ORCID Profile
0000-0003-0749-5117
Current Organisations
Kheiron Medical Technologies (United Kingdom)
,
Maastricht University
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Publisher: Elsevier BV
Date: 10-2011
DOI: 10.1016/J.IJROBP.2011.03.012
Abstract: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. A total of 110 patients (40%) developed acute esophagitis Grade ≥2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.
Publisher: MDPI AG
Date: 29-05-2021
Abstract: The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an in idual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection.
Publisher: Elsevier BV
Date: 10-2014
Publisher: Wiley
Date: 02-2009
Abstract: Understanding blood donation motivation among non‐donors is prerequisite to effective recruitment. Two studies explored the psychological antecedents of blood donation motivation and the generalisability of a model of donation motivation across groups differing in age and educational level. An older well‐educated population and a younger less well‐educated population were s led. The studies assessed the role of altruism, fear of blood/needles and donation‐specific cognitions including attitudes and normative beliefs derived from an extended theory of planned behaviour (TPB). Across both s les, results showed that affective attitude, subjective norm, descriptive norm, and moral norm were the most important correlates of blood donation intentions. Self‐efficacy was more important among the younger less well‐educated group. Altruism was related to donation motivation but only indirectly through moral norm. Similarly, fear of blood/needles only had an indirect effect on motivation through affective attitude and self‐efficacy. Additional analyses with the combined data set found no age or education moderation effects, suggesting that this core model of donation‐specific cognitions can be used to inform future practical interventions recruiting new blood donors in the general population.
Publisher: British Institute of Radiology
Date: 04-2020
DOI: 10.1259/BJR.20190948
Abstract: Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.
Publisher: Public Library of Science (PLoS)
Date: 12-09-0066
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Cary Oberije.