Possibility of Using Conventional Computed Tomography Features and Histogram Texture Analysis Parameters as Imaging Biomarkers for Preoperative Prediction of High-Risk Gastrointestinal Stromal Tumors of the Stomach

BACKGROUND

The objective of this study is to determine the morphological computed tomography features of the tumor and texture analysis parameters, which may be a useful diagnostic tool for the preoperative prediction of high-risk gastrointestinal stromal tumors (HR GISTs).

METHODS

This is a prospective cohort study that was carried out in the period from 2019 to 2022. The study included 79 patients who underwent CT examination, texture analysis, surgical resection of a lesion that was suspicious for GIST as well as pathohistological and immunohistochemical analysis.

RESULTS

Textural analysis pointed out min norm (p = 0.032) as a histogram parameter that significantly differed between HR and LR GISTs, while min norm (p = 0.007), skewness (p = 0.035) and kurtosis (p = 0.003) showed significant differences between high-grade and low-grade tumors. Univariate regression analysis identified tumor diameter, margin appearance, growth pattern, lesion shape, structure, mucosal continuity, enlarged peri- and intra-tumoral feeding or draining vessel (EFDV) and max norm as significant predictive factors for HR GISTs. Interrupted mucosa (p < 0.001) and presence of EFDV (p < 0.001) were obtained by multivariate regression analysis as independent predictive factors of high-risk GISTs with an AUC of 0.878 (CI: 0.797-0.959), sensitivity of 94%, specificity of 77% and accuracy of 88%.

CONCLUSION

This result shows that morphological CT features of GIST are of great importance in the prediction of non-invasive preoperative metastatic risk. The incorporation of texture analysis into basic imaging protocols may further improve the preoperative assessment of risk stratification.

CT and 18FDG-PET/CT findings in progressive mediastinal idiopathic fibrosis as a benign mimicker of esophageal carcinoma: a case report

Idiopathic mediastinal fibrosis, also called sclerosing or fibrosing mediastinitis, is a very rare and aggressive fibroinflammatory process characterized by fibrous tissue proliferation in the mediastinal region. Herein, we present a rare case of idiopathic mediastinal fibrosis presenting with esophageal obstruction, most likely associated with immunoglobulin G (IgG4)-related disease, affecting the posterior mediastinum with intrapulmonary infiltration. Computed tomography revealed a narrowed lumen and thickened wall of the distal esophagus surrounded by a necrotic mass with infiltration into the nearby structures, suggesting a locally advanced malignant process. Positron emission tomography revealed intense accumulation of 18F-fluorodeoxyglucose, indicating an active inflammatory component, which complicates further differential diagnosis of mediastinal masses. Thoracoscopic biopsy and immunohistochemical analysis confirmed a fibroinflammatory process with perivascular lymphoid cell infiltration that was cluster of differentiation (CD)3 (++) and CD20 (++), with massive numbers of IgG4-immunoreactive plasma cells. Although a benign condition, sclerosing mediastinitis is a close mimicker of esophageal carcinoma, which cannot be differentiated by computed tomography or positron emission tomography and must be considered in a differential diagnosis.

Predicting the Assisted Living Care Needs Using Machine Learning and Health State Survey Data

Effective pain management can significantly improve quality of life and outcomes for various types of patients (e.g. elderly, adult, young) and often requires assisted living for a significant number of people worldwide. In order to improve our understanding of patients' response to pain and needs for assisted living we need to develop adequate data processing techniques that would enable us to understand underlying interdependencies. To this purpose in this paper we develop several different algorithms that can predict the need for medically assisted living outcomes using a large database obtained as a part of the national health survey. As a part of the survey the respondents provided detailed information about general health care state, acute and chronic problems as well as personal perception of pain associated with performing two simple talks: walking on the flat surface and walking upstairs. We model the correspondent responses using multinomial random variables and propose structured deep learning models based on maximum likelihood estimation and machine learning for information fusion. For comparison purposes we also implement fully connected deep learning network and use its results as benchmark measurements. We evaluate the performance of the proposed techniques using the national survey data and split them into two parts used for training and testing. Our preliminary results indicate that the proposed models can potentially be useful in forecasting the need for medically assisted living.

Alignment of Perceived Competencies and Perceived Job Tasks among Primary Care Managers

In this study we aimed to explore how managers in primary health care (PHC) organizations assess their managerial knowledge and skills, as well as the importance of these competencies for their job, and to identify whether there is an alignment between these two perceptions; therefore, whether there is a need for management competency improvement. With this study, we tried to address a high demand for information about health managers, especially in health systems in low- and middle-income countries. In a sample of 58 primary health care centers (n = 106 managers) in Serbia, we used a basic managerial competency matrix consisting of the following six competencies—communication, team-building, planning and priority-setting, performance assessment, problem-solving, and leading. Managerial perception of the importance of their job tasks differs by educational level and managerial position. The best alignment between the importance of knowledge and skills was for communication and leading. The study pointed out that managers were aware of the necessity to improve their level of managerial competencies, particularly in the domains of planning and priority-setting, performance assessment, and problem solving. The study highlights the need for formal managerial education for managers in PHC settings and commitment to continuously evaluate and improve management competencies in order to better manage PHC.