Objective To evaluate the combination use of transcatheter arterial chemoembolization (TACE), portal vein embolization (PVE) and high intensity focused ultrasound (HIFU) in treating portal vein tumor thrombus(PVTT). Methods A total of 85 patients with primary hepatocellular carcinoma complicated by PVTT, who were encountered during the period from Jan. 2011 to Feb. 2012 at authors’ hospital, were enrolled in this study. The patients were divided into the study group (n=47) and the control group (n=38). TACE, PVE and HIFU were performed in the patients of the study group, while only TACE and PVE were carried out in the patients of the control group. The therapeutic process was as follows: PVE was carried out 2 weeks after TACE was performed, and for the patients of the study group additional HIFU was conducted about 10 days after PVE procedure. Results The short-term effective rate in the study group and the control group was 89.4% (42/47) and 39.5% (15/38) respectively, and the difference between the two groups was statistically significant (P< 0.05). The 6-month, one-year, and two-year survival rate in the study group were 87.2%(41/47), 66.0%(31/47) and 27.7%(13/47) respectively; the median survival time was 15.4 months. In the control group, the 6-month, one-year, and two-year survival rate were 55.3% (21/38), 39.5% (15/38) and 10.5%(4/38) respectively;the median survival time was 10.3 months. The differences between the two groups were statistically significant (P< 0.05). Conclusion For the treatment of primary hepatocellular carcinoma associated with portal vein tumor thrombus, transcatheter arterial chemoembolization, portal vein embolization together with high intensity focused ultrasound is an safe and effective therapy as it can significantly improve the therapeutic effect and prolong the survival time.

Objective:To explore the effectiveness and feasibility of the machine learning models based on radiomics in the diagnosis of pituitary prolactin macroadenoma.Methods:Totally 122 histologically proven pituitary macroadenoma patients, including 70 cases of pituitary prolactin macroadenoma (PPM) and 52 cases of non-pituitary prolactin macroadenoma (NPPM), were retrospectively recruited. The differences of age, sex, serum prolactin value, bleeding, cystic degeneration and Knosp classification were compared between PPM and NPPM. The pre-processing, delineation of the region of interest and feature extraction of the preoperative axial contrast-enhanced T 1WI image were performed in the 3Dslicer software. The optimal feature set were selected by least absolute shrinkage and selection operator. All patients were randomly divided into the training group ( n=85) and the test group ( n=37) at a ratio of 7∶3. The models were established in the training group by logistic regression and support vector machine (SVM), and then verified by the test group. ROC curves were drawn respectively, and specificity, sensitivity, accuracy and area under the ROC curve (AUC) were calculated. Results:The age [(38±12) years vs . (43±11) years], gender ratio (male/female 50 cases/20 cases vs . 14 cases/38 cases) and prolactin value [366.00 (117.75, 1 156.25)μg/L vs . 47.25 (32.68, 62.40) μg/L] of patients with PPM and NPPM were statistically different ( P<0.05). The AUC values of logistic regression and SVM in the training group were 0.936 and 0.946, and the AUC values of the test group were 0.768 and 0.774, respectively. The diagnostic accuracy of logistic regression and SVM in the training group were 88.2% and 91.8%, and the accuracy of the test group were 73.0% and 77.8%. Conclusion:The machine learning models based on the radiomics can predict the pituitary prolactin macroadenoma well with a high accuracy.