Search

Xi Ma,1,* Yu-Rui Wang,2,* Li-Yong Zhuo,1 Xiao-Ping Yin,1 Jia-Liang Ren,3 Cai-Ying Li,4 Li-Hong Xing,1 Tong-Tong Zheng4 1CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei Province, 071000, People’s Republic of China; 2Department of Computed Tomography, Tangshan Gongren Hospital, Tangshan, Hebei Province, 063000, People’s Republic of China; 3GE Healthcare[Shanghai] Co Ltd, Shanghai, 210000, People’s Republic of China; 4Department of Radiology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao-Ping YinCT/MRI Room, Affiliated Hospital of Hebei University, No. 212 Eastern Yuhua Road, Baoding City, Hebei Province, 071000, People’s Republic of ChinaTel +86 0312-5981699Email xpydr_sea123@163.comCai-Ying LiDepartment of Radiology, The Second Hospital of Hebei Medical University, 215 Eastern Heping Road, Shijiazhuang city, Hebei Province, 050000, People’s Republic of ChinaTel +86 0311-87046901Email lcy_dr06@126.comPurpose: To investigate the feasibility of enhanced computed tomography (CT) radiomics analysis to differentiate between pancreatic cancer (PC) and chronic pancreatitis.Methods and materials: The CT images of 151 PCs and 24 chronic pancreatitis were retrospectively analyzed in the three-dimensional regions of interest on arterial phase (AP) and venous phase (VP) and segmented by MITK software. A multivariable logistic regression model was established based on the selected radiomics features. The radiomics score was calculated, and the nomogram was established. The discrimination of each model was analyzed by the receiver operating characteristic curve (ROC). Decision curve analysis (DCA) was used to evaluate clinical utility. The precision recall curve (PRC) was used to evaluate whether the model is affected by data imbalance. The Delong test was adopted to compare the diagnostic eff

Li-Yong Zhuo,* Li-Hong Xing,* Xi Ma, Yu Zhang, Ze-Peng Ma, Xiao-Ping Yin, Jia-Ning Wang Department of CT-MRI Diagnostic, Affiliated Hospital of Hebei University, Baoding, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao-Ping Yin; Jia-Ning WangDepartment of CT-MRI Diagnostic, Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding City, Hebei Province, 071000, People’s Republic of ChinaTel +8613103128133; +8618233289724Email xiaoping_yin@outlook.com; jianingwang06@outlook.comObjective: To identify diffusion-weighted imaging (DWI) patterns and conspicuity discrepancies on hepatobiliary phase imaging (HBPI) to distinguish atypical hepatic abscesses from hepatic metastases.Materials and Methods: This retrospective study recruited 31 patients with 43 atypical hepatic abscesses and 32 patients with 35 hepatic metastases who underwent gadobenate dimeglumine-enhanced magnetic resonance imaging. All lesions were confirmed by pathological or clinical diagnosis. For the qualitative and quantitative analyses, the signal intensity, DWI pattern, apparent diffusion coefficient, degree of perilesional edema, perilesional hyperemia, perilesional signal on HBPI, conspicuity, size discrepancy between sequences, contrast-to-noise ratio, signal-to-noise ratio, and relative enhancement ratio on dynamic phases were independently assessed by two radiologists. Significant findings for differentiating the two groups were identified via univariate and multivariate analyses with a nomogram for predicting atypical hepatic abscesses. The interobserver agreement was also analyzed for each variable.Results: The multivariate analysis revealed that the conspicuity discrepancy (odds ratio [OR] 34.78, 95% confidence interval [CI] 2.09– 579.47, p = 0.013) and non-peripheral high signal intensity (SI) rim on DWI (OR 67.46, 95% CI 2.64, 1723.20, p = 0.011) were significant independent factors for predicting atypic