The value of intra-tumoral and peri-tumoral early dynamic contrast-enhanced MRI-based radiomics models in identifying benign from malignant in breast imaging-reporting and data system 4 breast tumors / 中华放射学杂志

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.

Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)

For diagnosis of COVID-19, a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to two days to complete, serial testing may be required to rule out the possibility of false negative results, and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiologic findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and laboratory testing to rapidly diagnose COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARSCoV-2. In a test set of 279 patients, the AI system achieved an AUC of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of RT-PCR positive COVID-19 patients who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.

Physiochemical and biological evaluation of SLM-manufactured Ti-10Ta-2Nb-2Zr alloy for biomedical implant applications.

Titanium alloys, such as Ti-10Ta-2Nb-2Zr (TTNZ), are promising biomaterials due to their excellent biocompatibility and low Young's modulus. The TTNZ samples herein were manufactured by selective laser melting and the novel material was evaluated as a dental implant in vitro and in vivo. The microstructure, mechanical properties, electrochemical behaviour, cytotoxicity, haemocompatibility and osteogenic differentiation were systematically investigated. Based on the tensile test results, the as-printed TTNZ samples had an elongation of 20.23% ± 1.95%, an ultimate tensile strength of 646.61 ± 24.96 MPa and a Young's modulus of 23.72 ± 1.18 GPa. According to the biocompatible value, the as-printed TTNZ sample exhibited no cell cytotoxicity and it showed even better cell adhesion ability than that of the as-printed Ti-6Al-4 V and wrought Ti-6Al-4 V samples. The haemolysis percentage of the as-printed TTNZ sample was 0.629% ± 0.363%. Moreover, the as-printed TTNZ sample facilitated protein adsorption and osteogenic differentiation of human osteoblast-like (MG-63) cells in vitro. The in vivo data also demonstrated the histocompatibility of the as-printed TTNZ. In summary, the as-printed TTNZ developed in this study demonstrated good biocompatibility, low stress shielding, excellent ductility and great osteogenic differentiation. These results indicated that as-printed TTNZ alloys can be promising for end-use human biomedical applications.

Imaging dynamic observation of cured COVID-19 patients with imported coronavirus pneumonia / 中华放射学杂志

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.

Imaging dynamic observation of COVID-19 patients cured by imported coronavirus pneumonia / 中华放射学杂志

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.

Correlation Between Apparent Diffusion Coefifcient Value on MR Diffusion-weighted Imaging and Prognostic Factors in Breast Invasive Ductal Carcinomas / 中国医学影像学杂志

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.