Browsing by Author "Popovic, Marina (57428070900)"

Background. Recurrent disease in post-irradiation patients with cervical cancer is often difficult to delineate on magnetic resonance imaging (MRI), because posttreatment changes can have a similar appearance, and further evaluation is often required. The aims of the study were to evaluate positron emission tomography/computed tomography with 18F-fluorodeoxyglucose (FDG PET-CT) diagnostic role in suspected recurrent cervical cancer after radiotherapy, compare it to MRI, and assess their prognostic impact in these patients. Patients and methods. This cohort retrospective study included patients previously treated with radiotherapy for carcinoma of uterine cervix with suspected recurrence, who had undergone MRI of abdomen and pelvis, and were subsequently evaluated on FDG PET-CT, with minimum follow-up period of 12 months. Results. In the total of 84 patients included in analysis, MRI vs. FDG PET-CT showed sensitivity, specificity and accuracy of 80.1%, 52.4% and 66.7%, vs. 97.6%, 61.9% and 79.8%, respectively. Patients with positive findings on MRI (Log Rank, p = 0.003) and PET-CT (Log Rank, p < 0.001) had shorter progression-free survival (PFS) than those with negative results. In univariate Cox regression models, MRI and FDG PET-CT results were found to be related to PFS (p = 0.005 and p < 0.001, respectively). However, multivariate analysis proved only FDG PET-CT to be independent prognostic factor, where patients with positive FDG PET-CT results had almost nine times higher risk of progression (p < 0.001). Conclusion. FDG PET-CT represents useful diagnostic tool in suspected recurrent cervical cancer after radiotherapy, showing high sensitivity in its detection. In addition, it is an independent factor in predicting progression-free survival in these patients. © 2022 Sciendo. All rights reserved.

The aim was to explore the factors associated with the financial burden (FB) of medical care, dental care, and medicines among older-aged people in Slovenia, Serbia, and Croatia using EU-SILC 2017. The highest frequency of FB of medical care and medicines was in Croatia (50% and 69.1%, respectively) and of dental care in Slovenia (48.5%). The multivariate logistic regression analysis with FB as an outcome variable showed that the FB of medical care was associated with being married (OR: 1.54), reporting not severe (OR: 1.51) and severe limitations in daily activities (OR: 2.05), having higher education (OR: 2.03), and heavy burden of housing costs (OR: 0.51) in Slovenia, with very bad self-perceived health (OR: 5.23), having the slight (OR: 0.69) or heavy (OR: 0.47) burden of housing costs, making ends meet fairly easily or with some difficulty (OR: 3.58) or with difficulty or great difficulty (OR: 6.80) in Serbia, and with being married (OR: 1.43), having heavy burden of housing costs (OR: 0.62), and making ends meet fairly easily or with some difficulty (OR: 2.08) or with difficulty or great difficulty (OR: 2.52) in Croatia. The older-aged have the FB of healthcare, especially the poorest or those with health problems. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

The aim was to explore the factors associated with the financial burden (FB) of medical care, dental care, and medicines among older-aged people in Slovenia, Serbia, and Croatia using EU-SILC 2017. The highest frequency of FB of medical care and medicines was in Croatia (50% and 69.1%, respectively) and of dental care in Slovenia (48.5%). The multivariate logistic regression analysis with FB as an outcome variable showed that the FB of medical care was associated with being married (OR: 1.54), reporting not severe (OR: 1.51) and severe limitations in daily activities (OR: 2.05), having higher education (OR: 2.03), and heavy burden of housing costs (OR: 0.51) in Slovenia, with very bad self-perceived health (OR: 5.23), having the slight (OR: 0.69) or heavy (OR: 0.47) burden of housing costs, making ends meet fairly easily or with some difficulty (OR: 3.58) or with difficulty or great difficulty (OR: 6.80) in Serbia, and with being married (OR: 1.43), having heavy burden of housing costs (OR: 0.62), and making ends meet fairly easily or with some difficulty (OR: 2.08) or with difficulty or great difficulty (OR: 2.52) in Croatia. The older-aged have the FB of healthcare, especially the poorest or those with health problems. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Purpose: Radioactive iodine therapy (RAIT) is important when treating patients who have been diagnosed with differentiated thyroid carcinoma and have gone through initial surgery. However, deciding whether a patient should undergo such therapy as well as the proper iodine dose is a complex task, especially for those with a lack of experience. Therein, this paper aimed to develop and compare classifier systems to aid inexperienced physicians in decision making on radioactive iodine therapy for thyroid cancer patients. Methods: The study cohort consisted of 210 thyroid cancer patients who had undergone a total thyroidectomy. We developed and evaluated the performance of three machine learning (ML) algorithms that suggest whether these patients should undergo RAIT and propose an administrable I-131 dose. These algorithms were Artificial Neural Network (ANN), Naïve Bayes Classifier (NB) and Group Method of Data Handling (GMDH). The kappa coefficient was used to measure agreement of classifiers with gold standard decision made by an experienced physician. Results: Our results indicate that the ANN performs better than NB and GMDH in terms of accuracy (95.71%). On the basis of the Kappa coefficient, ANN was also the best 0.96 (0.91-1.00). Additionally, kappa coefficient increased to 0.93 (0.86-1.00) by comparing young physicians' decisions on thyroid cancer therapy before and after using ANN as a decision making tool. Conclusion: Our results suggest that developed classifiers are able to imitate the real decisions of medical expert. Furthermore, classifiers may be utilized to educate inexperienced medical professionals, especially in the absence of strict guidelines' recommendations. © 2021 Zerbinis Publications. All rights reserved.

Purpose: Radioactive iodine therapy (RAIT) is important when treating patients who have been diagnosed with differentiated thyroid carcinoma and have gone through initial surgery. However, deciding whether a patient should undergo such therapy as well as the proper iodine dose is a complex task, especially for those with a lack of experience. Therein, this paper aimed to develop and compare classifier systems to aid inexperienced physicians in decision making on radioactive iodine therapy for thyroid cancer patients. Methods: The study cohort consisted of 210 thyroid cancer patients who had undergone a total thyroidectomy. We developed and evaluated the performance of three machine learning (ML) algorithms that suggest whether these patients should undergo RAIT and propose an administrable I-131 dose. These algorithms were Artificial Neural Network (ANN), Naïve Bayes Classifier (NB) and Group Method of Data Handling (GMDH). The kappa coefficient was used to measure agreement of classifiers with gold standard decision made by an experienced physician. Results: Our results indicate that the ANN performs better than NB and GMDH in terms of accuracy (95.71%). On the basis of the Kappa coefficient, ANN was also the best 0.96 (0.91-1.00). Additionally, kappa coefficient increased to 0.93 (0.86-1.00) by comparing young physicians' decisions on thyroid cancer therapy before and after using ANN as a decision making tool. Conclusion: Our results suggest that developed classifiers are able to imitate the real decisions of medical expert. Furthermore, classifiers may be utilized to educate inexperienced medical professionals, especially in the absence of strict guidelines' recommendations. © 2021 Zerbinis Publications. All rights reserved.