The hypoxia-inducible factor-1α（HIF-1α） plays an important role in regulating angiogenesis, which is essential for tumor growth and metastasis. Genetic variations of HIFIA （coding HIF-1α） have been shown to inf...The hypoxia-inducible factor-1α（HIF-1α） plays an important role in regulating angiogenesis, which is essential for tumor growth and metastasis. Genetic variations of HIFIA （coding HIF-1α） have been shown to influence an individual＇s susceptibility to many human tumors; however, evidence on associations between HIFIA single-nucleotide polymorphisms （SNPs） and prostate cancer （PCa） risk is conflicting. We genotyped three potentially functional polymorphisms in HIFIA （rs11549465, rs11549467 and rs2057482） using the TaqMan method and assessed their associations with PCa risk in a case-control study of 662 PCa patients and 716 controls in a Chinese Hart population. Compared with rs 11549467 GG genotype, the variant genotypes GA ＋AA had a significantly increased PCa risk （adjusted odds ratio （OR）= 1.70; 95% confidence interval （C1）= 1.06-2.72）, particularly among older patients （0R=2.01; 95%C1 = 1.05-3.86）, smokers （0R=2.06; 95%C1 = 1.07-3.99）, never drinkers （OR=2.16; 95%C1 = 1.20-3.86） and patients without a family history of cancer （OR= 1.71; 95%C1= 1.02-2.89）. Furthermore, patients with rs11549467 variant genotypes were associated with a higher Gleason score （OR=2.14; 95%CI = 1.22-3.75）. No altered PCa risk was associated with the rs 11549465 and rs2057482 polymorphism. However, the combined variant genotypes of rs2057482 and rs 11549467 were associated with increased PCa risk （0R=2.10; 95%C1= 1.23-3.57 among subjects carrying three or more risk alleles）. These results suggest that HIFIA polymorphisms may impact PCa susceptibility and progression in the Chinese Han population.展开更多
This study was designed to evaluate whether the revised 2010 Tumour Node Metastasis （TNM） staging system could lead to a more accurate prediction of the prognosis of renal cell carcinoma （RCC） patients. A total of...This study was designed to evaluate whether the revised 2010 Tumour Node Metastasis （TNM） staging system could lead to a more accurate prediction of the prognosis of renal cell carcinoma （RCC） patients. A total of 1216 patients who had undergone radical nephrectomy or partial nephrectomy for RCC from 2003 to 2011 were enrolled. All of the patients had pathologically confirmed clear cell RCC （ccRCC）. All cases were staged by both the 2002 and 2010 TNM staging systems after pathological review, and survival data were collected. Univariate and multivariate Cox regression models were used to evaluate cancer-specific survival （CSS） and progression-free survival （PFS） after surgery. Continuous variables, such as age and tumour diameter, were calculated as mean values and standard deviations （s.d.） or as median values. Survival was calculated by the Kaplan-Meier method, and the log-rank test assessed differences between groups. Statistically significant differences in CSS and PFS were noted among patients in T3 subgroups using the new 2010 staging system. Therefore, the revised 2010 TNM staging system can lead to a more accurate prediction of the prognosis of ccRCC patients. However, when using the revised 2010 staging system, we found that more than 92% of patients （288/313） with T3 tumours were staged in the T3a subgroup, and their survival data were not significantly different from those of patients with T2b tumours. In addition, T2 subclassification failed to independently predict survival in RCC patients.展开更多
Differential expression of non-coding RNA after traumatic spinal cord injury(TSCI)is closely related to the pathophysiological process.The purposes of this study were to systematically profile and characterize express...Differential expression of non-coding RNA after traumatic spinal cord injury(TSCI)is closely related to the pathophysiological process.The purposes of this study were to systematically profile and characterize expression of circular RNA(circRNA)in the lesion epicenter of spinal tissues after TSCI,and predict the structure and potential function of the regulatory circRNA/miRNA network.Forty-eight C57BL/6 mice were randomly and equally assigned to two groups:one subjected to TSCI at T8–10 with an Allen’s drop impactor,and a second subjected to laminectomy without TSCI.Spinal cord samples were stained with hematoxylin and eosin,sequenced,and validated.RNA-Seq,Gene Ontology analysis,Kyoto Encyclopedia of Genes and Genomes analysis,and network analyses(Targetscan and miRanda)were used to predict and annotate the circRNA/miRNA/mRNA network.Luciferase reporter,quantitative reverse transcription polymerase chain reaction,and western blot assays were used to profile expression and regulation patterns of the network in mouse models of TSCI.Hematoxylin-eosin staining revealed severe damage to the blood-spinal cord barrier after TSCI.Differentially expressed circRNA and miRNA profiles were obtained after TSCI;differentially expressed circRNAs,which were abundant in the cytoplasm,were involved in positive regulation of transcription and protein phosphorylation.miR-135b-5p was the most significantly downregulated miRNA after TSCI;circRNAAbca1 and KLF4 were predicted to be its target circRNA and mRNA,respectively.Subsequently,the circAbca1/miR-135b-5P/KLF4 regulatory axis was predicted and constructed,and its targeted binding was verified.After inhibiting circAbca1,GAP43 expression was upregulated.Differential expression of circRNAs might play an important role after TSCI.circAbca1 plays a neuroinhibitory role by targeted binding of the miR-135b-5P/KLF4 axis.The identified circRNA/miRNA/mRNA network could provide the basis for understanding pathophysiological mechanisms underlying TSCI,as well as guide the formulation of related therapeutic strategies.All animal protocols were approved by the Research Ethics Committee of West China Hospital of China(approval No.2017128)on May 16,2017.展开更多
Background: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primar...Background: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use. Methods: Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome;recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model. Results: Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.134, and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients. Conclusion: Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.展开更多
Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique sys...Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique system,allowing this system to read computed tomography(CT)images correctly and make diagnosis of pancreatic cancer faster.Methods:The establishment of the artificial intelligence(AI)system for pancreatic cancer diagnosis based on sequential contrastenhanced CT images were composed of two processes:training and verification.During training process,our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set.Additionally,we used VGG16,which was pretrained in ImageNet and contained 13 convolutional layers and three fully connected layers,to initialize the feature extraction network.In the verification experiment,we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network(Faster R-CNN)model that had completed training.Totally,1699 images from 100 pancreatic cancer patients were included for clinical verification.Results:A total of 338 patients with pancreatic cancer were included in the study.The clinical characteristics(sex,age,tumor location,differentiation grade,and tumor-node-metastasis stage)between the two training and verification groups were insignificant.The mean average precision was 0.7664,indicating a good training ejffect of the Faster R-CNN.Sequential contrastenhanced CT images of 100 pancreatic cancer patients were used for clinical verification.The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632.It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image,which is much faster than the time required for diagnosis by an imaging specialist.Conclusions:Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.展开更多
Background:Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features.This study ai...Background:Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features.This study aimed to use deep neural netAVorks for computed tomography(CT)diagnosis of perigastric metastatic lymph nodes(PGMLNs)to simulate the recognition of lymph nodes by radiologists,and to acquire more accurate identification results.Methods:A total of 1371 images of suspected lymph node metastasis from enhanced abdominal CT scans were identified and labeled by radiologists and were used with 18,780 original images for faster region-based convolutional neural networks(FR-CNN)deep learning.The identification results of 6000 random CT images from 100 gastric cancer patients by the FR-CNN were compared with results obtained from radiologists in terms of their identification accuracy.Similarly,1004 CT images with metastatic lymph nodes that had been post-operatively confirmed by pathological examination and 11,340 original images were used in the identification and learning processes described above.The same 6000 gastric cancer CT images were used for the verification,according to which the diagnosis results were analyzed.Results:In the initial group,precision-recall curves were generated based on the precision rates,the recall rates of nodule classes of the training set and the validation set;the mean average precision(mAP)value was 0.5019.To verify the results of the initial learning group,the receiver operating characteristic curves was generated,and the corresponding area under the curve(AUC)value was calculated as 0.8995.After the second phase of precise learning,all the indicators were improved,and the mAP and AUC values were 0.7801 and 0.9541,respectively.Conclusion:Through deep learning,FR-CNN achieved high judgment effectiveness and recognition accuracy for CT diagnosis of PGMLNs.展开更多
文摘The hypoxia-inducible factor-1α（HIF-1α） plays an important role in regulating angiogenesis, which is essential for tumor growth and metastasis. Genetic variations of HIFIA （coding HIF-1α） have been shown to influence an individual＇s susceptibility to many human tumors; however, evidence on associations between HIFIA single-nucleotide polymorphisms （SNPs） and prostate cancer （PCa） risk is conflicting. We genotyped three potentially functional polymorphisms in HIFIA （rs11549465, rs11549467 and rs2057482） using the TaqMan method and assessed their associations with PCa risk in a case-control study of 662 PCa patients and 716 controls in a Chinese Hart population. Compared with rs 11549467 GG genotype, the variant genotypes GA ＋AA had a significantly increased PCa risk （adjusted odds ratio （OR）= 1.70; 95% confidence interval （C1）= 1.06-2.72）, particularly among older patients （0R=2.01; 95%C1 = 1.05-3.86）, smokers （0R=2.06; 95%C1 = 1.07-3.99）, never drinkers （OR=2.16; 95%C1 = 1.20-3.86） and patients without a family history of cancer （OR= 1.71; 95%C1= 1.02-2.89）. Furthermore, patients with rs11549467 variant genotypes were associated with a higher Gleason score （OR=2.14; 95%CI = 1.22-3.75）. No altered PCa risk was associated with the rs 11549465 and rs2057482 polymorphism. However, the combined variant genotypes of rs2057482 and rs 11549467 were associated with increased PCa risk （0R=2.10; 95%C1= 1.23-3.57 among subjects carrying three or more risk alleles）. These results suggest that HIFIA polymorphisms may impact PCa susceptibility and progression in the Chinese Han population.
文摘This study was designed to evaluate whether the revised 2010 Tumour Node Metastasis （TNM） staging system could lead to a more accurate prediction of the prognosis of renal cell carcinoma （RCC） patients. A total of 1216 patients who had undergone radical nephrectomy or partial nephrectomy for RCC from 2003 to 2011 were enrolled. All of the patients had pathologically confirmed clear cell RCC （ccRCC）. All cases were staged by both the 2002 and 2010 TNM staging systems after pathological review, and survival data were collected. Univariate and multivariate Cox regression models were used to evaluate cancer-specific survival （CSS） and progression-free survival （PFS） after surgery. Continuous variables, such as age and tumour diameter, were calculated as mean values and standard deviations （s.d.） or as median values. Survival was calculated by the Kaplan-Meier method, and the log-rank test assessed differences between groups. Statistically significant differences in CSS and PFS were noted among patients in T3 subgroups using the new 2010 staging system. Therefore, the revised 2010 TNM staging system can lead to a more accurate prediction of the prognosis of ccRCC patients. However, when using the revised 2010 staging system, we found that more than 92% of patients （288/313） with T3 tumours were staged in the T3a subgroup, and their survival data were not significantly different from those of patients with T2b tumours. In addition, T2 subclassification failed to independently predict survival in RCC patients.
基金This study was supported by the National Natural Science Foundation of China,No.81874002(to LL)the Science and Technology Support Project of Sichuan Province of China,Nos.2018SZ0159(to LL),2018SZ0246(to XLH)+2 种基金the Innovation and Entrepreneurship Project of Sichuan Technology Gallery of China,Nos.2019JDRC0100(to JL),2020JDRC0054(to WZW)the National Clinical Research Center for Geriatrics,West China Hospital,Sichuan University,China,Nos.Y2018B22(to LL),Z20192013(to JL)West China Hospital Postdoctoral Research and Development Fund,No.2019HXBH068(to JL)。
文摘Differential expression of non-coding RNA after traumatic spinal cord injury(TSCI)is closely related to the pathophysiological process.The purposes of this study were to systematically profile and characterize expression of circular RNA(circRNA)in the lesion epicenter of spinal tissues after TSCI,and predict the structure and potential function of the regulatory circRNA/miRNA network.Forty-eight C57BL/6 mice were randomly and equally assigned to two groups:one subjected to TSCI at T8–10 with an Allen’s drop impactor,and a second subjected to laminectomy without TSCI.Spinal cord samples were stained with hematoxylin and eosin,sequenced,and validated.RNA-Seq,Gene Ontology analysis,Kyoto Encyclopedia of Genes and Genomes analysis,and network analyses(Targetscan and miRanda)were used to predict and annotate the circRNA/miRNA/mRNA network.Luciferase reporter,quantitative reverse transcription polymerase chain reaction,and western blot assays were used to profile expression and regulation patterns of the network in mouse models of TSCI.Hematoxylin-eosin staining revealed severe damage to the blood-spinal cord barrier after TSCI.Differentially expressed circRNA and miRNA profiles were obtained after TSCI;differentially expressed circRNAs,which were abundant in the cytoplasm,were involved in positive regulation of transcription and protein phosphorylation.miR-135b-5p was the most significantly downregulated miRNA after TSCI;circRNAAbca1 and KLF4 were predicted to be its target circRNA and mRNA,respectively.Subsequently,the circAbca1/miR-135b-5P/KLF4 regulatory axis was predicted and constructed,and its targeted binding was verified.After inhibiting circAbca1,GAP43 expression was upregulated.Differential expression of circRNAs might play an important role after TSCI.circAbca1 plays a neuroinhibitory role by targeted binding of the miR-135b-5P/KLF4 axis.The identified circRNA/miRNA/mRNA network could provide the basis for understanding pathophysiological mechanisms underlying TSCI,as well as guide the formulation of related therapeutic strategies.All animal protocols were approved by the Research Ethics Committee of West China Hospital of China(approval No.2017128)on May 16,2017.
文摘Background: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use. Methods: Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome;recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model. Results: Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.134, and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients. Conclusion: Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.
基金This work was supported by grants from the National Natural Science Foundation of China(No.81802888)the Key Research and Development Project of Shandong Province(No.2018GSF118206 and No.2018GSF118088).
文摘Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique system,allowing this system to read computed tomography(CT)images correctly and make diagnosis of pancreatic cancer faster.Methods:The establishment of the artificial intelligence(AI)system for pancreatic cancer diagnosis based on sequential contrastenhanced CT images were composed of two processes:training and verification.During training process,our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set.Additionally,we used VGG16,which was pretrained in ImageNet and contained 13 convolutional layers and three fully connected layers,to initialize the feature extraction network.In the verification experiment,we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network(Faster R-CNN)model that had completed training.Totally,1699 images from 100 pancreatic cancer patients were included for clinical verification.Results:A total of 338 patients with pancreatic cancer were included in the study.The clinical characteristics(sex,age,tumor location,differentiation grade,and tumor-node-metastasis stage)between the two training and verification groups were insignificant.The mean average precision was 0.7664,indicating a good training ejffect of the Faster R-CNN.Sequential contrastenhanced CT images of 100 pancreatic cancer patients were used for clinical verification.The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632.It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image,which is much faster than the time required for diagnosis by an imaging specialist.Conclusions:Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.
文摘Background:Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features.This study aimed to use deep neural netAVorks for computed tomography(CT)diagnosis of perigastric metastatic lymph nodes(PGMLNs)to simulate the recognition of lymph nodes by radiologists,and to acquire more accurate identification results.Methods:A total of 1371 images of suspected lymph node metastasis from enhanced abdominal CT scans were identified and labeled by radiologists and were used with 18,780 original images for faster region-based convolutional neural networks(FR-CNN)deep learning.The identification results of 6000 random CT images from 100 gastric cancer patients by the FR-CNN were compared with results obtained from radiologists in terms of their identification accuracy.Similarly,1004 CT images with metastatic lymph nodes that had been post-operatively confirmed by pathological examination and 11,340 original images were used in the identification and learning processes described above.The same 6000 gastric cancer CT images were used for the verification,according to which the diagnosis results were analyzed.Results:In the initial group,precision-recall curves were generated based on the precision rates,the recall rates of nodule classes of the training set and the validation set;the mean average precision(mAP)value was 0.5019.To verify the results of the initial learning group,the receiver operating characteristic curves was generated,and the corresponding area under the curve(AUC)value was calculated as 0.8995.After the second phase of precise learning,all the indicators were improved,and the mAP and AUC values were 0.7801 and 0.9541,respectively.Conclusion:Through deep learning,FR-CNN achieved high judgment effectiveness and recognition accuracy for CT diagnosis of PGMLNs.
