other than ultrasonography would be of value to minimise healthcare costs (Chapter 8).
The IOTA study has made significant progress in relation to the preoperative classification of adnexal masses, however what is now needed is to see if these or new diagnostic tools will have a positive influence on both clinical management and patient outcomes in true interventional studies; can assist clinicians to select patients with adnexal masses that are suitable for expectant management; and that will work in all health care settings (i.e. primary vs secondary vs tertiary care). The future agenda of the IOTA project will focus on these important themes (Chapter 9).status: Publishe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Klinische predictiemodellen op basis van multicentrische studies: methoden voor geclusterde data
No full textRisk prediction models are developed to assist doctors in diagnosing patients, decision-making, counseling patients or providing a prognosis. To enhance the generalizability of risk models, researchers increasingly collect patient data in different settings and join forces in multicenter collaborations. The resulting datasets are clustered: patients from one center may have more similarities than patients from different centers, for example, due to regional population differences or local referral patterns. Consequently, the assumption of independence of observations, underlying the most often used statistical techniques to analyze the data (e.g., logistic regression), does not hold. This is mostly ignored in much of the current clinical prediction research. Research that relies on faulty assumptions may yield misleading results and lead to suboptimal improvements in patient care.
To address this issue, I investigated the consequences of ignoring the assumption of independence and studied alternative techniques that acknowledge clustering throughout the process of planning a study, building a model and validating models in new data. I used mixed and random effects methods throughout the research as they allow to explicitly model differences between centers, and evaluated the proposed solutions with simulations and real clinical data. This dissertation covers sample size requirements, data collection and predictor selection, model fitting, and the validation of risk models in new data, focusing mainly on diagnostic models. The main case study is the development and validation of models for the pre-operative diagnosis of ovarian cancer, for which the multicenter dataset collected by the International Ovarian Tumor Analysis (IOTA) consortium is used.
The results suggested that mixed effects logistic regression models offer center-specific predictions that have a better predictive performance in new patients than the predictions from standard logistic regression models. Although simulations showed that models were severely overfitted with only five events per variable, mixed effects models did not require more demanding sample size guidelines than standard logistic regression models. A case study on predictors of ovarian malignancy demonstrated that in multicenter data, measurements may vary systematically from one center to another, indicating potential threats to generalizability. These predictors could be detected using the residual intraclass correlation coefficient and may be excluded from risk models. In addition, a case study showed that, if statistical variable selection is used, mixed effects models are required in every step of the selection procedure to prevent incorrect inferences. Finally, case studies on risk models for ovarian cancer demonstrated that the predictive performance of risk models varied considerably between centers. This could be detected using meta-analytic models to analyze discrimination, calibration and clinical utility.
In conclusion, taking into account differences between centers during the planning of prediction research, the development of a model and the validation of risk predictions in new patients offers insight in the heterogeneity and better predictions in local settings. Many methodological challenges remain, among which the inclusion of predictor-by-center interactions, the optimal application of mixed effects models in new centers, and the refinement of techniques to summarize clinical utility in multicenter data. Nonetheless, the findings in this dissertation imply that current clinical prediction research would benefit from adopting mixed and random effects techniques to fully employ the information that is available in multicenter data.status: Publishe
Echografie en translationeel onderzoek naar preoperatieve diagnostiek en differentiatie van adnexiële tumoren
No full textOvarian cancer is the most aggressive and lethal gynaecological malignant neoplasm. It is a "silent killing disease", often diagnosed at an advanced stage, with an overall 5-year survival rate of only 40%, a high recurrence rate and a high mortality rate, as around 80% of patients will die of the disease. Therefore, more efforts are needed to improve the preoperative early and accurate detection of adnexal lesions, which would significantly improve the prognosis of patients with ovarian cancer. The first tool we have for this purpose is transvaginal ultrasonography, which is the most common and one of the best imaging exams for the assessment of adnexal tumours. To date, the International Ovarian Tumour Analysis (IOTA) is the largest prospective multicentre international analysis in the literature on ultrasound diagnosis of adnexal lesions. In two recent meta-analyses, it has been confirmed that the IOTA models outperform the other preoperative diagnostic models. Our overall aim is to develop a new diagnostic algorithm based on clinical, ultrasound features and biomarkers, in order to improve the preoperative diagnosis of all types of adnexal pathology, improving the detection, the management and the prognosis of patients with adnexal tumours. On one hand, we will investigate new ultrasound features in a specific group of masses, which are particularly difficult to be preoperatively assessed, even by an expert ultrasound examiner (i.e. the unilocular masses with papillary projections). On the other hand, we will investigate new biomarkers in the bloodstream of patients with adnexal lesions. These new variables will be integrated in the IOTA models, in order to improve their performance and accuracy in the preoperative diagnosis of adnexal tumours. For this last purpose, we will investigate circulating tumour DNA and immune cells. Preliminary but limited reports have been published about the detection of circulating DNA in the blood of ovarian cancer patients. Moreover, it is now well established that the immune system is an important regulator in the development of cancer. We will evaluate the ratios of immune cells (e.g. effector T cells (Teff), regulatory T cells (Treg), myeloid derived suppressor cells (MDSC), and tumor-associated macrophages (TAM)) in patients with ovarian cancer. Ovarian cancer is the most aggressive and lethal gynaecological malignant neoplasm. It is a "silent killing disease", often diagnosed at an advanced stage, with an overall 5-year survival rate of only 40%, a high recurrence rate and a high mortality rate, as around 80% of patients will die of the disease. Therefore, more efforts are needed to improve the preoperative early and accurate detection of adnexal lesions, which would significantly improve the prognosis of patients with ovarian cancer. The first tool we have for this purpose is transvaginal ultrasonography, which is the most common and one of the best imaging exams for the assessment of adnexal tumours. To date, the International Ovarian Tumour Analysis (IOTA) is the largest prospective multicentre international analysis in the literature on ultrasound diagnosis of adnexal lesions. In two recent meta-analyses, it has been confirmed that the IOTA models outperform the other preoperative diagnostic models. Our overall aim is to develop a new diagnostic algorithm based on clinical, ultrasound features and biomarkers, in order to improve the preoperative diagnosis of all types of adnexal pathology, improving the detection, the management and the prognosis of patients with adnexal tumours. On one hand, we will investigate new ultrasound features in a specific group of masses, which are particularly difficult to be preoperatively assessed, even by an expert ultrasound examiner (i.e. the unilocular masses with papillary projections). On the other hand, we will investigate new biomarkers in the bloodstream of patients with adnexal lesions. These new variables will be integrated in the IOTA models, in order to improve their performance and accuracy in the preoperative diagnosis of adnexal tumours. For this last purpose, we will investigate circulating tumour DNA and immune cells. Preliminary but limited reports have been published about the detection of circulating DNA in the blood of ovarian cancer patients. Moreover, it is now well established that the immune system is an important regulator in the development of cancer. We will evaluate the ratios of immune cells (e.g. effector T cells (Teff), regulatory T cells (Treg), myeloid derived suppressor cells (MDSC), and tumor-associated macrophages (TAM)) in patients with ovarian cancer.status: Publishe
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Updating en calibratie van multinomiale risico-predictiemodellen
No full textRisk prediction models for diagnostic or prognostic outcomes are useful tools for clinical decision support. Most commonly, a dichotomous outcome (e.g. a benign or malignant tumor) is considered. Especially in diagnostic problems, however, a differential diagnosis often includes more levels than categorization of subjects as diseased versus non-diseased (e.g. a benign, borderline or invasive tumor). Methods for updating existing risk prediction models, i.e. adjusting an existing model in order to improve predictions from future patients in a new and different setting, had already been suggested for dichotomous models but did not yet exist for multinomial models. Closely related, the aspect of calibration of multinomial risk prediction models, i.e. the reliability of the predicted risks, had not been studied extensively. Therefore, in this dissertation we extended calibration statistics, calibration plots as well as updating techniques to prediction models for polytomous outcomes based on multinomial logistic regression.status: Publishe
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