Objective:To evaluate the performance of deep learning reconstruction (DLR) in visualizing lenticulostriate arteries (LSAs) on cerebral CT angiography (CTA).Methods:This cross-sectional study retrospectively analyzed cerebral CTA from 38 patients who underwent cerebral CTA at the Fourth Affiliated Hospital of Harbin Medical University between January and December 2023. Images were reconstructed using filtered back projection (FBP), three-dimensional adaptive iterative dose reduction (AIDR), and DLR-advanced inteuigent clear-IQ engine(AiCE) algorithms (FBP group, AIDR group, DLR-AiCE group). On axial images, the mean CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus. Subjective evaluations were performed for overall vascular visualization and LSAs delineation. Comparisons of subjective and objective evaluation indexes among the 3 groups were performed using the complex measurement ANOVA, Friedman test, or χ2 test. Results:The CT, SD, SNR and CNR values at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus demonstrated statistically significance among DLR-AiCE group, AIDR group and FBP group ( P<0.001), in which, except for the difference between the FBP group and the AIDR group in the CT value of the head of the caudate nucleus and the CT value of the cerebrospinal fluid of the lateral ventricle which was not statistically significant ( P>0.05), the remaining pairwise comparisons between the groups for each site measurements were statistically significant ( P<0.001). The difference in the overall comparison of the subjective scores of the overall vessels and LSAs in the images of the DLR-AiCE group, the AIDR group, and the FBP group was statistically significant ( P<0.001), and the two-by-two comparisons showed a statistically significant difference ( P<0.001) except for the difference in the subjective scores of LSAs between the FBP group and the AIDR group. Conclusion:The DLR-AiCE algorithm significantly reduces image noise and improves image quality, enabling superior visualization of LSAs, thereby enhancing diagnostic confidence.

Objective To investigate the value of diffusion kurtosis imaging(DKI)in the differential diagnosis between benign and malignant sinonasal lesions.Methods The clinical and imaging data of 49 patients(20 benign and 29 malignant lesions)with sinonasal lesions confirmed by surgery and pathology were analyzed retrospectively.Conventional MRI and DKI were scanned for all patients.DKI quantitative parameters,including D-value and K-value,were measured by using FireVoxel software.The t-test was used to compare the differences of D-value and K-value between benign and malignant lesions.Diagnostic performances of these parameters and their combination in differentiating benign from malignant sinonasal lesions were assessed by using receiver operating characteristic(ROC)curve.Results Compared with benign group,malignant group showed significantly lower D-value(1.354±0.329 vs 1.924±0.595,P＜0.001)and higher K-value(0.856±0.190 vs 0.630±0.165,P＜0.001).ROC curve analysis showed that the area under the curve(AUC)for D-value in differentiating benign from malignant lesions was 0.820,with a cut-off value of＜1.775,sensitivity of 93.10％,and specificity of 55.00％.The AUC for K-value was 0.831,with a cut-off value of＞0.716,sensitivity of 79.31％,and specificity of 80.00％.The model combining D-value and K-value achieved an AUC of 0.859,sensitivity of 93.10％,and specificity of 70.00％.Conclusion The D-value and K-value obtained based on DKI can provide significant assistance in differentiating benign from malignant sinonasal lesions.

Objective To explore the value of integrating CT image features and quantitative dual-energy computed tomography(DECT)parameters in predicting cervical lymph nodes metastasis from laryngeal and hypopharyngeal squamous cell carcinoma(LHSCC).Methods The clinical and imaging data of 99 patients with LHSCC confirmed by pathology were retrospectively analyzed.All patients were divided into metastatic group(41 cases)and non-metastatic group(58 cases).The CT image features,including location,size and depth,were analyzed,respectively.The quantitative DECT parameters in the arterial and venous phases including iodine concentration(IC)and normalized iodine concentration(NIC)were measured.The rank sum test or independent-samples t-test were used to compare the difference of CT image features and quantitative DECT parameters between the two groups.The multivariate logistic regression analysis was used to build the models based on CT image features(image feature model)and combination of CT image features and quantitative DECT parameters(combined model).The receiver operating characteristic(ROC)curve was performed to analyze and compare the difference of predictive efficiency between the two groups.Results There were significant differences in tumor location between the non-metastatic group and the metastatic group(χ2=21.736,P<0.001).Size(33.20 mm vs 24.95 mm,P<0.001),depth(21.10 mm vs 13.15 mm,P<0.001)and NIC in the arterial phase(0.18 vs 0.14,P<0.001)in the metastatic group were significantly higher than those in the non-metastatic group.The area under the curve(AUC),sensitivity,specificity,positive predictive value,negative predictive value,accuracy of the combined model were 0.851,75.6%,82.8%,58.5%,87.9%and 75.8%for predicting cervical lymph nodes metastasis.The AUC,sensitivity,specificity,positive predictive value,negative predictive value,accuracy of the image feature model were 0.792,95.1%,56.9%,53.7%,81.0%and 69.7%,respectively.The prediction performance of the combined model was better than that of the image feature model(Z=-2.028,P=0.043).Conclusion Integrating CT image features and quantitative DECT parameters has important value for predicting cervical lymph nodes metastasis from LHSCC.

Objective To investigate the value of diffusion kurtosis imaging(DKI)in the differential diagnosis between benign and malignant sinonasal lesions.Methods The clinical and imaging data of 49 patients(20 benign and 29 malignant lesions)with sinonasal lesions confirmed by surgery and pathology were analyzed retrospectively.Conventional MRI and DKI were scanned for all patients.DKI quantitative parameters,including D-value and K-value,were measured by using FireVoxel software.The t-test was used to compare the differences of D-value and K-value between benign and malignant lesions.Diagnostic performances of these parameters and their combination in differentiating benign from malignant sinonasal lesions were assessed by using receiver operating characteristic(ROC)curve.Results Compared with benign group,malignant group showed significantly lower D-value(1.354±0.329 vs 1.924±0.595,P＜0.001)and higher K-value(0.856±0.190 vs 0.630±0.165,P＜0.001).ROC curve analysis showed that the area under the curve(AUC)for D-value in differentiating benign from malignant lesions was 0.820,with a cut-off value of＜1.775,sensitivity of 93.10％,and specificity of 55.00％.The AUC for K-value was 0.831,with a cut-off value of＞0.716,sensitivity of 79.31％,and specificity of 80.00％.The model combining D-value and K-value achieved an AUC of 0.859,sensitivity of 93.10％,and specificity of 70.00％.Conclusion The D-value and K-value obtained based on DKI can provide significant assistance in differentiating benign from malignant sinonasal lesions.

Objective:To evaluate the performance of deep learning reconstruction (DLR) in visualizing lenticulostriate arteries (LSAs) on cerebral CT angiography (CTA).Methods:This cross-sectional study retrospectively analyzed cerebral CTA from 38 patients who underwent cerebral CTA at the Fourth Affiliated Hospital of Harbin Medical University between January and December 2023. Images were reconstructed using filtered back projection (FBP), three-dimensional adaptive iterative dose reduction (AIDR), and DLR-advanced inteuigent clear-IQ engine(AiCE) algorithms (FBP group, AIDR group, DLR-AiCE group). On axial images, the mean CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus. Subjective evaluations were performed for overall vascular visualization and LSAs delineation. Comparisons of subjective and objective evaluation indexes among the 3 groups were performed using the complex measurement ANOVA, Friedman test, or χ2 test. Results:The CT, SD, SNR and CNR values at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus demonstrated statistically significance among DLR-AiCE group, AIDR group and FBP group ( P<0.001), in which, except for the difference between the FBP group and the AIDR group in the CT value of the head of the caudate nucleus and the CT value of the cerebrospinal fluid of the lateral ventricle which was not statistically significant ( P>0.05), the remaining pairwise comparisons between the groups for each site measurements were statistically significant ( P<0.001). The difference in the overall comparison of the subjective scores of the overall vessels and LSAs in the images of the DLR-AiCE group, the AIDR group, and the FBP group was statistically significant ( P<0.001), and the two-by-two comparisons showed a statistically significant difference ( P<0.001) except for the difference in the subjective scores of LSAs between the FBP group and the AIDR group. Conclusion:The DLR-AiCE algorithm significantly reduces image noise and improves image quality, enabling superior visualization of LSAs, thereby enhancing diagnostic confidence.

Objective To explore the value of integrating CT image features and quantitative dual-energy computed tomography(DECT)parameters in predicting cervical lymph nodes metastasis from laryngeal and hypopharyngeal squamous cell carcinoma(LHSCC).Methods The clinical and imaging data of 99 patients with LHSCC confirmed by pathology were retrospectively analyzed.All patients were divided into metastatic group(41 cases)and non-metastatic group(58 cases).The CT image features,including location,size and depth,were analyzed,respectively.The quantitative DECT parameters in the arterial and venous phases including iodine concentration(IC)and normalized iodine concentration(NIC)were measured.The rank sum test or independent-samples t-test were used to compare the difference of CT image features and quantitative DECT parameters between the two groups.The multivariate logistic regression analysis was used to build the models based on CT image features(image feature model)and combination of CT image features and quantitative DECT parameters(combined model).The receiver operating characteristic(ROC)curve was performed to analyze and compare the difference of predictive efficiency between the two groups.Results There were significant differences in tumor location between the non-metastatic group and the metastatic group(χ2=21.736,P<0.001).Size(33.20 mm vs 24.95 mm,P<0.001),depth(21.10 mm vs 13.15 mm,P<0.001)and NIC in the arterial phase(0.18 vs 0.14,P<0.001)in the metastatic group were significantly higher than those in the non-metastatic group.The area under the curve(AUC),sensitivity,specificity,positive predictive value,negative predictive value,accuracy of the combined model were 0.851,75.6%,82.8%,58.5%,87.9%and 75.8%for predicting cervical lymph nodes metastasis.The AUC,sensitivity,specificity,positive predictive value,negative predictive value,accuracy of the image feature model were 0.792,95.1%,56.9%,53.7%,81.0%and 69.7%,respectively.The prediction performance of the combined model was better than that of the image feature model(Z=-2.028,P=0.043).Conclusion Integrating CT image features and quantitative DECT parameters has important value for predicting cervical lymph nodes metastasis from LHSCC.

Objective:To investigate the value of multi-parametric analysis based on dual-layer detector spectral CT (DLCT) in predicting the initial recurrence risk for papillary thyroid carcinoma (PTC).Methods:From November 2021 to October 2022, 102 PTC patients confirmed by pathology were retrospectively collected at the First Affiliated Hospital of Nanjing Medical University in this cross-sectional study. There were 25 males and 77 females, with an age of (42±13) years old. The initial recurrence risk assessment for PTC patients was categorized into a low-risk group (75 cases) and an intermediate-high-risk group (27 cases). Clinical data, including age, gender, body mass index, history of nodular goiter, history of Hashimoto thyroiditis, and preoperative thyroid function, were collected. Tumor morphological features, including size, location, shape, aspect ratio, the degree of thyroid capsule contact, calcification, and cystic change, were evaluated. Quantitative DLCT parameters, including iodine concentration (IC), standardized iodine concentration (NIC), effective atomic number (Z eff), standardized effective atomic number (NZ eff), electronic density (ED), CT values under different energy levels (40-200 keV, 30 keV intervals) and slope of energy spectrum curve (λ HU) both in the arterial and venous phase were measured. The differences in clinical, morphological features, and spectral CT quantitative parameters between the two groups were compared using independent sample ttest, Mann-Whitney U test, or χ2 test. Multivariate logistic regression analyses were used to construct three models based on clinical and morphological features, quantitative DLCT parameters and their combination, respectively. The receiver operating characteristic curve was used to evaluate the predictive performance of these models for the initial recurrence risk of PTC patients, and the area under the curve (AUC) was compared using the DeLong test. Results:Significant differences were found in gender, lesion long diameter, lesion short diameter and calcification between the low-risk group and intermediate-high-risk groups ( P<0.05). The arterial phase IC, arterial phase Z eff, arterial phase λ HU, arterial phase CT 40 keV, venous phase NIC and venous phase NZ eff in intermediate-high-risk group were significantly lower than those in the low-risk group ( P<0.05). The logistic regression analysis revealed that the clinical model included gender ( OR=2.895, 95% CI 1.047-8.002, P=0.040) and lesion long diameter ( OR=1.142, 95% CI 1.042-1.251, P=0.004), with an AUC of 0.720, sensitivity of 63.0%, and specificity of 78.7% in predicting the initial recurrence risk of PTC patients. The DLCT quantitative parameter model included arterial phase IC ( OR=0.580, 95% CI 0.370-0.908, P=0.017), venous phase NIC ( OR=0.077, 95% CI 0.011-0.536, P=0.010), and venous phase NZ eff ( OR=0.002, 95% CI 0.001-0.103, P=0.009), with an AUC of 0.774, sensitivity of 71.9%, and specificity of 70.0%. The AUC of the combined model was 0.857, with a sensitivity of 74.1%, and specificity of 88.0%, outperforming the clinical model ( Z=2.92, P=0.004) and the DLCT quantitative parameter model ( Z=2.07, P=0.046). Conclusion:Multi-parametric analysis based on DLCT can help predict the initial recurrence risk for PTC, and combining it with clinical and morphological features, the predictive accuracy can be improved.

Objective:To explore the diagnostic value of radiomics based on arterial-venous mixed images derived from dual-energy CT (DECT) data in diagnosis of cervical lymph nodes (LNs) metastasis of papillary thyroid cancer (PTC).Methods:From June 2017 to December 2018, eighty-four patients with preoperatively DECT scanning and pathologically confirmed PTC (129 non-metastatic LNs and 97 metastatic LNs) in the First Affiliated Hospital of Nanjing Medical University were included in this study. The clinical and imaging data of all patients were retrospectively analyzed. The training cohort consisted of 62 PTC cases with 156 LNs (91 non-metastatic LNs and 65 metastatic LNs). An independent validation cohort consisted of 22 PTC patients with 70 LNs (38 non-metastatic LNs and 32 metastatic LNs). Semi-automatic LNs segmentation was conducted on arterial-venous mixed images derived from DECT using Syngo.via Frontier Radiomics software. Totally 1 226 radiomics features were extracted from arterial-venous mixed images for each LN. The least absolute shrinkage and selection operator (LASSO) regression was applied for radiomics features selection and signature building. The logistic regression modeling was used to construct diagnostic models based on the CT image features of LNs (model 1), the radiomics signature (model 2) and the combination of the CT image features and radiomics signature (model 3). An intuitive nomogram was plotted for model 3. The ROC curve analyses and area under the curve (AUC) were performed to evaluate the diagnostic efficiency of the three models, with the performances compared using the Delong test.Results:Model 1 was developed with LNs shape, degree of enhancement, pattern of enhancement, calcification and extra nodal extension. Three arterial phase radiomics features were selected and used to establish radiomics signature using LASSO regression (model 2). Model 3 was developed with LNs size, shape, degree of enhancement and radiomics signature. In both the training and validation cohort, model 3 showed the best diagnostic performance (AUC=0.965, 0.933), followed by model 2 (AUC=0.947, 0.910), and both these two models significantly outperformed model 1 (AUC=0.850, 0.846) (training cohort, Z=4.066 and 3.758, P both<0.001; validation cohort, Z=2.871 and 1.998, P=0.017 and 0.042) respectively. Conclusion:The radiomics model based on arterial-venous mixed images derived from DECT data can realize effective diagnosis of LNs metastasis in patients with PTC; and the combination model of radiomics signature with CT image features can further improve the diagnostic accuracy.

Objective Toinvestigatetheclinicalvalueofreadoutsegmentationoflongvariableecho-trainsdiffusion-weightedimaging (RESOLVE-DWI)inthediagnosisandstagingofthyroid-associatedophthalmopathy(TAO).Methods Atotalof30consecutivepatientswith TAOand30healthycontrols(HCs)whounderwentRESOLVE-DWIwereenrolledinourstudy.ADCvaluesofextraocularmuscles (superiorrectus,inferiorrectus,medialrectusandlateralrectus)were measuredandcomparedbetween TAOsand HCs,active TAOsandinactiveTAOs,orinactiveTAOsandHCs.ROCanalysiswasperformedtoevaluatethediagnosticvalueofsignificantparametersfor discriminatingactivefrominactiveTAOs.Results TheADCvaluesofallextraocularmusclesinTAOsweresignificantlyhigherthan thoseinHCs(P<0.05).Meanwhile,alltheextraocularmusclesinactiveTAOsshowedsignificantlyhigherADCvaluesthanthose ininactiveTAOs(P<0.05),exceptlateralrectus(P=0.267).WhilstnosignificantdifferenceswerefoundontheADCvaluesofall extraocularmusclesbetweeninactiveTAOsandHCs(P>0.05).ROCanalysisresultsindicatedthattheADCvalueofmedialrectus showedtheoptimalstagingefficacy(cutoffvalue,1.40×10-3 mm2/s;AUC,0.766;sensitivity,92.1%;specificity,59.1%).Conclusion RESOLVE-DWIanditsderivedADCvaluesofextraocularmusclescanassistinthediagnosisofTAO.TheADCvalueofmedial rectushastheoptimalefficacyontheevaluationofitsclinicalactivity.

The human microbiome project (HMP) promoted further understanding of human oral microbes. However, research on the human oral microbiota has not made as much progress as research on the gut microbiota. Currently, the causal relationship between the oral microbiota and oral diseases remains unclear, and little is known about the link between the oral microbiota and human systemic diseases. To further understand the contribution of the oral microbiota in oral diseases and systemic diseases, a Human Oral Microbiome Database (HOMD) was established in the US. The HOMD includes 619 taxa in 13 phyla, and most of the microorganisms are from American populations. Due to individual differences in the microbiome, the HOMD does not reflect the Chinese oral microbial status. Herein, we established a new oral microbiome database-the Oral Microbiome Bank of China (OMBC, http://www.sklod.org/ombc ). Currently, the OMBC includes information on 289 bacterial strains and 720 clinical samples from the Chinese population, along with lab and clinical information. The OMBC is the first curated description of a Chinese-associated microbiome; it provides tools for use in investigating the role of the oral microbiome in health and diseases, and will give the community abundant data and strain information for future oral microbial studies.
China ; Humans ; Microbiota ; Mouth ; microbiology