摘要
Background： Effective methods for managing patients with solitary pulmonary nodules（SPNs） depend critically on the predictive probability of malignancy.Methods： Between July 2009 and June 2011, data on gender, age, cancer history, tumor familial history, smoking status, tumor location, nodule size, spiculation, calcification, the tumor border, and the final pathological diagnosis were collected retrospectively from 154 surgical patients with an SPN measuring 3-30 mm. Each final diagnosis was compared with the probability calculated by three predicted models—the Mayo, VA, and Peking University（PU） models. The accuracy of each model was assessed using area under the receiver operating characteristics（ROC） and calibration curves.Results： The area under the ROC curve of the PU model [0.800; 95% confidence interval（CI）： 0.708-0.891] was higher than that of the Mayo model（0.753; 95% CI： 0.650-0.857） or VA model（0.728; 95% CI： 0.623-0.833）; however, this finding was not statistically significant. To varying degrees, calibration curves showed that all three models overestimated malignancy.Conclusions： The three predicted models have similar accuracy for prediction of SPN malignancy, although the accuracy is not sufficient. For Chinese patients, the PU model may has greater predictive power.Background： Here, we introduced our short experience on the application of a new CUSA Excel ultrasonic aspiration system, which was provided by Integra Lifesciences corporation, in skull base meningiomas resection.Methods： Ten patients with anterior, middle skull base and sphenoid ridge meningioma were operated using the CUSA Excel ultrasonic aspiration system at the Neurosurgery Department of Shanghai Huashan Hospital from August 2014 to October 2014. There were six male and four female patients, aged from 38 to 61 years old（the mean age was 48.5 years old）. Five cases with tumor located at anterior skull base, three cases with tumor on middle skull base, and two cases with tumor on sphe
Background： Effective methods for managing patients with solitary pulmonary nodules（SPNs） depend critically on the predictive probability of malignancy.Methods： Between July 2009 and June 2011, data on gender, age, cancer history, tumor familial history, smoking status, tumor location, nodule size, spiculation, calcification, the tumor border, and the final pathological diagnosis were collected retrospectively from 154 surgical patients with an SPN measuring 3-30 mm. Each final diagnosis was compared with the probability calculated by three predicted models—the Mayo, VA, and Peking University（PU） models. The accuracy of each model was assessed using area under the receiver operating characteristics（ROC） and calibration curves.Results： The area under the ROC curve of the PU model [0.800; 95% confidence interval（CI）： 0.708-0.891] was higher than that of the Mayo model（0.753; 95% CI： 0.650-0.857） or VA model（0.728; 95% CI： 0.623-0.833）; however, this finding was not statistically significant. To varying degrees, calibration curves showed that all three models overestimated malignancy.Conclusions： The three predicted models have similar accuracy for prediction of SPN malignancy, although the accuracy is not sufficient. For Chinese patients, the PU model may has greater predictive power.Background： Here, we introduced our short experience on the application of a new CUSA Excel ultrasonic aspiration system, which was provided by Integra Lifesciences corporation, in skull base meningiomas resection.Methods： Ten patients with anterior, middle skull base and sphenoid ridge meningioma were operated using the CUSA Excel ultrasonic aspiration system at the Neurosurgery Department of Shanghai Huashan Hospital from August 2014 to October 2014. There were six male and four female patients, aged from 38 to 61 years old（the mean age was 48.5 years old）. Five cases with tumor located at anterior skull base, three cases with tumor on middle skull base, and two cases with tumor on sphe
基金
supported by National Natural Science Foundation of China (81200936, 81372707)
Shanghai Committee of Science and Technology (12JC1401800)
