In order to explore the application of the dielectric properties of white matter and grey matter in β, δ and γ dispersion transition zones used in clinical medicine and microwave imaging technology, we calculated the dielectric constant and its increment by using Cole-Cole equation. Based on the mutation of the increment of dielectric constant, the frequency range of three dispersions were evaluated. The dominate dispersion and the corresponding polarization mechanism were analyzed by using Cole-Cole circle. The results showed that there are 3 transition zones in brain white matter, which occur between β and δ dispersion, δ and γ dispersion and β and γ dispersion respectively. In grey matter, there are only 2 transition zones, which are between β and δ dispersion and δ and γ dispersion respectively. By comparing the frequency range of white matter and grey matter, the frequency range in white matter is broader than that in grey matter for the transition zone of β and δ dispersion with the β dispersion occupying dominate position in both tissues, and the corresponding polarization mechanism is interfacial polarization. For the transition zone of δ and γ dispersion, the frequency range in white matter is also broader than that in grey matter with the δ dispersion occupying dominate position in both tissues, and the corresponding polarization mechanism is orientation polarization. This study can provide basic theory and reference for diagnosis of brain diseases and microwave imaging technology.
ObjectiveTo develop and validate a nomogram for predicting the cancer-specific survival in patients with intrahepatic cholangiocarcinoma (ICC) after hepatectomy. MethodsSuitable patient cases were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were established based on the independent prognostic factors identified by COX and Lasso regression models. The performance of the nomograms was validated internally and externally by using the concordance index (c-index), calibration plot, and decision curve analysis. ResultsThe multi factor COX regression results showed that: age, gender, T stage, tumor grade, tumour diameter and number of positive lymph nodes were independent prognostic predictors for cancer-specific survival (CSS) in ICC patients. Nomogram predicting CSS had a c-index of 0.66 (95%CI 0.64 to 0.69) in the training cohort and 0.67 (95%CI 0.63 to 0.72) in the internal validation cohort. The 1-, 3- and 5-year areas under the curve (AUC) of nomogram were 0.68, 0.74 and 0.75 in the training cohort respectively. In the validation cohort, the 1-, 3- and 5-year AUC of nomogram were 0.69, 0.68 and 0.71, respectively. ConclusionThe prediction model constructed based on six factors, including age, gender, pathological stage, T-stage, tumour diameter and number of positive lymph nodes, shows good prediction accuracy.