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Objective:To explore the value of radiomics model based on intratumoral and peritumoral early dynamic contrast-enhanced (DCE) MRI for identifying benign and malignant in breast imaging reporting and data system (BI-RADS) 4 tumors.Methods:A total of 191 patients diagnosed with BI-RADS 4 breast tumors by breast MRI examination with clear pathological diagnosis from January 2016 to December 2020 in the First Affiliated Hospital of Bengbu Medical College were analyzed retrospectively, including 77 benign and 114 malignant cases, aged 23-68 (46±10) years. The one-slice image with the largest area of the lesion of the second stage DCE-MRI images was selected to outline the region of interest, and automatically conformal extrapolated by 5 mm to extract the intra-tumoral and peritumoral radiomics features. The included cases were randomly divided into training and testing cohorts in the ratio of 8∶2. The statistical and machine learning methods were used for feature dimensionality reduction and selection of optimal radiomics features, and logistic regression was used as the classifier to establish the intratumoral, peritumoral, and intratumoral combined with peritumoral radiomics models. The independent risk factors that could predict the benignity and malignancy of breast tumors were retained as clinical-radiological characteristics by univariate and multivariate logistic regression to establish a clinical-radiological model. Finally, the intratumoral and peritumoral radiomics features were combined with clinical-radiological features to develop a combined model of the three. The receiver operating curve was used to analyze the predictive performance of each model and calculate the area under the curve (AUC),the AUC was compared by DeLong test. The stability of the three-component combined diagnostic model was tested by 10-fold cross-validation, and the model was visualized by plotting nomogram and calibration curves.Results:In the training cohort, the AUC of the three-component combined model for identifying benign and malignant BI-RADS 4 breast tumors was significantly higher than that of the intratumoral radiomics model ( Z=3.38, P<0.001), the peritumoral radiomics model ( Z=4.01, P<0.001), the intratumoral combined with peritumoral radiomics model ( Z=3.11, P=0.002), and the clinical-radiological model ( Z=3.24, P=0.001). And the AUC, sensitivity, specificity, accuracy, and F1-score of the three-component combined model were 0.932, 91.2%, 86.9%, 87.0% and 0.89, respectively. In the testing cohort, the three-component combined model also had the highest AUC value (0.875), and diagnostic sensitivity, specificity, accuracy and malignancy F1-score were 95.7%, 62.5%, 76.9%, and 0.89, respectively. The AUC calculated by 10-fold cross-validation was 0.90 (0.85-0.92), and the predicted curve of the three-component combined model in the calibration curve was in good agreement with the ideal curve. Conclusion:The three-component combined diagnostic model based on the intratumoral and peritumoral radiomics features and clinical-radiological features of early DCE-MRI has good performance and stability for identifying the benign and malignant in BI-RADS 4 breast tumors, and it can provide guidance for clinical decision non-invasively.
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Objective@#To explore the imaging changes of lung lesions in patients with imported COVID-19 patients when reaching the discharge standard.@*Method@#The clinical and CT imaging data of 60 patients with imported COVID-19 cured and discharged from January to February 2020 in Anhui Province were retrospectively collected. The clinical characteristics of the patients and the characteristics of chest CT images at discharge were analyzed.@*Results@#Fever (57 cases) and cough (55 cases) were the main symptoms in 60 patients. At the initial diagnosis, 5 cases were mild, 53 were ordinary, and 2 were severe. In 5 light patients, 3 cases were negative in the whole course of CT examination, 2 cases were negative in the first time and abnormal in the second time. . The first CT imaging features of 55 patients (53 common type and 2 severe type)were mainly bilateral lung involvement (51 cases), multiple lesions (33 cases), more common under the pleura (40 cases), and ground glass opacities were the most common. (55 cases). The clinical features of chest CT in clinical outcomes are that the ground glass shadow in the lung gradually fades and was completely absorbed (19 cases); the scope of ground glass shadow in the lung expanded and progressed to crazy-paving, consolidation shadow, and the lesion gradually absorbs again followed by Fibrous cord shadows (27 cases); ground-glass opacities in the lungs quickly progressed to a consolidation and then slowly absorbed . Most of the lesions were accompanied by more residual fibrous cord shadows (4 cases). In 2 severe patients, the lesions in the lungs were larger ground glass, and a big amount of fibrous foci remained after slowly absorption.@*Conclusion@#Chest CT plays an important role in the diagnosis and treatment of imported COVID-19, and the degree of lung involvement seen on CT images is in good agreement with clinical outcome.
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Objective:To explore the imaging changes of lung lesions in patients with imported COVID-19 when reaching the discharge standard.Methods:The clinical and CT imaging data of 60 cured patients with imported COVID-19 and discharged from January to February 2020 in Anhui Province were retrospectively collected. At the initial diagnosis, 5 cases were mild, 53 were ordinary, and 2 were severe. The clinical characteristics of the patients and the characteristics of chest CT images at discharge were analyzed.Results:Fever (57 cases) and cough (55 cases) were the main symptoms in 60 patients. In 5 mild patients, 3 cases were negative in the whole course of CT examination, 2 cases were negative in the first time and abnormal in the second time. The first CT imaging features of 55 patients (53 common type and 2 severe type) were mainly bilateral lung involvement (51 cases), multiple lesions (33 cases), more common under the pleura (40 cases), and ground glass opacities were the most common (55 cases). The features of chest CT in clinical outcomes were that the ground glass shadow in the lung gradually faded and was completely absorbed (19 cases); the scope of ground glass shadow in the lung expanded and progressed to crazy-paving, consolidation shadow, and the lesion gradually absorbed again followed by fibrous cord shadows (27 cases); ground-glass opacities in the lungs quickly progressed to a consolidation and then slowly absorbed. Most of the lesions were accompanied by more residual fibrous cord shadows (4 cases). In 2 severe patients, the lesions in the lungs were larger ground glass, and a large amount of fibrous foci remained after slow absorption.Conclusions:Chest CT plays an important role in the diagnosis and treatment of imported COVID-19, and the degree of lung involvement seen on CT images is in good agreement with clinical outcome.
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PurposeTo explore the correlation between apparent diffusion coefficient (ADC) value on MR diffusion-weighted imaging (DWI) and prognostic factors in breast invasive ductal carcinomas.Materials and Methods 103 patients with pathology-proven invasive breast ductal carcinomas underwent DWI MR scan using b=1000 s/mm2. The minimum ADC values of the lesions were determined. Histopathological specimens were analyzed for tumor size, lymph node metastasis, pathological grade (traditional prognostic factors) and the expression of prognostic factors including Ki-67, ToPo-IIα, P53 and CyclinD1. The correlations between ADC values and these prognostic factors were evaluated.Results In 103 breast invasive ductal carcinomas, there was no significant relationship between tumor size, lymph node metastasis, pathological grade and mean ADC values (P>0.05). The correlations between mean ADC values and the biological prognostic factors were not significant (P>0.05). However, positive correlations were observed between pathological grade and the expression of Ki-67 as well as ToPo-IIα(P<0.05).Conclusion ADC values cannot serve as a prognostic factor for invasive ductal breast carcinomas. However, the expression of Ki-67 and ToPo-IIα in breast invasive ductal carcinomas may be important in evaluating prognosis of the tumor and guiding clinical therapy.