Background: The differentiation of surgical from nonsurgical adult intussusception may enable the appropriate selection of management strategies. Objective: This study aimed to investigate the diagnostic potential of multidetector computed tomography (MCDT) features to differentiate surgical from nonsurgical adult intussusception and develop a diagnostic model. Methods: A retrospective study was performed on 96 patients with intussusceptions at the University Medical Center Hospital between January 2014 and January 2020. Two radiologists reviewed all images, and intussusception characteristics were documented. The location of intussusception, length, diameter, interposed fat thickness, lead point, and complications were evaluated. Based on the results, a diagnostic tree model was developed to differentiate between surgical and nonsurgical adult intussusception. Results: A total of 99 intussusceptions in 96 patients (mean age: 53.0 ± 16.5 years), including 35 (35.3%) enteroenteric, 27 (27.3%) enterocolic, and 37 (37.4%) colocolic lesions, were evaluated. Of the enteroenteric intussusceptions, 22 (62.9%) were surgical, including 19 (79.2%) with lead points. Among colon intussusceptions, 63 (98.4%) were surgical, and 100% had lead points. The characteristics used to predict surgical intussusceptions included lead point presence, length ≥ 5.0 cm, diameter ≥ 3.2 cm, interposed fat thickness ≥ 0.5 cm, and complications (p < 0.001). Based on these features, we established a diagnostic tree model that correctly classified 96 (97%) of 99 lesions. Conclusion: Our study reinforces the importance of MDCT for the diagnosis and guided management of adult intussusceptions. The characteristics that predicted surgical intussusceptions included lead points, length, diameter, interposed fat thickness, and complications. A systematic approach using this diagnostic tree model could be used to distinguish surgical and nonsurgical adult intussusception.
Background: Thymic epithelial tumors (TETs) are clinically the most frequently encountered neoplasm of the prevascular mediastinum in adults. The role of chest magnetic resonance (MR) imaging has been increasingly stressed thanks to its excellent contrast resolution, freedom from ionizing radiation, and capability to provide additional information regarding tumors' cellular structure and vascularity. Methods: This study aimed to establish the relationship between the MR findings and pathological classification of TETs, focusing on diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) imaging. This retrospective cross-sectional study included 44 TET patients who underwent chest MR scanning. The tumors were classified into three groups according to the WHO classification: low-risk thymoma (LRT), high-risk thymoma (HRT), and non-thymoma (NT). Along with morphological characteristics, the apparent diffusion coefficient (ADC) value, time-intensity curve (TIC) pattern, and time to peak enhancement (TTP) of the tumors were recorded and compared between the three groups. Results: A smooth contour and complete or almost complete capsule were suggestive of LRTs. The median ADC value of the 44 tumors was 0.95 × 10 -3 mm 2 /sec. Among the three groups, LRTs had the highest ADC values, while NTs had the lowest. The differences between the ADC values of the three groups were statistically significant (p = 0.006). Using an ADC cutoff of 0.82 × 10 -3 mm 2 /sec to differentiate between LRTs and tumors of the two remaining groups, the area under the curve was 0.775, sensitivity was 100%, specificity was 50%, and accuracy was 65.91%. The washout (type 3) TIC pattern was the most prevalent, accounting for 45.45% of the population; this pattern was also predominantly observed in LRTs (71.43%). Although the median TTP of LRTs was lower than that of HRTs or NTs, no statistically significant differences were found between the TTPs of the three groups (p = 0.170). Conclusions: MR is a good imaging modality to preoperatively assess TETs. Morphological features, ADC value, TIC pattern, and TTP are helpful in preoperatively predicting TET pathology.
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