Patients with coronavirus disease 19 (COVID-19) have increased susceptibility to secondary respiratory infections including invasive pulmonary aspergillosis (IPA). COVID-19-associated pulmonary aspergillosis (CAPA) is difficult to diagnose and can be associated with increased mortality especially in severe immunodeficiency such as hematological malignancies. Our study evaluates IPA in COVID-19 patients defined as COVID-19-CAPA among patients with acute leukemia (AL). A retrospective single-center study analyzed 46 patients with COVID-19 infection and acute leukemia, admitted to the Clinic for Haematology, Clinical Center of Serbia, Belgrade between the 2 April 2020 and 15 May 2021. During hospitalization, all participants were diagnosed with probable IPA according to the previous consensus definitions. Positive serology and galactomannan (GM) detection values in bronchoalveolar lavage (BAL) and serum were used as microbiological criteria. COVID-19 associated probable IPA was found in 22% (9/41) tested patients, where serum GM and IgM anti-Aspergillus antibodies were positive in 12% (5/41) and 10% (4/41) had positive serology for aspergillosis. One patient died while eight recovered during follow-up. Our study showed that COVID-19 might be a risk factor for IPA development in patients with AL. Early diagnosis and prompt treatment are required as reported mortality rates are high.

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.

Small bowel obstruction is a frequent medical condition with various causes, the most common being postoperative adhesions, volvulus, intussusception, hernias, and tumors. A bezoar-induced blockage of the small intestine is a rare condition that accounts for approximately 4% of all small bowel obstruction cases. Herein, we present the case report of a 71-year-old patient with diffuse abdominal pain caused by a small bowel obstruction due to a calcified bezoar (bezoar egg) resulting from a post-radiation intestinal stricture. The patient underwent a small bowel excision with the extraction of the bezoar, after which a full recovery was made.

Inflammatory fibroid polyp (IFP) or Vanek’s tumor is a rare benign submucosal lesion of the gastrointestinal tract that may radiologically mimic mesenchymal gastric tumors, particularly gastrointestinal stromal tumors. We present the case of a 65-year-old patient with a contrast-enhancing gastric submucosal mass detected on computed tomography, initially interpreted as a suspected mesenchymal neoplasm. CT imaging demonstrated a well-defined enhancing lesion arising from the gastric wall without evidence of metastatic disease. Surgical resection was performed because imaging findings were considered highly suggestive of GIST. Gross intraoperative appearance and pathological examination, however, established the final diagnosis of gastric inflammatory fibroid polyp (Vanek’s tumor). Histopathological analysis demonstrated characteristic spindle-cell proliferation with inflammatory eosinophil-rich infiltrates, while immunohistochemistry excluded GIST. This case highlights the diagnostic challenge in differentiating IFP from other gastric submucosal neoplasms based solely on imaging findings and emphasizes the importance of histopathological confirmation for definitive diagnosis.

Background: Reliable and accurate prediction of treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) is usually demanding and continues to pose a challenge. Kurtosis as a histogram parameter calculated on T2-weighted MRI sequences might be an additional tool, as it represents a quantitative biomarker for response prediction. It is defined as a measure of distributions’ tails relative to the center of the distribution curve, which reflects tissue heterogeneity. The aim of the study was to evaluate the added value of T2-weighted kurtosis in predicting pathological response to nCRT in patients with LARC. Methods: a single-center cohort study included 71 patients with LARC who underwent both initial and post-nCRT MRI examinations followed by surgical resection in the form of the total mesorectal excision (TME). Histogram analysis was performed using software MIPAV (Medical Image Processing, Analysis, and Visualization, version 11.3.2, developed by the National Institutes of Health, Bethesda, MD, USA) on T2-weighted sequences, extracting kurtosis along with other histogram parameters. Pathological tumor regression grade (pTRG) in accordance with Mandard classification was considered the gold standard. Patients were classified as responders (pTRG 1–2) or non-responders (pTRG 3–5). Results: while other histogram parameters did not show statistically significant differences between groups, post-treatment values of kurtosis were significantly higher in responders compared to non-responders (4.28 ± 0.73 vs. 3.01 ± 0.17, p = 0.024). The F1 score as a classification metric (0.821) indicates an improvement in classification performance following therapy. Conclusions: T2-weighted kurtosis might be a significant tool in predicting pathological response to nCRT, representing a potentially valuable quantitative biomarker that could improve treatment response assessment.