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 maligna展开更多
Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance im...Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging(MRI), image features from T2-weighted images(T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone(PZ) and central gland(CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features(10/12) had significant difference(P<0.01) between PCa and non-PCa in the PZ, while only five features(sum average, minimum value, standard deviation, 10 th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2 w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.展开更多
目的应用分类决策树(classification and regression tree,CART)算法构建胸片鉴别新生儿透明膜病及湿肺病的诊断模型,探讨多种临床及影像因素对肺透明膜病及湿肺病的诊断价值。方法病例为2008年1月～2010年12月间经过临床及影像证实的...目的应用分类决策树(classification and regression tree,CART)算法构建胸片鉴别新生儿透明膜病及湿肺病的诊断模型,探讨多种临床及影像因素对肺透明膜病及湿肺病的诊断价值。方法病例为2008年1月～2010年12月间经过临床及影像证实的新生儿肺透明膜病43例、湿肺病48例。分别提取和上述两种疾病有关的6个临床指标和7个影像学指标作为CART预测新生儿肺透明膜病及湿肺病的变量。用CART建立两者鉴别诊断的分类决策模型,并通过交互验证方法计算该模型的诊断可靠性。同时比较高年资医师与CART诊断结果的一致性。结果建立的CART诊断模型共有九条诊断路径,能够比较可靠区分新生儿肺透明病及湿肺病;模型揭示对区分两者最具价值的X线征象是:支气管气像、孕周、毛玻璃样改变和水平裂的出现。另外,统计分析显示,CART模型和高年资医师对两种疾病诊断的一致性分别是中度一致(湿肺病,Kappa值为0.553)和较高度一致(肺透明膜病,Kappa值为0.628)。结论分类决策树算法可以应用于新生儿的肺透明病及湿肺病的鉴别诊断。展开更多
Diffusion-weighted imaging(DWI) is considered to be one of the dominant modalities used in prostate cancer(PCa) detection and the assessment of lesion aggressiveness,especially for peripheral zone(PZ) PCa.Computer-aid...Diffusion-weighted imaging(DWI) is considered to be one of the dominant modalities used in prostate cancer(PCa) detection and the assessment of lesion aggressiveness,especially for peripheral zone(PZ) PCa.Computer-aided diagnosis(CAD),which is capable of automatically extracting and evaluating image features,can integrate multiple parameters and improve the detection of PCa.In this study,13 quantitative image features were extracted from DWI by CAD,and diagnostic efficacy was analyzed in both the PZ and transition zone(TZ).The results demonstrated that there was a significant difference(P<0.05) between PCa and non-PCa for nine of the 13 features in the PZ and five of the 13 features in the TZ.Besides,the prediction outcome of CAD had a strong correlation with the DWI scores that were graded by experienced radiologists according to the Prostate Imaging-Reporting and Data System Version 2(PI-RADS v2).展开更多
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 maligna
文摘Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging(MRI), image features from T2-weighted images(T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone(PZ) and central gland(CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features(10/12) had significant difference(P<0.01) between PCa and non-PCa in the PZ, while only five features(sum average, minimum value, standard deviation, 10 th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2 w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.
文摘目的应用分类决策树(classification and regression tree,CART)算法构建胸片鉴别新生儿透明膜病及湿肺病的诊断模型,探讨多种临床及影像因素对肺透明膜病及湿肺病的诊断价值。方法病例为2008年1月～2010年12月间经过临床及影像证实的新生儿肺透明膜病43例、湿肺病48例。分别提取和上述两种疾病有关的6个临床指标和7个影像学指标作为CART预测新生儿肺透明膜病及湿肺病的变量。用CART建立两者鉴别诊断的分类决策模型,并通过交互验证方法计算该模型的诊断可靠性。同时比较高年资医师与CART诊断结果的一致性。结果建立的CART诊断模型共有九条诊断路径,能够比较可靠区分新生儿肺透明病及湿肺病;模型揭示对区分两者最具价值的X线征象是:支气管气像、孕周、毛玻璃样改变和水平裂的出现。另外,统计分析显示,CART模型和高年资医师对两种疾病诊断的一致性分别是中度一致(湿肺病,Kappa值为0.553)和较高度一致(肺透明膜病,Kappa值为0.628)。结论分类决策树算法可以应用于新生儿的肺透明病及湿肺病的鉴别诊断。
文摘Diffusion-weighted imaging(DWI) is considered to be one of the dominant modalities used in prostate cancer(PCa) detection and the assessment of lesion aggressiveness,especially for peripheral zone(PZ) PCa.Computer-aided diagnosis(CAD),which is capable of automatically extracting and evaluating image features,can integrate multiple parameters and improve the detection of PCa.In this study,13 quantitative image features were extracted from DWI by CAD,and diagnostic efficacy was analyzed in both the PZ and transition zone(TZ).The results demonstrated that there was a significant difference(P<0.05) between PCa and non-PCa for nine of the 13 features in the PZ and five of the 13 features in the TZ.Besides,the prediction outcome of CAD had a strong correlation with the DWI scores that were graded by experienced radiologists according to the Prostate Imaging-Reporting and Data System Version 2(PI-RADS v2).
