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BackgroundRadiomics can improve the accuracy of traditional image diagnosis to evaluate extrahepatic cholangiocarcinoma (ECC); however, this is limited by variations across radiologists, subjective evaluation, and restricted data. A radiomics-based particle swarm optimization and support vector machine (PSO-SVM) model may provide a more accurate auxiliary diagnosis for assessing differentiation degree (DD) and lymph node metastasis (LNM) of ECC. ObjectiveThe objective of our study is to develop a PSO-SVM radiomics model for predicting DD and LNM of ECC. MethodsFor this retrospective study, the magnetic resonance imaging (MRI) data of 110 patients with ECC who were diagnosed from January 2011 to October 2019 were used to construct a radiomics prediction model. Radiomics features were extracted from T1-precontrast weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) using MaZda software (version 4.6; Institute of Electronics, Technical University of Lodz). We performed dimension reduction to obtain 30 optimal features of each sequence, respectively. A PSO-SVM radiomics model was developed to predict DD and LNM of ECC by incorporating radiomics features and apparent diffusion coefficient (ADC) values. We randomly divided the 110 cases into a training group (88/110, 80%) and a testing group (22/110, 20%). The performance of the model was evaluated by analyzing the area under the receiver operating characteristic curve (AUC). ResultsA radiomics model based on PSO-SVM was developed by using 110 patients with ECC. This model produced average AUCs of 0.8905 and 0.8461, respectively, for DD in the training and testing groups of patients with ECC. The average AUCs of the LNM in the training and testing groups of patients with ECC were 0.9036 and 0.8889, respectively. For the 110 patients, this model has high predictive performance. The average accuracy values of the training group and testing group for DD of ECC were 82.6% and 80.9%, respectively; the average accuracy values of the training group and testing group for LNM of ECC were 83.6% and 81.2%, respectively. ConclusionsThe MRI-based PSO-SVM radiomics model might be useful for auxiliary clinical diagnosis and decision-making, which has a good potential for clinical application for DD and LNM of ECC.

Investigation on the pathways of embryonic stem cells differentiation into insulin-producing cells is of importance to pancreatic tissue-engineering. Instead of passing through the classic multi-step-inducing method, the expanded embryonic stem cells that were cultured and expanded in the presence of mouse embryonic fibroblast feed-layer and leukemia inhibitory factor (LIF) were induced into insulin-producing cells directly. The results showed a similar consequence from two different inducing cultures. Without passing through a so-called indispensable differentiation phase, the neural-precursor-cell-stage, the expanded embryonic stem cells could be induced into insulin-producing cells. The insulin-producing cells population resulting from our modified method were similar to that resulting from the classic multi-step method (passing through the neural-precursor-cells-stage), thus suggesting that neural-precursor-cell-phase is not the indispensable checkpoint of embryonic stem cell differentiation into insulin-producing cells. Embryonic stem cells can be induced into insulin-producing cell by classic multi-step inducing method or by direct inducing method.