BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identif...BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.METHODS A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography(CT)within 30 d prior to surgery were included in the study.Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system.Radiomics features were extracted from the region of interest(ROI)for each patient using Analysis Kit software.The most important and predictive radiomics features were selected using Mann-Whitney U test,univar-iate logistic regression analysis,and minimum redundancy maximum relevance(MRMR)method.Random forest(RF)method was used to construct the radiomics model,and 10-times leave group out cross-validation(LGOCV)method was used to validate the robustness and reproducibility of the model.RESULTS A total of 792 radiomics features(396 from late arterial phase and 396 from portal venous phase)were extracted from the ROI for each patient using Analysis Kit software.Nine most important and predictive features were selected using Mann-Whitney U test,univariate logistic regression analysis,and MRMR method.RF method was used to construct the radiomics model with the nine most predictive radiomics features,which showed a high discriminative ability with 97.7%accuracy,97.6%sensitivity,97.8%specificity,98.4%positive predictive value,and 96.8%negative predictive value.The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.CONCLUSION The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.展开更多
Chronic pancreatitis(CP)is a fibroinflammatory disease characterized by irreversible destruction of pancreatic tissue.With the development of the disease,it may lead to exocrine and/or endocrine insufficiency.CP is on...Chronic pancreatitis(CP)is a fibroinflammatory disease characterized by irreversible destruction of pancreatic tissue.With the development of the disease,it may lead to exocrine and/or endocrine insufficiency.CP is one of the common diseases that cause abdominal pain,which will not get permanent spontaneous relief as the disease evolves.The American College of Gastroenterology clinical guidelines recommend computed tomography or magnetic resonance imaging as the first-line examination for the diagnosis of CP.CP common imaging findings include pancreatic atrophy,irregular dilatation of the pancreatic duct,calcification of pancreatic parenchyma,pancreatic duct stones,etc.In clinical practice,whether any correlations between CP-induced abdominal pain patterns(no pain/constant/intermittent pain)and corresponding imaging findings present are not well known.Therefore,this review aims to comprehensively sort out and analyze the relevant information by collecting lots of literature on this field,so as to construct a cross-bridge between the clinical manifestations and imaging manifestations of CP patients.Also,it provides an imaging basis and foundation for the classification and diagnosis of abdominal pain types in clinical CP patients.展开更多
BACKGROUND Solid pseudopapillary neoplasms of the pancreas(SPN)share similar imaging findings with pancreatic ductal adenocarcinoma with cystic changes(PDAC with cystic changes),which may result in unnecessary surgery...BACKGROUND Solid pseudopapillary neoplasms of the pancreas(SPN)share similar imaging findings with pancreatic ductal adenocarcinoma with cystic changes(PDAC with cystic changes),which may result in unnecessary surgery.AIM To investigate the value of computed tomography(CT)in differentiation of SPN from PDAC with cystic changes.METHODS This study retrospectively analyzed the clinical and imaging findings of 32 patients diagnosed with SPN and 14 patients diagnosed with PDAC exhibiting cystic changes,confirmed through pathological diagnosis.Quantitative and qualitative analysis was performed,including assessment of age,sex,tumor size,shape,margin,density,enhancement pattern,CT values of tumors,CT contrast enhancement ratios,“floating cloud sign,”calcification,main pancreatic duct dilatation,pancreatic atrophy,and peripancreatic invasion or distal metastasis.Multivariate logistic regression analysis was used to identify relevant features to differentiate between SPN and PDAC with cystic changes,and receiver operating characteristic curves were obtained to evaluate the diagnostic performance of each variable and their combination.RESULTS When compared to PDAC with cystic changes,SPN had a lower age(32 years vs 64 years,P<0.05)and a slightly larger size(5.41 cm vs 3.90 cm,P<0.05).SPN had a higher frequency of“floating cloud sign”and peripancreatic invasion or distal metastasis than PDAC with cystic changes(both P<0.05).No significant difference was found with respect to sex,tumor location,shape,margin,density,main pancreatic duct dilatation,calcification,pancreatic atrophy,enhancement pattern,CT values of tumors,or CT contrast enhancement ratios between the two groups(all P>0.05).The area under the receiver operating characteristic curve of the combination was 0.833(95%confidence interval:0.708-0.957)with 78.6%sensitivity,81.3%specificity,and 80.4%accuracy in differentiation of SPN from PDAC with cystic changes.CONCLUSION A larger tumor size,“floating cloud sign,”and peripancreatic invasion or distal metastasis are useful CT imaging features that are more common in SPN and may help discriminate SPN from PDAC with cystic changes.展开更多
Micro-computed tomography (MCT) encompasses two primary scanning options: ex-vivo and in-vivo imaging. Ex-vivo scanning involves the examination of extracted teeth or dental specimens, allowing for detailed analyses o...Micro-computed tomography (MCT) encompasses two primary scanning options: ex-vivo and in-vivo imaging. Ex-vivo scanning involves the examination of extracted teeth or dental specimens, allowing for detailed analyses of the microarchitecture of mineralized tissue. By analyzing the microarchitecture of dental tissues, MCT can provide valuable information about bone density, porosity, and microstructural changes, contributing to a better understanding of disease progression and treatment outcomes. Moreover, MCT facilitates the quantification of dental parameters, such as bone volume, trabecular thickness, and connectivity density, which are crucial for evaluating the efficacy of dental interventions. This present study aims to comprehensively review and explore the applications of MCT in dentistry and highlight its potential in advancing research and clinical practice. The results depicted that the quantitative approach of MCT enhances the precision and reliability of dental research. Researchers and clinicians can make evidence-based decisions regarding treatment strategies and patient management, relying on quantifiable data provided by MCT. The applications of MCT in dentistry extend beyond research, with potential clinical implications in fields such as dental implantology and endodontics. MCT is expected to play an increasingly significant role in enhancing our understanding of dental pathologies, improving treatment outcomes, and ultimately, benefiting patient care in the field of dentistry.展开更多
基金Supported by the National Natural Science foundation of China,No.82202135,82371919,82372017,and 82171925China Postdoctoral Science Foundation,No.2023M741808+3 种基金Young Elite Scientists Sponsorship Program by Jiangsu Association for Science and Technology,No.JSTJ-2023-WJ027Foundation of Excellent Young Doctor of Jiangsu Province Hospital of Chinese Medicine,No.2023QB0112Nanjing Postdoctoral Science Foundation,Natural Science Foundation of Nanjing University of Chinese Medicine,No.XZR2023036 and XZR2021050Medical Imaging Artificial Intelligence Special Research Fund Project,Nanjing Medical Association Radiology Branch,Project of National Clinical Research Base of Traditional Chinese Medicine in Jiangsu Province,China,No.JD2023SZ16.
文摘BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.METHODS A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography(CT)within 30 d prior to surgery were included in the study.Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system.Radiomics features were extracted from the region of interest(ROI)for each patient using Analysis Kit software.The most important and predictive radiomics features were selected using Mann-Whitney U test,univar-iate logistic regression analysis,and minimum redundancy maximum relevance(MRMR)method.Random forest(RF)method was used to construct the radiomics model,and 10-times leave group out cross-validation(LGOCV)method was used to validate the robustness and reproducibility of the model.RESULTS A total of 792 radiomics features(396 from late arterial phase and 396 from portal venous phase)were extracted from the ROI for each patient using Analysis Kit software.Nine most important and predictive features were selected using Mann-Whitney U test,univariate logistic regression analysis,and MRMR method.RF method was used to construct the radiomics model with the nine most predictive radiomics features,which showed a high discriminative ability with 97.7%accuracy,97.6%sensitivity,97.8%specificity,98.4%positive predictive value,and 96.8%negative predictive value.The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.CONCLUSION The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.
文摘Chronic pancreatitis(CP)is a fibroinflammatory disease characterized by irreversible destruction of pancreatic tissue.With the development of the disease,it may lead to exocrine and/or endocrine insufficiency.CP is one of the common diseases that cause abdominal pain,which will not get permanent spontaneous relief as the disease evolves.The American College of Gastroenterology clinical guidelines recommend computed tomography or magnetic resonance imaging as the first-line examination for the diagnosis of CP.CP common imaging findings include pancreatic atrophy,irregular dilatation of the pancreatic duct,calcification of pancreatic parenchyma,pancreatic duct stones,etc.In clinical practice,whether any correlations between CP-induced abdominal pain patterns(no pain/constant/intermittent pain)and corresponding imaging findings present are not well known.Therefore,this review aims to comprehensively sort out and analyze the relevant information by collecting lots of literature on this field,so as to construct a cross-bridge between the clinical manifestations and imaging manifestations of CP patients.Also,it provides an imaging basis and foundation for the classification and diagnosis of abdominal pain types in clinical CP patients.
基金Supported by the National Natural Science foundation of China,No.82202135,No.82371919,No.82372017 and No.82171925Project funded by China Postdoctoral Science Foundation,No.2023M741808+4 种基金Jiangsu Provincial Key research and development program,No.BE2023789Young Elite Scientists Sponsorship Program by Jiangsu Association for Science and Technology,No.JSTJ-2023-WJ027Foundation of Excellent Young Doctor of Jiangsu Province Hospital of Chinese Medicine,No.2023QB0112Project funded by Nanjing Postdoctoral Science Foundation,Natural Science Foundation of Nanjing University of Chinese Medicine,No.XZR2023036,No.XZR2021003 and No.XZR2021050Medical Imaging Artificial Intelligence Special Research Fund Project,Nanjing Medical Association Radiology Branch,Project of National Clinical Research Base of Traditional Chinese Medicine in Jiangsu Province,China,No.JD2023SZ16.
文摘BACKGROUND Solid pseudopapillary neoplasms of the pancreas(SPN)share similar imaging findings with pancreatic ductal adenocarcinoma with cystic changes(PDAC with cystic changes),which may result in unnecessary surgery.AIM To investigate the value of computed tomography(CT)in differentiation of SPN from PDAC with cystic changes.METHODS This study retrospectively analyzed the clinical and imaging findings of 32 patients diagnosed with SPN and 14 patients diagnosed with PDAC exhibiting cystic changes,confirmed through pathological diagnosis.Quantitative and qualitative analysis was performed,including assessment of age,sex,tumor size,shape,margin,density,enhancement pattern,CT values of tumors,CT contrast enhancement ratios,“floating cloud sign,”calcification,main pancreatic duct dilatation,pancreatic atrophy,and peripancreatic invasion or distal metastasis.Multivariate logistic regression analysis was used to identify relevant features to differentiate between SPN and PDAC with cystic changes,and receiver operating characteristic curves were obtained to evaluate the diagnostic performance of each variable and their combination.RESULTS When compared to PDAC with cystic changes,SPN had a lower age(32 years vs 64 years,P<0.05)and a slightly larger size(5.41 cm vs 3.90 cm,P<0.05).SPN had a higher frequency of“floating cloud sign”and peripancreatic invasion or distal metastasis than PDAC with cystic changes(both P<0.05).No significant difference was found with respect to sex,tumor location,shape,margin,density,main pancreatic duct dilatation,calcification,pancreatic atrophy,enhancement pattern,CT values of tumors,or CT contrast enhancement ratios between the two groups(all P>0.05).The area under the receiver operating characteristic curve of the combination was 0.833(95%confidence interval:0.708-0.957)with 78.6%sensitivity,81.3%specificity,and 80.4%accuracy in differentiation of SPN from PDAC with cystic changes.CONCLUSION A larger tumor size,“floating cloud sign,”and peripancreatic invasion or distal metastasis are useful CT imaging features that are more common in SPN and may help discriminate SPN from PDAC with cystic changes.
文摘Micro-computed tomography (MCT) encompasses two primary scanning options: ex-vivo and in-vivo imaging. Ex-vivo scanning involves the examination of extracted teeth or dental specimens, allowing for detailed analyses of the microarchitecture of mineralized tissue. By analyzing the microarchitecture of dental tissues, MCT can provide valuable information about bone density, porosity, and microstructural changes, contributing to a better understanding of disease progression and treatment outcomes. Moreover, MCT facilitates the quantification of dental parameters, such as bone volume, trabecular thickness, and connectivity density, which are crucial for evaluating the efficacy of dental interventions. This present study aims to comprehensively review and explore the applications of MCT in dentistry and highlight its potential in advancing research and clinical practice. The results depicted that the quantitative approach of MCT enhances the precision and reliability of dental research. Researchers and clinicians can make evidence-based decisions regarding treatment strategies and patient management, relying on quantifiable data provided by MCT. The applications of MCT in dentistry extend beyond research, with potential clinical implications in fields such as dental implantology and endodontics. MCT is expected to play an increasingly significant role in enhancing our understanding of dental pathologies, improving treatment outcomes, and ultimately, benefiting patient care in the field of dentistry.
