Background Endometrial carcinoma (EC) is one of the most common gynecological malignancies with an increasing incidence, and an accurate preoperative diagnosis of deep myometrial invasion (DMI) is crucial for personalized treatment. Objective To determine the predictive value of a magnetic resonance imaging (MRI)‐based radiomics nomogram for the presence of DMI in the International Federation of Gynecology and Obstetrics (FIGO) stage I EC. Methods We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (Center 1) and a validation group (Center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2‐weighted and axial contrast‐enhanced T1‐weighted (CE‐T1W) images were treated with the intraclass correlation coefficient, Mann–Whitney U test, least absolute shrinkage and selection operator, and logistic regression analysis with Akaike information criterion to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC). Result The four most significant radiomics features were finally selected from the CE‐T1W MRI. For the diagnosis of DMI, the AUCT/AUCV of M1 was 0.798/0.738, the AUCT/AUCV of M2 was 0.880/0.852, and the AUCT/AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram. Conclusion A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO stage I EC.

Percutaneous transhepatic cholangiodrainage (PTCD) and percutaneous transhepatic biliary stenting (PTBS) may be used as a palliative treatment for inoperable patients with malignant biliary obstruction (MBO) to improve the prognosis and their quality of life. However, acute pancreatitis is a common and severe complication that cannot be ignored after PTCD and PTBS in patients with MBO. A few cases may develop severe pancreatitis with a higher mortality rate. In this study, we summarize the known risk factors for acute pancreatitis after percutaneous biliary interventional procedures and investigate possible risk factors to reduce its occurrence by early identifying high-risk patients and taking appropriate measures.

Background: This study aims to develop and externally validate a multi-sequence MRI-based radiomics nomogram for preoperatively differentiating low-grade and high-grade tumors in FIGO stage I endometrial carcinoma (EMC).Methods: A primary cohort was established with a total of 100 patients enrolled from our hospital between Jan. 2017 and Apr. 2021. A consecutively enrolled internal validation cohort (n=41) and an external validation cohort (n=50) were used to test the models. Radiomics features were extracted from T1-weighted contrast-enhanced (T1-CE) and T2-weighted (T2W) MRI and selected with the least absolute shrinkage and selection operator (LASSO) to build the radiomics signature (RS). Clinical factors were analyzed with Mann-Whitney U test and Chi-square test. A radiomics nomogram model was constructed incorporating the RS and the most discriminative clinical parameter. Performance of the RS, clinical parameter and nomogram were validated with receiver operating characteristic (ROC), calibration and decision curve analysis (DCA).Results: The multi-sequence MRI-based RS was built integrating 3 selected features, all from T1-CE MRI. The deep myometrial invasion was considered as the most important clinical parameter (P<0.05). The nomogram model incorporates RS and deep myometrial invasion yielded the best discriminative performance with AUCs of 0.845 (95% confidence interval [CI] 0.759-0.910, SEN=0.900, SPE =0.650), 0.756 (95% CI 0.597-0.876, SEN=0.818, SPE =0.632) and 0.779 (95% CI 0.639-0.884, SEN=0.800, SPE =0.720) in the primary, internal validation and external validation cohorts, respectively. Calibration curves and DCA suggested good potential of our nomogram in clinical uses.Conclusions: The developed radiomics nomogram can be used as a potential non-invasive tool for preoperatively differentiating low- and high-grade tumors in stage I EMC patients.