目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性...目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性806例,恶性779例,按8∶2分为训练集(n=1268)和测试集(n=317),并按照5折交叉验证对训练集进行分层,采用集成的DDSM数据集和INBreast数据集作为外部测试集(n=1645)来评估模型性能。输入层每个病例包含4个视图,通过删除ResNet22网络模型的最后两层网络结构并加入平均池化层作为特征提取层,以及分别加入全连接层和softmax激活函数作为决策层构建MMV-Net模型,使用贝叶斯超参数优化。比较MMV-Net、MFA-Net和集成Inception V4模型在AUC值、准确率、精确率、召回率和F1分数上的表现。结果MMV-Net模型在测试集上区分良性和恶性肿块的AUC值为0.913,MFA-Net的AUC为0.882,Inception V4的AUC为0.865;MMV-Net模型的准确率和精确率等评估指标也高于其他两种模型。结论基于乳腺X线多视图的深度学习MMV-Net模型有助于乳腺良性和恶性肿块的分类。展开更多
Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years a...Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years after surgery.Methods The clinical,PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed.According to PD or not,the patients were divided into the PD group(n=71)and non-PD group(n=179).The basic data and PET/CT findings were compared between groups,among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic(ROC)curve and corresponding cut-off value.Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery.The patients were divided into training,validation and test sets at the ratio of 6∶2∶2,and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model,and the combination model was built in serial connection of DL model and the predictive factors.In test set,the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve(AUC).Results Patients'gender and smoking status,as well as the long diameter,SUV max and SUV mean of lesions measured on PET images,the long diameter,short diameter and type of lesions showed on CT were statistically different between groups(all P<0.05).Smoking(HR=1.787[1.053,3.031],P=0.031)and lesion SUV max>4.15(HR=5.249[1.062,25.945],P=0.042)were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery.In test set,the AUC of PET/CT DL model for predicting PD was 0.847,of the combination model was 0.890,of the latter was higher than of the former(P=0.036).Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery.Combining with Cox proportional hazard model could further improve its predicting efficacy.展开更多
文摘目的构建基于乳腺X线多视图的深度学习框架(Network based on mammography multiple views,MMV-Net),评价模型对乳腺良性和恶性肿块的分类效能。方法回顾性分析2018-2020年哈尔滨医科大学附属肿瘤医院1585例乳腺X线图像数据集,其中良性806例,恶性779例,按8∶2分为训练集(n=1268)和测试集(n=317),并按照5折交叉验证对训练集进行分层,采用集成的DDSM数据集和INBreast数据集作为外部测试集(n=1645)来评估模型性能。输入层每个病例包含4个视图,通过删除ResNet22网络模型的最后两层网络结构并加入平均池化层作为特征提取层,以及分别加入全连接层和softmax激活函数作为决策层构建MMV-Net模型,使用贝叶斯超参数优化。比较MMV-Net、MFA-Net和集成Inception V4模型在AUC值、准确率、精确率、召回率和F1分数上的表现。结果MMV-Net模型在测试集上区分良性和恶性肿块的AUC值为0.913,MFA-Net的AUC为0.882,Inception V4的AUC为0.865;MMV-Net模型的准确率和精确率等评估指标也高于其他两种模型。结论基于乳腺X线多视图的深度学习MMV-Net模型有助于乳腺良性和恶性肿块的分类。
文摘Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years after surgery.Methods The clinical,PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed.According to PD or not,the patients were divided into the PD group(n=71)and non-PD group(n=179).The basic data and PET/CT findings were compared between groups,among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic(ROC)curve and corresponding cut-off value.Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery.The patients were divided into training,validation and test sets at the ratio of 6∶2∶2,and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model,and the combination model was built in serial connection of DL model and the predictive factors.In test set,the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve(AUC).Results Patients'gender and smoking status,as well as the long diameter,SUV max and SUV mean of lesions measured on PET images,the long diameter,short diameter and type of lesions showed on CT were statistically different between groups(all P<0.05).Smoking(HR=1.787[1.053,3.031],P=0.031)and lesion SUV max>4.15(HR=5.249[1.062,25.945],P=0.042)were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery.In test set,the AUC of PET/CT DL model for predicting PD was 0.847,of the combination model was 0.890,of the latter was higher than of the former(P=0.036).Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery.Combining with Cox proportional hazard model could further improve its predicting efficacy.
