BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI...BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI)in predicting the histopathologic grade in hepatocellular carcinoma.METHODS We retrospectively analyzed a total of 826 patients from two medical centers(training 459;validation 196;test 171).T2-weighted imaging,diffusion-weighted imaging,and portal venous phases were collected.Tumor segmentations were conducted automatically by 3D U-Net.Based on generative adversarial network,we utilized 3D SR reconstruction to produce SR MRI.Radiomics models were developed and validated by XGBoost and Catboost.The predictive efficiency was demonstrated by calibration curves,decision curve analysis,area under the curve(AUC)and net reclassification index(NRI).RESULTS We extracted 3045 radiomic features from both NR and SR MRI,retaining 29 and 28 features,respectively.For XGBoost models,SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts(0.83 vs 0.79;0.80 vs 0.78),respectively.Consistent trends were seen in CatBoost models:SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI’s 0.81 and 0.76.NRI indicated that the SR MRI models could improve the prediction accuracy by-1.6%to 20.9%compared to the NR MRI models.CONCLUSION Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC.It may be a powerful tool for better stratification management for patients with operable HCC.展开更多
Background The clinicopathological classification was proposed in the St. Gallen Consensus Report 2011. We conducted a retrospective analysis of breast cancer subtypes, tumor-nodal-metastatic （TNM） staging, and hist...Background The clinicopathological classification was proposed in the St. Gallen Consensus Report 2011. We conducted a retrospective analysis of breast cancer subtypes, tumor-nodal-metastatic （TNM） staging, and histopathological grade to investigate the value of these parameters in the treatment strategies of invasive breast cancer.展开更多
The dynamic process of wound healing has various phases,and the knowledge of which is essential for identification of the pathology involved in a chronic intractable wound.Various instruments for the assessment of wou...The dynamic process of wound healing has various phases,and the knowledge of which is essential for identification of the pathology involved in a chronic intractable wound.Various instruments for the assessment of wound healing have been described,primarily for clinical assessment of the wound.However,very few instruments are currently available for histological grading of the wound.The aim of this article is to review all available literature from 1993 to 2014 on the objective histological scoring of the state of wound healing.This review article emphasizes the importance of histological grading of wounds based on the different parameters from each phase of wound healing and the need for an ideal grading system in order to help assessment of wound status.The parameter chosen in an experimental model should be defined by the scientific question,the underlying hypothesis and the pathogenesis of the disease.展开更多
基金Supported by AI+Health Collaborative Innovation Cultivation Project of Beijing City,No.Z221100003522005.
文摘BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI)in predicting the histopathologic grade in hepatocellular carcinoma.METHODS We retrospectively analyzed a total of 826 patients from two medical centers(training 459;validation 196;test 171).T2-weighted imaging,diffusion-weighted imaging,and portal venous phases were collected.Tumor segmentations were conducted automatically by 3D U-Net.Based on generative adversarial network,we utilized 3D SR reconstruction to produce SR MRI.Radiomics models were developed and validated by XGBoost and Catboost.The predictive efficiency was demonstrated by calibration curves,decision curve analysis,area under the curve(AUC)and net reclassification index(NRI).RESULTS We extracted 3045 radiomic features from both NR and SR MRI,retaining 29 and 28 features,respectively.For XGBoost models,SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts(0.83 vs 0.79;0.80 vs 0.78),respectively.Consistent trends were seen in CatBoost models:SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI’s 0.81 and 0.76.NRI indicated that the SR MRI models could improve the prediction accuracy by-1.6%to 20.9%compared to the NR MRI models.CONCLUSION Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC.It may be a powerful tool for better stratification management for patients with operable HCC.
文摘Background The clinicopathological classification was proposed in the St. Gallen Consensus Report 2011. We conducted a retrospective analysis of breast cancer subtypes, tumor-nodal-metastatic （TNM） staging, and histopathological grade to investigate the value of these parameters in the treatment strategies of invasive breast cancer.
文摘The dynamic process of wound healing has various phases,and the knowledge of which is essential for identification of the pathology involved in a chronic intractable wound.Various instruments for the assessment of wound healing have been described,primarily for clinical assessment of the wound.However,very few instruments are currently available for histological grading of the wound.The aim of this article is to review all available literature from 1993 to 2014 on the objective histological scoring of the state of wound healing.This review article emphasizes the importance of histological grading of wounds based on the different parameters from each phase of wound healing and the need for an ideal grading system in order to help assessment of wound status.The parameter chosen in an experimental model should be defined by the scientific question,the underlying hypothesis and the pathogenesis of the disease.
