Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can expli...Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can explicitly delineate the pathological condition of the lungs.To meet the imperative for accurate diagnosis by physicians,expeditious segmentation of the region harboring lung cancer is of utmost significance.We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner,erect an interpretable model,and attain segmentation of lung cancer.The specific advancements can be encapsulated as follows:1)Concentration on the lung parenchyma region:Based on 16-bit CT image capturing and the luminance characteristics of lung cancer,we proffer an intercept histogram algorithm.2)Focus on the specific locus of lung malignancy:Utilizing the spatial interrelation of lung cancer,we propose a memory-based Unet architecture and incorporate skip connections.3)Data Imbalance:In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples,we scrutinize the existing loss function and suggest a mixed loss function.Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy,measured as Area Overlap Measure(AOM)is superior to 0.81,which markedly ameliorates in comparison with conventional algorithms,thereby facilitating physicians in diagnosis.展开更多
BACKGROUND Pulmonary tuberculosis(TB)and lung cancer(LC)are common diseases with a high incidence and similar symptoms,which may be misdiagnosed by radiologists,thus delaying the best treatment opportunity for patient...BACKGROUND Pulmonary tuberculosis(TB)and lung cancer(LC)are common diseases with a high incidence and similar symptoms,which may be misdiagnosed by radiologists,thus delaying the best treatment opportunity for patients.AIM To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography(CT)images.METHODS We enrolled 478 patients(January 2012 to October 2018),who underwent preoperative CT screening.Radiomics features were extracted and selected from the CT data to establish a logistic regression model.A radiomics nomogram model was constructed,with the receiver operating characteristic,decision and calibration curves plotted to evaluate the discriminative performance.RESULTS Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance.The radiomics nomogram model exhibited good discrimination,with an area under the curve of 0.914(sensitivity=0.890,specificity=0.796)in the training cohort,and 0.900(sensitivity=0.788,specificity=0.907)in the validation cohort.The decision curve analysis revealed that the constructed nomogram had clinical usefulness.CONCLUSION These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.展开更多
Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology.Modern medical care and imaging technology are becoming increasingly inseparable.However,the current diagnosis ...Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology.Modern medical care and imaging technology are becoming increasingly inseparable.However,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows.In this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing images.Moreover,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not possess.Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.展开更多
基金This work is supported by Light of West China(No.XAB2022YN10).
文摘Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can explicitly delineate the pathological condition of the lungs.To meet the imperative for accurate diagnosis by physicians,expeditious segmentation of the region harboring lung cancer is of utmost significance.We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner,erect an interpretable model,and attain segmentation of lung cancer.The specific advancements can be encapsulated as follows:1)Concentration on the lung parenchyma region:Based on 16-bit CT image capturing and the luminance characteristics of lung cancer,we proffer an intercept histogram algorithm.2)Focus on the specific locus of lung malignancy:Utilizing the spatial interrelation of lung cancer,we propose a memory-based Unet architecture and incorporate skip connections.3)Data Imbalance:In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples,we scrutinize the existing loss function and suggest a mixed loss function.Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy,measured as Area Overlap Measure(AOM)is superior to 0.81,which markedly ameliorates in comparison with conventional algorithms,thereby facilitating physicians in diagnosis.
基金Supported by Youth Science and Technology Innovation Leader Support Project,No.RC170497Shenyang Municipal Science and Technology Project,No.F16-206-9-23+5 种基金Natural Science Foundation of Liaoning Province of China,No.201602450National Key R&D Program of Ministry of Science and Technology of China,No.2016YFC1303002National Natural Science Foundation of China,No.81872363Major Technology Plan Project of Shenyang,No.17-230-9-07Supporting Fund for Big data in Health Care,No.HMB2019031012018 Key Research and Guidance Project of Liaoning Province,No.2018225038.
文摘BACKGROUND Pulmonary tuberculosis(TB)and lung cancer(LC)are common diseases with a high incidence and similar symptoms,which may be misdiagnosed by radiologists,thus delaying the best treatment opportunity for patients.AIM To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography(CT)images.METHODS We enrolled 478 patients(January 2012 to October 2018),who underwent preoperative CT screening.Radiomics features were extracted and selected from the CT data to establish a logistic regression model.A radiomics nomogram model was constructed,with the receiver operating characteristic,decision and calibration curves plotted to evaluate the discriminative performance.RESULTS Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance.The radiomics nomogram model exhibited good discrimination,with an area under the curve of 0.914(sensitivity=0.890,specificity=0.796)in the training cohort,and 0.900(sensitivity=0.788,specificity=0.907)in the validation cohort.The decision curve analysis revealed that the constructed nomogram had clinical usefulness.CONCLUSION These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.
基金supported by the National Key Research and Development Program of China (2017YFA0205200,2023YFC2415200,2021YFF1201003,and 2021YFC2500402)the National Natural Science Foundation of China (82022036,91959130,81971776,62027901,81930053,81771924,62333022,82361168664,62176013,and 82302317)+5 种基金the Beijing Natural Science Foundation (Z20J00105)Strategic Priority Research Program of Chinese Academy of Sciences (XDB38040200)Chinese Academy of Sciences (GJJSTD20170004 and QYZDJ-SSW-JSC005)the Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai HLHPTP201703)the Youth Innovation Promotion Association CAS (Y2021049)the China Postdoctoral Science Foundation (2021M700341).
文摘Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology.Modern medical care and imaging technology are becoming increasingly inseparable.However,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows.In this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing images.Moreover,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not possess.Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.
文摘目的探讨肺部纯磨玻璃结节(p GGN)高分辨率CT影像特征,鉴别诊断浸润性肺腺癌与浸润前病变。方法分析85例高分辨靶扫描影像表现为p GGN且最大径>5mm的99个结节,根据手术病理分为浸润前病变组(AAH+AIS;79个)和浸润性病变组(MIA+IAC;20个)。图像评价内容包括病变部位、大小、密度、边界、病变边缘、形状、病变内部和周边征象(空泡征、支气管充气征、胸膜凹陷征、血管集束征)。结果两组患者病变部位、密度、边界、空泡征及空气支气管征差异均无统计学意义(均P>0.05)。浸润性病变组分叶/毛刺出现率(80.0%)高于浸润前病变组(38.0%)(P<0.01),胸膜凹陷征及血管集束征出现率(35.0%、50.0%)也高于浸润前病变组(2.5%、25.3%)(均P<0.05)。ROC曲线显示以病变直径10mm为分割值,其曲线下面积为0.78,区分浸润性病变的敏感度和特异度分别为60.0%和75.9%。结论 p GGN病灶>10mm,有分叶/毛刺、胸膜凹陷征及血管集束征,提示肿瘤有浸润性。
