Camera calibration is the key technique in a C-arm based orthopaedic surgical navigation system. The extraction of marker location information is a necessary step in the calibration process. Ideal marker images should...Camera calibration is the key technique in a C-arm based orthopaedic surgical navigation system. The extraction of marker location information is a necessary step in the calibration process. Ideal marker images should possess uniform background and contain marker shadow only, but in fact marker images always possess nonuniform background and are contaminated by noise and unwanted anatomic information, making the extraction very difficult. A target-orientated marker shadow extraction method was proposed. With this method a proper threshold for marker image binarization can be determined.展开更多
In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest...In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.展开更多
Two landmark studies demonstrate synergistic approaches to gastrointestinal cancer management.Lin et al identified activin A receptor type 1C polymor-phisms(rs4556933/rs77886248)as esophageal squamous cell carcinoma r...Two landmark studies demonstrate synergistic approaches to gastrointestinal cancer management.Lin et al identified activin A receptor type 1C polymor-phisms(rs4556933/rs77886248)as esophageal squamous cell carcinoma risk modifiers in Chinese Han populations through a case-control study(1264 patients/1361 controls),revealing transforming growth factor-beta pathway-mediated susceptibility in older male smokers(P<0.001).Concurrently,Luo et al established imaging-based differentiation of pancreatic cancer subtypes(pancreatic ductal adenocarcinoma vs neuroendocrine tumors)via retrospective analysis of 500 cases(area under the curve=0.89),enabling earlier intervention.These findings underscore the transformative potential of combining genetic risk stratification with advanced imaging to guide precision screening and therapeutic strategies,addressing critical gaps in esophageal and pancreatic cancer outcomes.展开更多
Brain iron deposition has been proposed to play an important role in the pathophysiology of Alzheimer disease（AD）.The aim of this study was to investigate the correlation of brain iron accumulation with the severity...Brain iron deposition has been proposed to play an important role in the pathophysiology of Alzheimer disease（AD）.The aim of this study was to investigate the correlation of brain iron accumulation with the severity of cognitive impairment in patients with AD by using quantitative MR relaxation rate R2＇ measurements.Fifteen patients with AD,15 age-and sex-matched healthy controls,and 30 healthy volunteers underwent 1.5T MR multi-echo T2 mapping and T2＊ mapping for the measurement of transverse relaxation rate R2＇（R2＇=R2＊-R2）.We statistically analyzed the R2＇ and iron concentrations of bilateral hippocampus（HP）,parietal cortex（PC）,frontal white matter（FWM）,putamen（PU）,caudate nucleus（CN）,thalamus（TH）,red nucleus（RN）,substantia nigra（SN）,and dentate nucleus（DN） of the cerebellum for the correlation with the severity of dementia.Two-tailed t-test,Student-Newman-Keuls test（ANOVA） and linear correlation test were used for statistical analysis.In 30 healthy volunteers,the R2＇ values of bilateral SN,RN,PU,CN,globus pallidus（GP）,TH,and FWM were measured.The correlation with the postmortem iron concentration in normal adults was analyzed in order to establish a formula on the relationship between regional R2＇ and brain iron concentration.The iron concentration of regions of interest（ROI） in AD patients and controls was calculated by this formula and its correlation with the severity of AD was analyzed.Regional R2＇ was positively correlated with regional brain iron concentration in normal adults（r=0.977,P0.01）.Iron concentrations in bilateral HP,PC,PU,CN,and DN of patients with AD were significantly higher than those of the controls（P0.05）;Moreover,the brain iron concentrations,especially in parietal cortex and hippocampus at the early stage of AD,were positively correlated with the severity of patients＇ cognitive impairment（P0.05）.The higher the R2＇ and iron concentrations were,the more severe the cognitive impairment was.Regional R2＇ and iron concentration in parietal cortex and hippocampus were positively correlated with the severity of AD patients＇ cognitive impairment,indicating that it may be used as a biomarker to evaluate the progression of AD.展开更多
Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes ...Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes are valuable for risk stratification and treatment strategies.This study aimed to develop and validate an explainable model for predicting long-term recurrence and all-cause death in patients with ICH,using clinical and imaging markers of cerebral small vascular diseases from MRI.Method We retrospectively analysed data from a prospectively collected large-scale cohort of patients with acute ICH admitted to the Neurology Department of The Second Affiliated Hospital of Zhejiang University between November 2016 and April 2023.After comprehensive variable selection using least absolute shrinkage and selection operator and stepwise Cox regression,we constructed Cox proportional hazards models to predict recurrence and all-cause death.Model performance was evaluated using the concordance index,integrated Brier score and time-dependent area under the curve.Global and local interpretability were assessed using variable importance calculated as SurvSHAP(t)and SurvLIME methods for the entire training set and individual patients,respectively.Results A total of 842 eligible patients were included.Over a median follow-up of 36 months(IQR:12-51),86 patients(9.1%)died,and 62 patients(6.6%)experienced recurrence of ICH.The concordance indexes for the all-cause death and recurrence models were 0.841(95%CI 0.767 to 0.913)and 0.759(95%CI 0.651 to 0.867),respectively,with integrated Brier scores of 0.079 and 0.063.The interpretability maps highlighted age,aetiology of ICH and low haemoglobin as key predictors of long-term death,while cortical superficial siderosis and previous haemorrhage were crucial for predicting recurrence.Conclusions This model demonstrates high predictive accuracy and emphasises the crucial factors in predicting long-term outcomes of patients with sICH.展开更多
基金Project of Science and Technology Committee of Shanghai Municipality (No.2528(3))
文摘Camera calibration is the key technique in a C-arm based orthopaedic surgical navigation system. The extraction of marker location information is a necessary step in the calibration process. Ideal marker images should possess uniform background and contain marker shadow only, but in fact marker images always possess nonuniform background and are contaminated by noise and unwanted anatomic information, making the extraction very difficult. A target-orientated marker shadow extraction method was proposed. With this method a proper threshold for marker image binarization can be determined.
基金The studies mentioned in this paper were supported in part by Grants R01 CA160205 and R01 CA197150 from the National Cancer Institute,National Institutes of Health,USAGrant HR15-016 from Oklahoma Center for the Advancement of Science and Technology,USA.
文摘In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.
文摘Two landmark studies demonstrate synergistic approaches to gastrointestinal cancer management.Lin et al identified activin A receptor type 1C polymor-phisms(rs4556933/rs77886248)as esophageal squamous cell carcinoma risk modifiers in Chinese Han populations through a case-control study(1264 patients/1361 controls),revealing transforming growth factor-beta pathway-mediated susceptibility in older male smokers(P<0.001).Concurrently,Luo et al established imaging-based differentiation of pancreatic cancer subtypes(pancreatic ductal adenocarcinoma vs neuroendocrine tumors)via retrospective analysis of 500 cases(area under the curve=0.89),enabling earlier intervention.These findings underscore the transformative potential of combining genetic risk stratification with advanced imaging to guide precision screening and therapeutic strategies,addressing critical gaps in esophageal and pancreatic cancer outcomes.
基金supported by grants from the National Natural Science Foundation of China (No. 30870702 and No.30570531)863 Project of China (No. 2006AA02Z4A1)
文摘Brain iron deposition has been proposed to play an important role in the pathophysiology of Alzheimer disease（AD）.The aim of this study was to investigate the correlation of brain iron accumulation with the severity of cognitive impairment in patients with AD by using quantitative MR relaxation rate R2＇ measurements.Fifteen patients with AD,15 age-and sex-matched healthy controls,and 30 healthy volunteers underwent 1.5T MR multi-echo T2 mapping and T2＊ mapping for the measurement of transverse relaxation rate R2＇（R2＇=R2＊-R2）.We statistically analyzed the R2＇ and iron concentrations of bilateral hippocampus（HP）,parietal cortex（PC）,frontal white matter（FWM）,putamen（PU）,caudate nucleus（CN）,thalamus（TH）,red nucleus（RN）,substantia nigra（SN）,and dentate nucleus（DN） of the cerebellum for the correlation with the severity of dementia.Two-tailed t-test,Student-Newman-Keuls test（ANOVA） and linear correlation test were used for statistical analysis.In 30 healthy volunteers,the R2＇ values of bilateral SN,RN,PU,CN,globus pallidus（GP）,TH,and FWM were measured.The correlation with the postmortem iron concentration in normal adults was analyzed in order to establish a formula on the relationship between regional R2＇ and brain iron concentration.The iron concentration of regions of interest（ROI） in AD patients and controls was calculated by this formula and its correlation with the severity of AD was analyzed.Regional R2＇ was positively correlated with regional brain iron concentration in normal adults（r=0.977,P0.01）.Iron concentrations in bilateral HP,PC,PU,CN,and DN of patients with AD were significantly higher than those of the controls（P0.05）;Moreover,the brain iron concentrations,especially in parietal cortex and hippocampus at the early stage of AD,were positively correlated with the severity of patients＇ cognitive impairment（P0.05）.The higher the R2＇ and iron concentrations were,the more severe the cognitive impairment was.Regional R2＇ and iron concentration in parietal cortex and hippocampus were positively correlated with the severity of AD patients＇ cognitive impairment,indicating that it may be used as a biomarker to evaluate the progression of AD.
基金supported by the Zhejiang Provincial Medical and Health Science and Technology Project(2022KY174)the‘Pioneer’R&D Program of Zhejiang(2024C03006).
文摘Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes are valuable for risk stratification and treatment strategies.This study aimed to develop and validate an explainable model for predicting long-term recurrence and all-cause death in patients with ICH,using clinical and imaging markers of cerebral small vascular diseases from MRI.Method We retrospectively analysed data from a prospectively collected large-scale cohort of patients with acute ICH admitted to the Neurology Department of The Second Affiliated Hospital of Zhejiang University between November 2016 and April 2023.After comprehensive variable selection using least absolute shrinkage and selection operator and stepwise Cox regression,we constructed Cox proportional hazards models to predict recurrence and all-cause death.Model performance was evaluated using the concordance index,integrated Brier score and time-dependent area under the curve.Global and local interpretability were assessed using variable importance calculated as SurvSHAP(t)and SurvLIME methods for the entire training set and individual patients,respectively.Results A total of 842 eligible patients were included.Over a median follow-up of 36 months(IQR:12-51),86 patients(9.1%)died,and 62 patients(6.6%)experienced recurrence of ICH.The concordance indexes for the all-cause death and recurrence models were 0.841(95%CI 0.767 to 0.913)and 0.759(95%CI 0.651 to 0.867),respectively,with integrated Brier scores of 0.079 and 0.063.The interpretability maps highlighted age,aetiology of ICH and low haemoglobin as key predictors of long-term death,while cortical superficial siderosis and previous haemorrhage were crucial for predicting recurrence.Conclusions This model demonstrates high predictive accuracy and emphasises the crucial factors in predicting long-term outcomes of patients with sICH.
