Objective The purpose of this study is to appraise the value of incremental dynamic enhanced computed tomography in surgical treatment of patients with solitary pulmonary nodules(SPNs).Methods The data of 42 cases wit...Objective The purpose of this study is to appraise the value of incremental dynamic enhanced computed tomography in surgical treatment of patients with solitary pulmonary nodules(SPNs).Methods The data of 42 cases with solitary pulmonary nodules who underwent surgical treatment from May 2002 to June 2003 in our hospital were collected to find the relationship between preoperative dynamic enhanced CT image and postoperative pathology.Result All bronchogenic carcinoma showed significant enhancement after intravenous 100 mL iodinated contrast material.The average degree of enhancement of bronchogenic carcinoma was significantly different from that of tuberculoma and other benign lesions.Conclusion Dynamic enhanced computed tomography is very valuable in distinguishing between malignant nodules and benign ones.Emphasis should be paid to lymph nodes in the dynamic enhanced computed tomography,which is useful both to the diagnosis of SPN and for surgical treatment.展开更多
The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomogr...The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.展开更多
In order to prospectively assess various parameters of diffusion weighted imaging (DWI)in differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs),58 patients (40 men and 18 women,and mean age ...In order to prospectively assess various parameters of diffusion weighted imaging (DWI)in differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs),58 patients (40 men and 18 women,and mean age of 48.1±10.4years old) with SPNs undergoing conventional MR,DWI using b=500s/mm^2 on a 1.5T MR scanner, were studied.Various DWI parameters [apparent diffusion coefficient (ADC),lesion-to-spinal cord signal intensity ratio (LSR),signal intensity (SI)score] were calculated and compared between malignant and benign SPNs groups.A receiver operating characteristic (ROC)curve analysis was employed to compare the diagnostic capabilities of all the parameters for discrimination between benign and malignant SPNs.The results showed that there were 42 malignant and 16 benign SPNs.The ADC was significantly,lower in malignant SPNs (1.40±0.44)×10^-3mm^2/s than in benign SPNs (1.81±0.58)×10^-3mm^2/ s.The LSR and SI scores were significantly increased in malignant SPNs (0.90±0.37 and 2.8±1.2)as compared with those in benign SPNs (0.68±0.39 and 2.2±1.2).The area under the ROC curves (AUC)of all parameters was not significantly different between malignant SPNs and benign SPNs.It was suggested that as three reported parameters for DWI,ADC,LSR and SI scores are all feasible for discrimination of malignant and benign SPNs.The three parameters have equal diagnostic performance.展开更多
Background Computer-aided diagnosis （CAD） of lung cancer is the subject of many current researches. Statistical methods and artificial neural networks have been applied to more quantitatively characterize solitary p...Background Computer-aided diagnosis （CAD） of lung cancer is the subject of many current researches. Statistical methods and artificial neural networks have been applied to more quantitatively characterize solitary pulmonary nodules （SPNs）. In this study, we developed a CAD scheme based on an artificial neural network to distinguish malignant from benign SPNs on thin-section computed tomography （CT） images, and investigated how the CAD scheme can help radiologists with different levels of experience make diagnostic decisions. Methods Two hundred thin-section CT images of SPNs with proven diagnoses （135 small peripheral lung cancers and 65 benign nodules） were analyzed. Three clinical features and nine CT signs of each case were studied by radiologists, and the indices of qualitative diagnosis were quantified. One hundred and forty nodules were selected randomly to form training samples, on which the neural network model was built. The remaining 60 nodules, forming test samples, were presented to 9 radiologists with 3-20 years of clinical experience, accompanied by standard reference images. The radiologists were asked to determine whether a nodule was malignant or benign first without and then with CAD output. Diagnostic performance was evaluated by receiver operating characteristic （ROC） analysis. Results CAD outputs on test samples had higher agreement with pathological diagnoses （Kappa=0.841, P〈0.001）. Compared with diagnostic results without CAD output, the average area under the ROC curve with CAD output was 0.96 （P〈0.001） for junior radiologists, 0.94 （P=0.014） for secondary radiologists and 0.96 （P=0.221） for senior radiologists, respectively. The differences in diagnostic performance with CAD output among the three levels of radiologists were not statistically significant （P=0.584, 0.920 and 0.707, respectively）. Conclusions This CAD scheme based on an artificial neural network could improve diagnostic performance and assist radiologists in distinguishing malignant from benign SPNs on thin-section CT images.展开更多
The differential diagnosis of solitary pulmonary nodules （SPNs） remains a challenge. It is acknowledged that combining positron-emission tomography （PET） and computed tomography （CT） offers the most reliable non...The differential diagnosis of solitary pulmonary nodules （SPNs） remains a challenge. It is acknowledged that combining positron-emission tomography （PET） and computed tomography （CT） offers the most reliable noninvasive method for the diagnosis of SPNs. Since Townsend et al1 developed integrated PET/CT in 1999, this technique has increasingly been introduced into clinical practice. To date, nuclear medicine physicians have usually undertaken PET/CT diagnosis, but the question is surfacing as how to make full use of the information of CT image to improve the accuracy of SPN diagnosis. To answer this question, we performed a retrospective study on 60 patients with SPNs.展开更多
文摘Objective The purpose of this study is to appraise the value of incremental dynamic enhanced computed tomography in surgical treatment of patients with solitary pulmonary nodules(SPNs).Methods The data of 42 cases with solitary pulmonary nodules who underwent surgical treatment from May 2002 to June 2003 in our hospital were collected to find the relationship between preoperative dynamic enhanced CT image and postoperative pathology.Result All bronchogenic carcinoma showed significant enhancement after intravenous 100 mL iodinated contrast material.The average degree of enhancement of bronchogenic carcinoma was significantly different from that of tuberculoma and other benign lesions.Conclusion Dynamic enhanced computed tomography is very valuable in distinguishing between malignant nodules and benign ones.Emphasis should be paid to lymph nodes in the dynamic enhanced computed tomography,which is useful both to the diagnosis of SPN and for surgical treatment.
文摘The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.
文摘In order to prospectively assess various parameters of diffusion weighted imaging (DWI)in differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs),58 patients (40 men and 18 women,and mean age of 48.1±10.4years old) with SPNs undergoing conventional MR,DWI using b=500s/mm^2 on a 1.5T MR scanner, were studied.Various DWI parameters [apparent diffusion coefficient (ADC),lesion-to-spinal cord signal intensity ratio (LSR),signal intensity (SI)score] were calculated and compared between malignant and benign SPNs groups.A receiver operating characteristic (ROC)curve analysis was employed to compare the diagnostic capabilities of all the parameters for discrimination between benign and malignant SPNs.The results showed that there were 42 malignant and 16 benign SPNs.The ADC was significantly,lower in malignant SPNs (1.40±0.44)×10^-3mm^2/s than in benign SPNs (1.81±0.58)×10^-3mm^2/ s.The LSR and SI scores were significantly increased in malignant SPNs (0.90±0.37 and 2.8±1.2)as compared with those in benign SPNs (0.68±0.39 and 2.2±1.2).The area under the ROC curves (AUC)of all parameters was not significantly different between malignant SPNs and benign SPNs.It was suggested that as three reported parameters for DWI,ADC,LSR and SI scores are all feasible for discrimination of malignant and benign SPNs.The three parameters have equal diagnostic performance.
基金This work was supported by a grant from Beijing Natural Science Foundation(No.7062020).
文摘Background Computer-aided diagnosis （CAD） of lung cancer is the subject of many current researches. Statistical methods and artificial neural networks have been applied to more quantitatively characterize solitary pulmonary nodules （SPNs）. In this study, we developed a CAD scheme based on an artificial neural network to distinguish malignant from benign SPNs on thin-section computed tomography （CT） images, and investigated how the CAD scheme can help radiologists with different levels of experience make diagnostic decisions. Methods Two hundred thin-section CT images of SPNs with proven diagnoses （135 small peripheral lung cancers and 65 benign nodules） were analyzed. Three clinical features and nine CT signs of each case were studied by radiologists, and the indices of qualitative diagnosis were quantified. One hundred and forty nodules were selected randomly to form training samples, on which the neural network model was built. The remaining 60 nodules, forming test samples, were presented to 9 radiologists with 3-20 years of clinical experience, accompanied by standard reference images. The radiologists were asked to determine whether a nodule was malignant or benign first without and then with CAD output. Diagnostic performance was evaluated by receiver operating characteristic （ROC） analysis. Results CAD outputs on test samples had higher agreement with pathological diagnoses （Kappa=0.841, P〈0.001）. Compared with diagnostic results without CAD output, the average area under the ROC curve with CAD output was 0.96 （P〈0.001） for junior radiologists, 0.94 （P=0.014） for secondary radiologists and 0.96 （P=0.221） for senior radiologists, respectively. The differences in diagnostic performance with CAD output among the three levels of radiologists were not statistically significant （P=0.584, 0.920 and 0.707, respectively）. Conclusions This CAD scheme based on an artificial neural network could improve diagnostic performance and assist radiologists in distinguishing malignant from benign SPNs on thin-section CT images.
文摘The differential diagnosis of solitary pulmonary nodules （SPNs） remains a challenge. It is acknowledged that combining positron-emission tomography （PET） and computed tomography （CT） offers the most reliable noninvasive method for the diagnosis of SPNs. Since Townsend et al1 developed integrated PET/CT in 1999, this technique has increasingly been introduced into clinical practice. To date, nuclear medicine physicians have usually undertaken PET/CT diagnosis, but the question is surfacing as how to make full use of the information of CT image to improve the accuracy of SPN diagnosis. To answer this question, we performed a retrospective study on 60 patients with SPNs.
