To evaluate the efficacy of the “double-low” scanning protocol combined with the artificial intelligence iterative reconstruction (AIIR) algorithm for abdominal computed tomography (CT) enhancement in obese patients and to identify the optimal AIIR algorithm level. Patients with a body mass index ≥ 30.00 kg/m2 who underwent abdominal CT enhancement were randomly assigned to groups A or B. Group A underwent conventional protocol with the Karl 3D iterative reconstruction algorithm at levels 3–5. Group B underwent the “double-low” protocol with AIIR algorithm at levels 1–5. Radiation dose, total iodine intake, along with subjective and objective image quality were recorded. The optimal reconstruction levels for arterial-phase and portal-venous-phase images were identified. Comparisons were made in terms of radiation dose, iodine intake, and image quality. Overall, 150 patients with obesity were collected, and each group consisted of 75 cases. Karl 3D level 5 was the optimal algorithm level for group A, while AIIR level 4 was the optimal algorithm level for group B. AIIR level 4 images in group B exhibited significantly superior subjective and objective image quality than those in Karl 3D level 5 images in group A (P < 0.001). Group B showed reductions in mean CT dose index values, dose-length product, size-specific dose estimate based on water-equivalent diameter, and total iodine intake, compared with group A (P < 0.001). The “double-low” scanning protocol combined with the AIIR algorithm significantly reduces radiation dose and iodine intake during abdominal CT enhancement in obese patients. AIIR level 4 is the optimal reconstruction level for arterial-phase and portal-venous-phase in this patient population.
该文报道1例胃壁异位支气管源性囊肿和肠源性囊肿患者。男性，23岁，CT表现为左侧肾上腺区与胃间见1个类圆形囊性低密度病变，胃大弯侧另见1个类圆形囊性低密度病变，增强扫描囊壁轻度强化。病理诊断分别为黏膜下支气管源性囊肿和黏膜固有肌内肠源性囊肿。
Purpose: To develop predictive models based on the integration of radiomics with the Vesical Imaging-Reporting and Data System (VIRADS) for determining muscle invasion of bladder cancer. Materials and Methods: One hundred ninety-one patients were retrospectively included in this study from January 2015 to March 2022. Of these, 121 data were randomly divided into training and validation sets at a ratio of 7:3. The remaining data (n = 70) served as the independent testing set. The radiomics features were extracted from bladder cancer on high-b-value DWI images. The pipelines of radiomics models were trained in the training set. One optimal model was selected based on the performance in the validation set. Then, the selected model was tested in the independent testing set. Two radiologists evaluated the VI-RADS based on T2WI and DWI. Reader 1 was an experienced reader, and Reader 2 was an inexperienced reader. A clinical-radiomics model was built by integrating the radiomics signature and VI-RADS. The performance was assessed using receiver operating characteristic curve analysis. The histopathological results were used as the standard reference to assess the diagnostic accuracy of muscle invasion. Results: The radiomics model had area under the curve (AUC) values of 0.801, 0.867, and 0.806 in the training, validation, and testing sets, respectively. The VI-RADS scores of Readers 1/2 yielded AUC values of 0.831/0.781, 0.909/0.815, and 0.871/0.776 in the training, validation, and testing sets, respectively. The clinical-radiomics model for Readers 1/2 revealed AUC values of 0.889/0.854, 0.961/0.919, and 0.881/0.844 in the training, validation, and testing sets, respectively. The performance of the clinical-radiomics model was improved compared to the VI-RADS score for inexperienced Reader 2 (P < 0.05). Conclusion: The radiomics model was useful in the diagnosis of muscle invasion of bladder cancer. The clinical-radiomics model integrating radiomics and VI-RADS further improved the performance compared to VI-RADS alone, which was helpful for readers with less diagnostic experience. & COPY; 2022 Elsevier Inc. All rights reserved.
Objective:To investigate the value of intratumoral and peritumoral radiomics features of multi-parameter MRI in evaluation of the status of human epithelial growth factor receptor 2 in breast cancer.Methods:The clinical, pathological and imaging data of 340 patients with pathologically confirmed breast cancer in Henan Provincial People′s Hospital from September 2019 to December 2020 were retrospectively collected. All patients were female, 48 (42, 55) years old. All patients underwent multi-parameter breast MRI before surgery, including dynamic contrast-enhanced T 1WI (DCE-T 1WI), fat-suppressed T 2WI (T 2WI) and diffusion-weighted imaging (DWI). The region of interest (ROI) for lesions were manually delineated and the segmented ROIs were zoomed in ring shape by 4 mm to acquire ROI intra and ROI prei, respectively. Then six sets of radiomics features were extracted from ROI intra and ROI prei of DCE-T 1WI, T 2WI and DWI. The cases were divided into a training set (272 cases) and a test set (68 cases) by stratified sampling at a ratio of 4∶1. The Mann-Whitney U test, Select K Best and minimum absolute contraction and selection operator were used for feature selection of the 6 sets of radiomics features. The feature subsets after reduction were used to construct independent and combined radiomics signatures with support vector machine algorithm to predict the HER2 status of breast cancer. Receiver operating characteristic curve was generated and area under curve (AUC) was calculated to compare the prediction performance of different models. Results:Of the 340 patients, 80 were HER2-positive and 260 were HER2-negative. Among the radiomics signatures based on single sequence, the DWI peri showed the best performance in predicting HER2 status of breast cancer, with an AUC of 0.678 for the test set. Among the combination of intratumoral and peritumoral radiomics signatures based on same sequence, the DWI intra+DWI peri had the highest prediction value, achieving an AUC of 0.774 for the testing set. Among the intratumoral or peritumoral radiomics signatures derived from two different sequences, the DCE-T 1WI intra+DWI intra and T 2WI peri+DWI peri showed the best predictive performance, yielding AUC of 0.766 and 0.769 in the testing set, respectively. Among the combination of intratumoral or peritumoral radiomics signatures derived from all 3 sequences or combinations of all features, the DCE-T 1WI intra+T 2WI intra+DWI intra+DCE-T 1WI peri+T 2WI peri+DWI peri obtained the highest prediction efficiency, with an AUC of 0.913 for the testing set. Conclusion:The radiomics features of intratumoral and peritumoral regions based on multi-parameter MRI have a certain value in non-invasive evaluation of HER2 status of breast cancer, which can help clinicians to provide scientific basis for decision-making of targeted therapy in patients with breast cancer.
Objective:To investigate the changes in gray matter volume of the subsystems as well as intra-subsystem and inter-subsystem functional connectivity in the default mode network (DMN) of relapsing-remitting multiple sclerosis (RRMS) patients with preserved cognitive function.Methods:In this prospective study, thirty-seven RRMS patients with preserved cognitive function who were admitted to Huashan Hospital of Fudan University from April 2020 to January 2021 (RRMS group) and 43 healthy volunteers (HC group) were recruited. Patients in the RRMS group received the cognitive assessment using a clinical cognitive functioning scale. Three-dimensional T 1WI and resting-state functional MRI were performed to obtain the brain structural and functional data. The DMN was divided into three subsystems: CORE, dorsal medial prefrontal cortex (DMPFC), and medial temporal lobe (MTL). The gray matter volume of the three subsystems were extracted from the gray matter volume map generated by spatial normalization; 24 regions of interest (ROIs) of the DMN were defined based on Yeo′s 17 networks, and their functional connectivity values were calculated to derive the mean intra-subsystem and inter-subsystem functional connectivity values. Differences in gray matter volume and functional connectivity between the RRMS and HC groups were compared using independent sample t-tests; Spearman′s partial correlation was used to analyze the correlation between subsystems′ gray matter volume and functional connectivity, as well as between subsystems′ functional connectivity and clinical scale scores. Results:Compared to the HC group, the gray matter volume of the three subsystems of the DMN were considerably reduced in the RRMS group ( P<0.05). The functional connectivity within and between the three subsystems were not statistically significantly different between the HC and RRMS groups ( P>0.05). Based on the ROI analysis, patients with RRMS the brain regions with significantly reduced DMN intra-subsystem functional connectivity values were mainly located in the left dorsomedial prefrontal cortex of the DMPFC, the right lateral temporal cortex of the DMPFC, and the left medial temporal cortex of the MTL, as compared with the HC group ( P<0.01). The gray matter volume of DMPFC was positively correlated with the functional connectivity within DMPFC in the control group ( r=0.326, P=0.040). In the RRMS group, the gray matter volume of CORE was positively correlated with the functional connectivity between CORE and DMPFC ( r=0.363, P=0.038), and the functional connectivity within CORE was positively correlated with scores on the memory and executive screening scale ( r=0.430, P=0.036). Conclusions:RRMS patients with preserved cognitive function exhibit gray matter atrophy in all three DMN subsystems. There is no correlation between the structure and function of the DMPFC subsystem. The functional connectivity within CORE subsystem may reflect memory and execution status; DMPFC and CORE may be critical encephalic regions for neurodegeneration and brain functional changes in RRMS patients with preserved cognitive function.
