BACKGROUND The accuracy of discriminating pT3a from pT3b-c rectal cancer using highresolution magnetic resonance imaging(MRI)remains unsatisfactory,although texture analysis(TA)could improve such discrimination.AIM To...BACKGROUND The accuracy of discriminating pT3a from pT3b-c rectal cancer using highresolution magnetic resonance imaging(MRI)remains unsatisfactory,although texture analysis(TA)could improve such discrimination.AIM To investigate the value of TA on apparent diffusion coefficient(ADC)maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors.METHODS This was a case-control study of 59 patients with pT3 rectal adenocarcinoma,who underwent diffusion-weighted imaging(DWI)between October 2016 and December 2018.The inclusion criteria were:(1)Proven pT3 rectal adenocarcinoma;(2)Primary MRI including high-resolution T2-weighted image(T2WI)and DWI;and(3)Availability of pathological reports for surgical specimens.The exclusion criteria were:(1)Poor image quality;(2)Preoperative chemoradiation therapy;and(3)A different pathological type.First-order(ADC values,skewness,kurtosis,and uniformity)and second-order(energy,entropy,inertia,and correlation)texture features were derived from whole-lesion ADC maps.Receiver operating characteristic curves were used to determine the diagnostic value for pT3b-c tumors.RESULTS The final study population consisted of 59 patients(34 men and 25 women),with a median age of 66 years(range,41-85 years).Thirty patients had pT3a,24 had pT3b,and five had pT3c.Among the ADC first-order textural differences between pT3a and pT3b-c rectal adenocarcinomas,only skewness was significantly lower in the pT3a tumors than in pT3b-c tumors.Among the ADC second-order textural differences,energy and entropy were significantly different between pT3a and pT3b-c rectal adenocarcinomas.For differentiating pT3a rectal adenocarcinomas from pT3b-c tumors,the areas under the curves(AUCs)of skewness,energy,and entropy were 0.686,0.657,and 0.747,respectively.Logistic regression analysis of all three features yielded a greater AUC(0.775)in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors(69.0%sensitivity and 83.3%specificity).CONCLUSION TA features derived from ADC maps might potentially differentiate pT3a rectal adenocarcinomas from pT3b-c tumors.展开更多
In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be...In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization（EM）algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly,we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements.These results are confirmed by our simulations.展开更多
基金Jiangsu Provincial Medical Youth Talent,No.QNRC2016212Suzhou Clinical Special Disease Diagnosis and Treatment Program,No.LCZX201823+2 种基金Suzhou GuSu Medical Talent Project,No.GSWS2019077The Science and Technology Bureau of Changshu,No.CS201624(to Lu ZH)Jiangsu Committee of Health,No.H2018071(to Xia KJ).
文摘BACKGROUND The accuracy of discriminating pT3a from pT3b-c rectal cancer using highresolution magnetic resonance imaging(MRI)remains unsatisfactory,although texture analysis(TA)could improve such discrimination.AIM To investigate the value of TA on apparent diffusion coefficient(ADC)maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors.METHODS This was a case-control study of 59 patients with pT3 rectal adenocarcinoma,who underwent diffusion-weighted imaging(DWI)between October 2016 and December 2018.The inclusion criteria were:(1)Proven pT3 rectal adenocarcinoma;(2)Primary MRI including high-resolution T2-weighted image(T2WI)and DWI;and(3)Availability of pathological reports for surgical specimens.The exclusion criteria were:(1)Poor image quality;(2)Preoperative chemoradiation therapy;and(3)A different pathological type.First-order(ADC values,skewness,kurtosis,and uniformity)and second-order(energy,entropy,inertia,and correlation)texture features were derived from whole-lesion ADC maps.Receiver operating characteristic curves were used to determine the diagnostic value for pT3b-c tumors.RESULTS The final study population consisted of 59 patients(34 men and 25 women),with a median age of 66 years(range,41-85 years).Thirty patients had pT3a,24 had pT3b,and five had pT3c.Among the ADC first-order textural differences between pT3a and pT3b-c rectal adenocarcinomas,only skewness was significantly lower in the pT3a tumors than in pT3b-c tumors.Among the ADC second-order textural differences,energy and entropy were significantly different between pT3a and pT3b-c rectal adenocarcinomas.For differentiating pT3a rectal adenocarcinomas from pT3b-c tumors,the areas under the curves(AUCs)of skewness,energy,and entropy were 0.686,0.657,and 0.747,respectively.Logistic regression analysis of all three features yielded a greater AUC(0.775)in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors(69.0%sensitivity and 83.3%specificity).CONCLUSION TA features derived from ADC maps might potentially differentiate pT3a rectal adenocarcinomas from pT3b-c tumors.
基金supported by the National Natural Science Foundation of China(No.61601254)the KC Wong Magna Fund of Ningbo University,China
文摘In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization（EM）algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly,we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements.These results are confirmed by our simulations.
