Browsing by Author "Mitrovic Jovanovic, Milica (56257450700)"

Aggressive fibromatosis is a rare type of intra-abdominal desmoid tumour that usually involves the small bowel mesentery. It is a locally-invasive lesion, with a high rate of recurrence, but without metastatic potential. Aggressive fibromatosis is seen more often in young female patients. This case report presents the radiological, intraoperative and histopathological findings from a 37-year-old female patient that presented with epigastric pain and a palpable mass in the right hemiabdomen. Histological and immunohistochemical examinations of the resected tumour, including positive staining for beta-catenin, confirmed a postoperative diagnosis of desmoid type fibromatosis. This specific case showed that desmoid type fibromatosis of the colon can mimic gastrointestinal stromal tumours (GIST) based on its clinical presentation, computed tomography and magnetic resonance imaging findings. Differential diagnosis between desmoid type fibromatosis and GIST is clinically very important due to the different treatments and follow-up protocols that are implemented for these lesions. © The Author(s) 2021.

Aggressive fibromatosis is a rare type of intra-abdominal desmoid tumour that usually involves the small bowel mesentery. It is a locally-invasive lesion, with a high rate of recurrence, but without metastatic potential. Aggressive fibromatosis is seen more often in young female patients. This case report presents the radiological, intraoperative and histopathological findings from a 37-year-old female patient that presented with epigastric pain and a palpable mass in the right hemiabdomen. Histological and immunohistochemical examinations of the resected tumour, including positive staining for beta-catenin, confirmed a postoperative diagnosis of desmoid type fibromatosis. This specific case showed that desmoid type fibromatosis of the colon can mimic gastrointestinal stromal tumours (GIST) based on its clinical presentation, computed tomography and magnetic resonance imaging findings. Differential diagnosis between desmoid type fibromatosis and GIST is clinically very important due to the different treatments and follow-up protocols that are implemented for these lesions. © The Author(s) 2021.

Background: This study analyzed different classifier models for differentiating pancreatic adenocarcinoma from surrounding healthy pancreatic tissue based on radiomic analysis of magnetic resonance (MR) images. Methods: We observed T2W-FS and ADC images obtained by 1.5T-MR of 87 patients with histologically proven pancreatic adenocarcinoma for training and validation purposes and then tested the most accurate predictive models that were obtained on another group of 58 patients. The tumor and surrounding pancreatic tissue were segmented on three consecutive slices, with the largest area of interest (ROI) of tumor marked using MaZda v4.6 software. This resulted in a total of 261 ROIs for each of the observed tissue classes in the training–validation group and 174 ROIs in the testing group. The software extracted a total of 304 radiomic features for each ROI, divided into six categories. The analysis was conducted through six different classifier models with six different feature reduction methods and five-fold subject-wise cross-validation. Results: In-depth analysis shows that the best results were obtained with the Random Forest (RF) classifier with feature reduction based on the Mutual Information score (all nine features are from the co-occurrence matrix): an accuracy of 0.94/0.98, sensitivity of 0.94/0.98, specificity of 0.94/0.98, and F1-score of 0.94/0.98 were achieved for the T2W-FS/ADC images from the validation group, retrospectively. In the testing group, an accuracy of 0.69/0.81, sensitivity of 0.86/0.82, specificity of 0.52/0.70, and F1-score of 0.74/0.83 were achieved for the T2W-FS/ADC images, retrospectively. Conclusions: The machine learning approach using radiomics features extracted from T2W-FS and ADC achieved a relatively high sensitivity in the differentiation of pancreatic adenocarcinoma from healthy pancreatic tissue, which could be especially applicable for screening purposes. © 2025 by the authors.

Background: This study analyzed different classifier models for differentiating pancreatic adenocarcinoma from surrounding healthy pancreatic tissue based on radiomic analysis of magnetic resonance (MR) images. Methods: We observed T2W-FS and ADC images obtained by 1.5T-MR of 87 patients with histologically proven pancreatic adenocarcinoma for training and validation purposes and then tested the most accurate predictive models that were obtained on another group of 58 patients. The tumor and surrounding pancreatic tissue were segmented on three consecutive slices, with the largest area of interest (ROI) of tumor marked using MaZda v4.6 software. This resulted in a total of 261 ROIs for each of the observed tissue classes in the training–validation group and 174 ROIs in the testing group. The software extracted a total of 304 radiomic features for each ROI, divided into six categories. The analysis was conducted through six different classifier models with six different feature reduction methods and five-fold subject-wise cross-validation. Results: In-depth analysis shows that the best results were obtained with the Random Forest (RF) classifier with feature reduction based on the Mutual Information score (all nine features are from the co-occurrence matrix): an accuracy of 0.94/0.98, sensitivity of 0.94/0.98, specificity of 0.94/0.98, and F1-score of 0.94/0.98 were achieved for the T2W-FS/ADC images from the validation group, retrospectively. In the testing group, an accuracy of 0.69/0.81, sensitivity of 0.86/0.82, specificity of 0.52/0.70, and F1-score of 0.74/0.83 were achieved for the T2W-FS/ADC images, retrospectively. Conclusions: The machine learning approach using radiomics features extracted from T2W-FS and ADC achieved a relatively high sensitivity in the differentiation of pancreatic adenocarcinoma from healthy pancreatic tissue, which could be especially applicable for screening purposes. © 2025 by the authors.

A pancreatic pseudoaneurysm is a rare but life-threatening clinical entity. Prompt diagnosis and appropriate treatment are of great clinical importance. We herein present an unusual case of a pseudoaneurysm of the posterior inferior pancreaticoduodenal artery that developed as a complication of chronic pancreatitis. It was detected in a timely manner and successfully treated with minimally invasive endovascular therapy. © The Author(s) 2022.

A pancreatic pseudoaneurysm is a rare but life-threatening clinical entity. Prompt diagnosis and appropriate treatment are of great clinical importance. We herein present an unusual case of a pseudoaneurysm of the posterior inferior pancreaticoduodenal artery that developed as a complication of chronic pancreatitis. It was detected in a timely manner and successfully treated with minimally invasive endovascular therapy. © The Author(s) 2022.