Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective:To investigate the predictive value of clinical factor model, deep learning model based on baseline plain CT images, and combination of both for predicting hematoma expansion in cerebral hemorrhage.Methods:The study was cross-sectional. Totally 471 cerebral hemorrhage patients who were firstly diagnosed in the Second Affiliated Hospital of Soochow University from January 2017 to December 2021 were collected retrospectively. These patients were randomly divided into a training dataset ( n=330) and a validation dataset ( n=141) at a ratio of 7∶3 by using the random function. All patients underwent two noncontrast CT examinations within 24 h and an increase in hematoma volume of >33% or an absolute increase in hematoma volume of >6 ml was considered hematoma enlargement. According to the presence or absence of hematoma enlargement, all patients were divided into hematoma enlargement group and hematoma non-enlargement group.Two-sample t test, Mann-Whitney U test or χ2 test were used for univariate analysis. The factors with statistically significant differences were included in multivariate logistic regression analysis, and independent influences related to hematoma enlargement were screened out to establish a clinical factor model. ITK-SNAP software was applied to manually label and segment the cerebral hemorrhage lesions on plain CT images to train and build a deep learning model based on ResNet50 architecture. A combination model for predicting hematoma expansion in cerebral hemorrhage was established by combining independent clinical influences with deep learning scores. The value of the clinical factor model, the deep learning model, and the combination model for predicting hematoma expansion in cerebral hemorrhage was evaluated using receiver operating characteristic (ROC) curves and decision curves in the training and validation datasets. Results:Among 471 cerebral hemorrhage patients, 136 cases were in the hematoma enlargement group and 335 cases were in the hematoma non-enlargement group. Regression analyses showed that male ( OR=1.790, 95% CI 1.136-2.819, P=0.012), time of occurrence ( OR=0.812, 95% CI 0.702-0.939, P=0.005), history of oral anticoagulants ( OR=2.157, 95% CI 1.100-4.229, P=0.025), admission Glasgow Coma Scale score ( OR=0.866, 95% CI 0.807-0.929, P<0.001) and red blood cell distribution width ( OR=1.045, 95% CI 1.010-1.081, P=0.011) were the independent factors for predicting hematoma expansion in cerebral hemorrhage. ROC curve analysis showed that in the training dataset, the area under the curve (AUC) of clinical factor model, deep learning model and combination model were 0.688 (95% CI 0.635-0.738), 0.695 (95% CI 0.642-0.744) and 0.747 (95% CI 0.697-0.793) respectively. The AUC of the combination model was better than that of the clinical model ( Z=0.54, P=0.011) and the deep learning model ( Z=2.44, P=0.015). In the validation dataset, the AUC of clinical factor model, deep learning model and combination model were 0.687 (95% CI 0.604-0.763), 0.683 (95% CI 0.599-0.759) and 0.736 (95% CI 0.655-0.806) respectively, with no statistical significance. Decision curves showed that the combination model had the highest net benefit rate and strong clinical practicability. Conclusions:Both the deep learning model and the clinical factor model established in this study have some predictive value for hematoma expansion in cerebral hemorrhage; the combination model established by the two together has the highest predictive value and can be applied to predict hematoma expansion.

Objective:To explore the value of the Nomogram model established by CT radiomics combined with clinical indicators for prediction of the severity of early acute pancreatitis (AP).Methods:From January 2016 to March 2023, the AP patients in the Second Affiliated Hospital of Soochow University were retrospectively collected. According to the revised Atlanta classification and definition of acute pancreatitis in 2012, all patients were divided into the severe group and the non-severe group. All patients were first diagnosed, and abdominal CT plain scan and enhanced scan were completed within 1 week. Patients were randomly (random number) divided into training and validation groups at a ratio of 7:3. The pancreatic parenchyma was delineated as the region of interest on each phase CT images, and the radiomics features were extracted by python software. LASSO regression and 10-fold cross-validation were used to reduce the dimension and select the optimal features to establish the radiomics signature. Multivariate Logistic regression was used to select the independent predictors of severe acute pancreatitis (SAP), and a clinical model was established. A Nomogram model was established by combining CT radiomics signature and clinical independent predictors. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive efficacy of each model.Results:Total of 205 AP patients were included (59 cases in severe group, 146 cases in non-severe group). 3, 5, 5 and 5 optimal radiomics features were selected from the plain CT scan, arterial phase, venous phase and delayed phase images of all patients, and the radiomics models were established. Among them, the arterial phase radiomics model had relatively better performance in predicting SAP, with an area under curve (AUC) of 0.937 in the training group and 0.913 in the validation group. Multivariate Logistic regression showed that C-reactive protein (CRP) and lactate dehydrogenase (LDH) were independent predictors of SAP, and they were used to establish a clinical model. The AUC in the training and validation groups were 0.879 and 0.889, respectively. The Nomogram model based on arterial phase CT radiomics signature, CRP and LDH was established, and the AUC was 0.956 and 0.947 in the training group and validation group, respectively. DCA showed that the net benefit of Nomogram model was higher than that of clinical model or radiomics model alone.Conclusions:The Nomogram model established by CT radiomics combined with clinical indicators has high application value for early prediction of the severity of AP, which is conducive to the formulation of clinical treatment plans and prognosis evaluation.

Objective:To investigate the value of dual-detector spectral CTA in distinguishing infarct core from penumbra in patients with acute ischemic stroke(AIS), and to further explore the risk factors associated with infarct core and their predictive value.Methods:The imaging and clinical data of 163 patients with AIS who met the inclusion criteria admitted to the Second Affiliated Hospital of Soochow University from March 2022 to May 2023 were retrospectively analyzed. Patients from March 2022 to December 2022 were used as the training group, and patients from January 2023 to May 2023 were used as the validation group for internal validation. The head and neck spectral CTA and brain CT perfusion imaging with dual-layer detector spectral CT were all carried out on all patients. Using CTP as reference, the patients were divided into infarct core group and non-infarct core group according to whether an infarct core occurred in the hypoperfusion regions of brain tissue. Multivariate logistic regression analysis was used to screen predictors related to the infarct core. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy.Results:A total of 163 patients were included in the study, including 112 in the training group and 51 in the validation group. There were significant differences in iodine density, effective atomic number, hypertension, triglyceride and neutrophils between the two groups ( P< 0.05). The cutoff values for iodine density values and effective atomic number values were 0.215 mg/mL and 7.405, respectively. Multivariate logistic regression analysis showed that iodine density and hypertension were independent risk factors for infarct core in AIS, and triglyceride was an independent protective factor. The area under the ROC curve (AUC) of iodine density value was the largest (0.859), with a sensitivity of 70.27%, and a specificity of 90.67%, which had a good predictive value. The ROC curve analysis results for the validation group were consistent with the training group. Conclusions:Spectral CT parameters iodine density values and effective atomic number values have the potential to distinguish the infarct core area from the penumbra area in patients with AIS. Iodine density and hypertension were independent risk factors of infarct core in AIS, triglyceride was an independent protective factor, and iodine density values obtained by dual-layer spectral detector CT had a high predictive value.

Objective:To explore the consistency of MRI-based ovarian-adnexal report and data system (O-RADS) score and its diagnostic value for ovarian adnexal masses.Methods:The MRI data of 309 patients with ovarian adnexal masses confirmed by pathology were retrospectively collected from January 2017 to August 2021 in the Second Affiliated Hospital of Soochow University, including 327 lesions consisted of 250 benign lesions, 21 borderline lesions, and 56 malignant lesions confirmed by pathology. Borderline and malignant lesions were classified into the malignant group ( n=77) and benign lesions were classified as benign group ( n=250). Two radiologists scored all lesions according to the MRI-based O-RADS, and scored again after 6 months. The proportion of borderline/malignant lesions in each MRI-based O-RADS score was calculated. The weighted Kappa test was used to assess the intra-reader and inter-reader consistency of the image interpretation results. The receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic efficacy of MRI-based O-RADS classification for distinguishing benign and malignant ovarian adnexal masses. Results:The weighted Kappa value of the MRI-based O-RADS score between the two radiologists was 0.810 (95%CI 0.764-0.855), and the weighted Kappa values of the two radiologists′ scores at different times were 0.848 (95%CI 0.806-0.889) and 0.875 (95%CI 0.835-0.914), respectively. The borderline/malignant lesions accounted for 0/16, 0.8% (1/127), 10.1% (10/99), 76.0% (57/75), 9/10 and 0/17, 0 (0/122), 8.0% (8/100), 76.2% (48/63), and 84.0% (21/25) of the lesions in the two radiologists based on the MRI O-RADS score of 1, 2, 3, 4, and 5, respectively. When adopting O-RADS score>3 as a cut-off value, the area under the ROC curve of the two radiologists for distinguishing benign and malignant ovarian adnexal masses was 0.928 (95%CI 0.895-0.954) and 0.942 (95%CI 0.911-0.965), respectively. The sensitivity was 0.857 and 0.896, the specificity was 0.924 and 0.924, and the accuracy was 0.908 and 0.917 respectively.Conclusion:The MRI-based O-RADS yields high diagnostic efficiency in the evaluation of benign and malignant ovarian adnexal masses, and the intra-reader and inter-reader consistency of the image interpretation is strong.

Objective To compare the predictive values of swirl sign and black hole sign on CT scanning in early hematoma expansion in spontaneous intracerebral hemorrhage (SICH) patients.Methods Two hundred and ten firstly diagnosed SICH patients,admitted to our hospital from January 2012 to December 2018,were enrolled in the study.All patients were divided into hematoma expansion and non-hematoma expansion group according to whether early hematoma expansion appeared;and they were also divided into positive imaging sign group and negative imaging sign group according to whether imaging signs appeared;the clinical and imaging data were compared between these groups,respectively.The accuracies of swirl sign and black hole sign in predicting early hematoma expansion were analyzed using receiver operator characteristic (ROC) curve.Multivariate Logistic regression analysis was performed to determine the independent risk factors for early hematoma expansion.Results (1) In the 57 patients with early hematoma expansion,21 (36.8％) had swirl sign,and 17 (29.8％) had black hole sign;in the 153 patients without hematoma expansion,12 (7.8％) had swirl sign and 22 (14.4％) had black hole sign;the differences between the two groups were statistically significant (P＜0.05).As compared with those in the non-hematoma expansion group,the admission systolic blood pressure increased significantly and number of patients with intraventricular hemorrhage was significantly larger in the hematoma expansion group (P＜0.05).(2) There were no statistical differences in clinical and imaging data between the patients with swirl sign (n=33) and patients without swirl sign (n=177,P＞0.05);the hematoma volume in patients with black hole sign (n=39) was significantly increased as compared with that in patients without black hole sign (n=171,P＜0.05),and there were no statistical differences in other clinical and imaging data between patients with and without black hole sign (P＞0.05).(3) The areas under ROC curve of swirl sign,black hole sign,and "swirl sign combined with black hole sign" were 0.645,0.577,and 0.570,respectively.(4) Multivariate Logistic regression analysis showed that admission systolic blood pressure,swirl sign and black hole sign were independent risk factors for early hematoma expansion (P＜0.05).Conclusion In comparison to black hole sign and "swirl sign combined with black hole sign",the swirl sign has higher predictive value in early hematoma expansion in ICH patients.

Objective To investigate the relationship between histogram analysis of DCE-MRI quantitative parameters and clinical stage of nasopharyngeal carcinoma (NPC).Methods 70 patients with NPC confirmed by pathology underwent MRI examination and staging.NPC tumors were measured by full-volume ROI setting method,and the obtained DCE-MRI quantitative parameters were analyzed by histogram.Spearman correlation coefficients were obtained to evaluate the potential correlation between the DCE-MRI histogram quantitative parameters and NPC clinical stages.Results The histogram-based Ktrans (mean,10 th,75 th,90 th),Kep (mean,10 th,kurtosis),and Ve (mean,90 th,skewness)had correlation with T stage (P＜0.05,respectively).The histogram-based Ktrans (mean)and Ve (mean,90 th) showed correlation with N stage (P＜0.05,respectively).The histogram-based Kep (kurtosis)and Ve (mean)had correlation with M stage (P＜0.05,respectively).The histogram-based Kep had no correlation with N stage,and Ktrans had no correlation with M stage. The histogram-based Ktrans (mean,10 th,75 th,90 th),Kep (10 th,75 th,kurtosis)and Ve (mean,75 th,90 th)had correlation with overall stage (P＜0.05,respectively).Conclusion The histogram analysis of DCE-MRI quantitative parameters showed that the multiple parameters associated with NPC overall stages.DCE-MRI quantitative parameters non-invasively reflect the aggressiveness and progression of NPC.The histogram analysis of DCE-MRI quantitative parameters may play a role in clinical stage of NPC.

Objective To evaluate the imaging features of nigrosome-1 in Parkinson's disease (PD) with a 3 T scanner by susceptibility weighted imaging (SWI),and to explore its clinical relevance.Methods Thirty-two patients with primary PD diagnosed by neurologists were collected.Healthy controls matched to their age and gender were recruited during the same period (n=20).All subjects underwent routine brain magnetic resonance imaging (MRI) and sensitive weighted imaging (SWI).The SWI images of the subjects were evaluated to evaluate nigrosome-1 by blinded investigators.Then,the correlation between imaging features and clinical data was analyzed.Results In the PD group,21 cases of bilateral "absent swallow-tail sign",five cases of bilateral "indecisive swallow-tail sign",five cases of "absent swallow-tail sign" on one side and "indecisive swallow-tail sign" on the other side,and one case of bilateral "clear swallow-tail sign" were found.The course of the "absent swallow-tail sign" group (56 (54) months) was significantly longer than the "non-absent swallow-tail sign" group (18 (18) months;U=-2.47,P=0.01).The Hoehn-Yahr stage was significantly higher in the "absent swallow-tail sign" group (2.0 (0.5)) than in the "non-absent swallow-tail sign" group (1.5 (0.5),U=-2.21,P=0.03).There was also a statistically significant difference in the Unified Parkinson's Disease Rating Scale score (24 (8),13 (14)) between the two groups (U=-2.91,P=0.01).However,there were no statistically significant differences between the two groups in the Hamilton Depression Scale score (5 (2) vs 5 (7),U=-0.10,P=0.94) and the Hamilton Anxiety Scale score (3.0 (2.5) vs 3.0 (3.0),U=-0.02,P=1.00).Conclusion The images of nigrosome-1 by SWI are closely related to the severity of the condition and motor symptoms of patients with PD,which can reflect the severity of the disease.

Objective To explore the difference in efficacy between multiparametric MRI (Mp-MRI) based on prostate imaging reporting and data system version 2 (PI-RADS v2) and abbreviated biparametric MRI (Bp-MRI) in detecting prostate cancer (PCa) and clinically significant prostate cancer (csPCa), and to evaluate the consistency of image interpretation between different readers. Methods The imaging, pathological and clinical data of patients with prostatic Mp-MRI in our hospital from February 2015 to June 2018 were retrospectively analyzed. At the beginning, 250 patients were randomly selected. Two radiologists visually evaluated the images of those patients using two 5-point scoring schemes based on Mp-MRI and Bp-MRI. The remaining cases were independently proceeded by one of the radiologists using two schemes respectively. Weighted Kappa test was used to assess the consistency of the results interpreted by the two radiologists. The receiver operating characteristic (ROC) curve was used to evaluate the efficiency of the two scoring schemes in detecting PCa and csPCa, and with Z test to investigate whether there was any difference in detection efficiency between the two schemes. Results Nine hundred and seventy eight patients were eventually enrolled in the study. The results of the consistency assessment showed that there was good agreement between the two radiologists, whether using Mp-MRI or Bp-MRI, with the weighted Kappa coefficient of 0.800 and 0.812, respectively. The ROC curve analysis showed that the area under the curve (AUC) of PCa detected by Mp-MRI and Bp-MRI was 0.873 and 0.879, respectively, and the AUC of csPCa detected was 0.922 and 0.932, respectively. In addition, there was no statistically significant difference between the AUC of PCa and csPCa detected by the two schemes (P>0.05). Conclusion The Bp-MRI scoring scheme has good stability in the evaluation of benign and malignant prostate, and its detection efficiency of PCa or csPCa is not lower than that of standard Mp-MRI based on PI-RADS v2.