Contrast-enhanced CT in the differential diagnosis of bladder cancer and paraganglioma.

PURPOSE: We sought to summarize the value of contrast-enhanced computed tomography (CECT) in the differential diagnosis of bladder paraganglioma (BPG) and bladder cancer. METHODS: The medical records of 19 patients with BPG (13 males, 6 females) and 56 patients with bladder cancer (49 males, 7 females) between November 2007 and June 2023 were retrospectively reviewed. All patients underwent unenhanced and contrast-enhanced CT scanning. RESULTS: Patient age (46.4 ± 11.1 years vs. 58.6 ± 16.0 years), tumor calcification (1/19 vs. 18/56), stalk (0/19 vs. 10/56), internal vessels (15/19 vs. 19/56) and the enlarged adjacent supplying artery (14/19 vs. 10/56) were significantly different between BPG and bladder cancer (P < 0.05). The CT value in the corticomedullary phase (92.4 ± 16.6 HU vs. 64.0 ± 14.5 HU) and the contrast-enhanced value in the corticomedullary phase (54.5 ± 17.4 HU vs. 28.5 ± 12.8 HU) were significantly greater in BPG patients than in bladder cancer patients (P < 0.001), with corresponding area under the curve values of 0.930 and 0.912, respectively. The optimal cutoff values were 83.2 HU and 38.5 HU, respectively. A CT value > 83.2 HU in the corticomedullary phase and a contrast-enhanced CT value > 38.5 HU in the corticomedullary phase were used to indicate BPG with sensitivities of 78.9% and 89.5%, respectively, and specificities of 94.6% and 75.0%, respectively. CONCLUSION: The corticomedullary phase of CECT plays an important role in the preoperative differential diagnosis of BPG and bladder cancer.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

Multidetector computed tomography diagnosis of small bowel intussusceptions in adults: observations in a 10-year single-center study.

BACKGROUND: The etiologies of small bowel intussusception (SBI) in adults are varied. PURPOSE: To investigate multidetector computed tomography (MDCT) characteristics in adults with neoplastic and non-neoplastic SBI. MATERIAL AND METHODS: Clinical data and MDCT images diagnosed with SBI in adults from January 2010 to May 2020 were retrospectively reviewed. RESULTS: The study included a total of 71 patients. Forty-two patients had a combined total of 55 neoplastic intussusceptions, including 29 patients with benign tumors and 13 patients with malignant tumors. Twenty-nine patients had a combined total of 36 non-neoplastic intussusceptions, of which the condition was idiopathic in 23 patients and cased by non-neoplastic benign lesions in six patients. There were no significant differences in patient age or sex ratio in the neoplastic and non-neoplastic groups. In the non-neoplastic group the intussusceptions were shorter in length (3.6 cm vs. 13.2 cm, P<0.05) and smaller in transverse diameter (2.8 cm vs. 4.2 cm, P<0.05), and less likely to be associated with intestinal obstruction (2 vs. 18, P<0.05). The percentage of patients with multiple intussusceptions was greater in the neoplastic group (10/42, 23.8% vs. 4/29, 13.8%). In the non-neoplastic group only one lead point was detected (in a patient with Meckel's diverticulum), whereas lead points were detected in all 55 intussusceptions in the neoplastic group. CONCLUSION: There are differences in the clinical and MDCT manifestations of adult neoplastic and non-neoplastic SBIs. Whether a lead point is present or not has implications with regard to deciding on the most appropriate treatment and avoiding unnecessary surgery.