Objective:The assessment of lateral lymph node metastasis(LLNM)in patients with papillary thyroid carcinoma(PTC)holds great significance.This study aims to develop and evaluate a deep learning-based automatic pipeline...Objective:The assessment of lateral lymph node metastasis(LLNM)in patients with papillary thyroid carcinoma(PTC)holds great significance.This study aims to develop and evaluate a deep learning-based automatic pipeline system(DLAPS)for diagnosing LLNM in PTC using computed tomography(CT).Methods:A total of 1,266 lateral lymph nodes(LLNs)from 519 PTC patients who underwent CT examinations from January 2019 to November 2022 were included and divided into training and validation set,internal test set,pooled external test set,and prospective test set.The DLAPS consists of an auto-segmentation network based on RefineNet model and a classification network based on ensemble model(ResNet,Xception,and DenseNet).The performance of the DLAPS was compared with that of manually segmented DL models,the clinical model,and Node Reporting and Data System(Node-RADS).The improvement of radiologists’diagnostic performance under the DLAPS-assisted strategy was explored.In addition,bulk RNA-sequencing was conducted based on 12 LLNs to reveal the underlying biological basis of the DLAPS.Results:The DLAPS yielded good performance with area under the receiver operating characteristic curve(AUC)of 0.872,0.910,and 0.822 in the internal,pooled external,and prospective test sets,respectively.The DLAPS significantly outperformed clinical models(AUC 0.731,P<0.001)and Node-RADS(AUC 0.602,P<0.001)in the internal test set.Moreover,the performance of the DLAPS was comparable to that of the manually segmented deep learning(DL)model with AUCs ranging 0.814−0.901 in three test sets.Furthermore,the DLAPSassisted strategy improved the performance of radiologists and enhanced inter-observer consistency.In clinical situations,the rate of unnecessary LLN dissection decreased from 33.33%to 7.32%.Furthermore,the DLAPS was associated with the cell-cell conjunction in the microenvironment.Conclusions:Using CT images from PTC patients,the DLAPS could effectively segment and classify LLNs non-invasively,and this system had a good generalization ability and clinical applicability.展开更多
The development of earth-abundant electrocatalysts with high performance for electrochemical CO_(2)reduction(ECR)is of great significance.Cu-based catalysts have been widely investigated for ECR due to their unique ab...The development of earth-abundant electrocatalysts with high performance for electrochemical CO_(2)reduction(ECR)is of great significance.Cu-based catalysts have been widely investigated for ECR due to their unique ability to generate various carbonaceous products,but directing selectivity toward one certain product and identifying the real active sites during ECR are still full of challenge.Here,after the incorporation of CdO into CuO,the Cu_(0.5)Cd_(0.5)-O catalyst achieves a 10.3-fold enhancement for CO selectivity in comparison with CuO,and a CO faradic efficiency nearly 90%with a current density around20 mA cm^(-2)could maintain at least 60 h.Interestingly,a wide CO/H_(2)ratio(0.07-10)is reached on Cu_(x)Cd_(1-x)-O catalysts by varying the Cu/Cd ratio,demonstrating the potential of syngas production using such catalysts.The results of ex situ XRD,XPS,and in situ Raman reveal that the real active sites of Cu_(0.5)Cd_(0.5)-O catalysts for CO production during ECR reaction are the reconstructed mixed phases of CuCd alloy and CdCO_(3).In situ FTIR and theoretical calculations further implicate the presence of Cd related species promotes the CO desorption and inhibits the H_(2)evolution,thus leading to an enhanced CO generation.展开更多
基金supported by the Taishan Scholar Project(No.ts20190991,No.tsqn202211378)the Key R&D Project of Shandong Province(No.2022CXPT023)+1 种基金the General Program of National Natural Science Foundation of China(No.82371933)the Medical and Health Technology Project of Shandong Province(No.202307010677)。
文摘Objective:The assessment of lateral lymph node metastasis(LLNM)in patients with papillary thyroid carcinoma(PTC)holds great significance.This study aims to develop and evaluate a deep learning-based automatic pipeline system(DLAPS)for diagnosing LLNM in PTC using computed tomography(CT).Methods:A total of 1,266 lateral lymph nodes(LLNs)from 519 PTC patients who underwent CT examinations from January 2019 to November 2022 were included and divided into training and validation set,internal test set,pooled external test set,and prospective test set.The DLAPS consists of an auto-segmentation network based on RefineNet model and a classification network based on ensemble model(ResNet,Xception,and DenseNet).The performance of the DLAPS was compared with that of manually segmented DL models,the clinical model,and Node Reporting and Data System(Node-RADS).The improvement of radiologists’diagnostic performance under the DLAPS-assisted strategy was explored.In addition,bulk RNA-sequencing was conducted based on 12 LLNs to reveal the underlying biological basis of the DLAPS.Results:The DLAPS yielded good performance with area under the receiver operating characteristic curve(AUC)of 0.872,0.910,and 0.822 in the internal,pooled external,and prospective test sets,respectively.The DLAPS significantly outperformed clinical models(AUC 0.731,P<0.001)and Node-RADS(AUC 0.602,P<0.001)in the internal test set.Moreover,the performance of the DLAPS was comparable to that of the manually segmented deep learning(DL)model with AUCs ranging 0.814−0.901 in three test sets.Furthermore,the DLAPSassisted strategy improved the performance of radiologists and enhanced inter-observer consistency.In clinical situations,the rate of unnecessary LLN dissection decreased from 33.33%to 7.32%.Furthermore,the DLAPS was associated with the cell-cell conjunction in the microenvironment.Conclusions:Using CT images from PTC patients,the DLAPS could effectively segment and classify LLNs non-invasively,and this system had a good generalization ability and clinical applicability.
基金financially supported by the National Natural Science Foundation of China with grant number of 22172082 and 21978137。
文摘The development of earth-abundant electrocatalysts with high performance for electrochemical CO_(2)reduction(ECR)is of great significance.Cu-based catalysts have been widely investigated for ECR due to their unique ability to generate various carbonaceous products,but directing selectivity toward one certain product and identifying the real active sites during ECR are still full of challenge.Here,after the incorporation of CdO into CuO,the Cu_(0.5)Cd_(0.5)-O catalyst achieves a 10.3-fold enhancement for CO selectivity in comparison with CuO,and a CO faradic efficiency nearly 90%with a current density around20 mA cm^(-2)could maintain at least 60 h.Interestingly,a wide CO/H_(2)ratio(0.07-10)is reached on Cu_(x)Cd_(1-x)-O catalysts by varying the Cu/Cd ratio,demonstrating the potential of syngas production using such catalysts.The results of ex situ XRD,XPS,and in situ Raman reveal that the real active sites of Cu_(0.5)Cd_(0.5)-O catalysts for CO production during ECR reaction are the reconstructed mixed phases of CuCd alloy and CdCO_(3).In situ FTIR and theoretical calculations further implicate the presence of Cd related species promotes the CO desorption and inhibits the H_(2)evolution,thus leading to an enhanced CO generation.
