In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(M...In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.展开更多
By linear perturbation theory, a sensitivity study is presented to calculate the contribution of the Mars gravity field to the orbital perturbations in velocity for spacecrafts in both low eccentricity Mars orbits and...By linear perturbation theory, a sensitivity study is presented to calculate the contribution of the Mars gravity field to the orbital perturbations in velocity for spacecrafts in both low eccentricity Mars orbits and high eccentricity orbits（HEOs）. In order to improve the solution of some low degree/order gravity coefficients, a method of choosing an appropriate semimajor axis is often used to calculate an expected orbital resonance, which will significantly amplify the magnitude of the position and velocity perturbations produced by certain gravity coefficients. We can then assess to what degree/order gravity coefficients can be recovered from the tracking data of the spacecraft. However, this existing method can only be applied to a low eccentricity orbit, and is not valid for an HEO. A new approach to choosing an appropriate semimajor axis is proposed here to analyze an orbital resonance. This approach can be applied to both low eccentricity orbits and HEOs. This small adjustment in the semimajor axis can improve the precision of gravity field coefficients and does not affect other scientific objectives.展开更多
Nuclear magnetic resonance gyroscopes （NMRGs） are a kind of rotation-speed sensor that senses the angular velocity by measuring a frequency shift in the Larmor pre- cession of the nuclear spin in a constant magnetic...Nuclear magnetic resonance gyroscopes （NMRGs） are a kind of rotation-speed sensor that senses the angular velocity by measuring a frequency shift in the Larmor pre- cession of the nuclear spin in a constant magnetic field.展开更多
文摘In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.
基金Supported by the National Natural Science Foundation of China
文摘By linear perturbation theory, a sensitivity study is presented to calculate the contribution of the Mars gravity field to the orbital perturbations in velocity for spacecrafts in both low eccentricity Mars orbits and high eccentricity orbits（HEOs）. In order to improve the solution of some low degree/order gravity coefficients, a method of choosing an appropriate semimajor axis is often used to calculate an expected orbital resonance, which will significantly amplify the magnitude of the position and velocity perturbations produced by certain gravity coefficients. We can then assess to what degree/order gravity coefficients can be recovered from the tracking data of the spacecraft. However, this existing method can only be applied to a low eccentricity orbit, and is not valid for an HEO. A new approach to choosing an appropriate semimajor axis is proposed here to analyze an orbital resonance. This approach can be applied to both low eccentricity orbits and HEOs. This small adjustment in the semimajor axis can improve the precision of gravity field coefficients and does not affect other scientific objectives.
基金supported by the National Natural Science Foundation of China(Nos.61673041,61673041,and 61227902)the National High Technology Research and Development Program 863(No.2014AA123401)
文摘Nuclear magnetic resonance gyroscopes （NMRGs） are a kind of rotation-speed sensor that senses the angular velocity by measuring a frequency shift in the Larmor pre- cession of the nuclear spin in a constant magnetic field.
