Meng-chen Yang ^* Hai-yang Liu, Yan-ming Zhang Yi Guo Shang-yu Yang Hua-wei Zhang Bao Cui Tian-min Zhou Hao-xiang Guo Dan-wei Hou

• Department of Medical Imaging, Shangluo Central Hospital, Shangluo, China

To investigate the value of a CT-enhanced scanning radiomics nomogram in distinguishing between early hepatic abscess (EHA) and intrahepatic cholangiocarcinoma (ICC), and to validate its diagnostic efficacy. Materials and Methods: Clinical and imaging data of 112 patients diagnosed with EHA and ICC who underwent double-phase CT-enhanced scanning at our hospital were collected. The contours of the lesions were delineated layer by layer across the three phases of CT scanning and enhancement using 3D Slicer software to define the region of interest (ROI). Subsequently, the contours were merged into 3D models, and radiomics features were extracted using the Radiomics plug-in. The data were randomly divided into training (n=78) and validation (n=34) cohorts in a 7:3 ratio, using the R programming language. Standardization was performed using the Z-score method, and LASSO regression was employed to select the best λ-value for screening variables, which were then used to establish prediction models. The radscore was calculated using the best radiomics model, and a joint model was constructed based on the radscore and clinical scores. A nomogram was developed based on the joint model. The diagnostic efficacy of the models for distinguishing ICC and EHA was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. Calibration curves were used to evaluate the reliability and accuracy of the nomograms, while decision curves and clinical impact curves were utilized to assess their clinical value. Results: Compared with the ICC group, significant differences were observed in clinical data and imaging characteristics in the EHA group, including age, centripetal enhancement, hepatic pericardial depression sign, arterial perfusion abnormality, arterial CT value, and arteriovenous enhancement (P < 0.05). Logistic regression analysis identified centripetal enhancement, hepatic pericardial depression sign, arterial perfusion abnormality, arterial CT value, and arteriovenous enhancement as independent influencing factors. Three, five, and four radiomics features were retained in the scanning, arterial, and venous phases, respectively. Single-phase models were constructed, with the radiomics model from the arterial phase demonstrating the best diagnostic efficacy.The CT-enhanced scanning radiomics nomogram demonstrates high clinical value in distinguishing between EHA and ICC, thereby enhancing the accuracy of preoperative diagnosis.