Improved breast lesion detection in mammogram images using a deep neural network.

PURPOSE: This study aimed to investigate the effect of using a deep neural network (DNN) in breast cancer (BC) detection. METHODS: In this retrospective study, a DNN-based model was constructed from a total of 880 mammograms that 220 patients underwent between April and June 2020. The mammograms were reviewed by two senior and two junior radiologists with and without the aid of the DNN model. The performance of the network was assessed by comparing the area under the curve (AUC) and receiver operating characteristic curves for the detection of four features of malignancy (masses, calcifications, asymmetries, and architectural distortions), with and without the aid of the DNN model and by the senior and junior radiologists. Additionally, the effect of utilizing the DNN on diagnosis time for both the senior and junior radiologists was evaluated. RESULTS: The AUCs of the model for the detection of mass and calcification were 0.877 and 0.937, respectively. In the senior radiologist group, the AUC values for evaluation of mass, calcification, and asymmetric compaction were significantly higher with the DNN model than those obtained without the model. Similar effects were observed in the junior radiologist group, but the increase in the AUC values was even more dramatic. The median mammogram assessment time of the junior and senior radiologists was 572 (357-951) s, and 273.5 (129-469) s, respectively, with the DNN model, and the corresponding assessment time without the model, was 739 (445-1003) s and 321 (195-491) s, respectively. CONCLUSION: The DNN model exhibited high accuracy in detecting the four named features of BC and effectively shortened the review time by both senior and junior radiologists.

Follow-up study of clinical and chest CT scans in confirmed COVID-19 patients.

OBJECTIVE: To analyze the CT imaging results of patients with COVID-19 who previously received several follow-up visits and to explain the changes in pulmonary inflammation. METHODS: Cases of 15 patients with COVID-19 were retrospectively analyzed: their epidemiology, clinical history, laboratory tests, and multiple CT chest scans obtained during the disease period were studied. RESULTS: The CT scans of the 15 patients showed different results. Four patients had no abnormal findings in their chest CT scans. The first scan of 1 patient revealed right lower lobe inflammation, while the lesion had been completely absorbed in follow-up. Two patients showed bilateral pulmonary inflammation in the first scan which had been absorbed by follow-up but the last examination showed extensive fibrosis. Two patients had no abnormalities in their first CT scans, while pulmonary inflammation was found in the second scan and this had not been completely absorbed by the last follow-up. One patient had pulmonary interstitial lesions with no evidence of National Cochlear Implant Programme (NCIP) on the first and second CT scans. NCIP was found at the third scan, and pulmonary inflammation was not completely absorbed at the last follow-up. Three patients were in the early stage of inflammation at the first scan, and the lesions were absorbed and repaired at the last follow-up. However, the lesions were not completely absorbed. One patient was in the advanced stage at the first scan, and the last follow-up pulmonary lesions were not completely absorbed. The first CT scan of 1 patient revealed large ground-glass opacity in the lungs involving the inner and middle bands. After follow-up, the disease progressed, and this condition was consistent with severe manifestations. CONCLUSION: The follow-up of chest CT can reflect the change process of NCIP and the treatment effect. The first CT scan of lung lesions has a certain predictive effect on the outcome and prognosis of patients.

[Value of susceptibility weighted imaging in hepatic fibrosis staging by using MR in a rabbit model].

OBJECTIVE: To assess the feasibility of susceptibility weighted imaging (SWI) in staging hepatic fibrosis(HF). METHODS: Sixty healthy rabbits were divided into three groups: HF group(n=32), control group(n=16), supplementary group(n=12). Rabbits in HF group and supplementary group were injected subcutaneously with 50% CCl4 oil solution to establish hepatic fibrosis model. On the basis of preliminary test, eight rabbits from HF group and four rabbits from control group underwent liver conventional MR scans and SWI once a time at 4, 5, 6, 10 weeks after CCl4 administration.After MR scans at each time point, rabbits were killed to detect pathological staging with Scheuer staging.The liver signal intensity (SI) and liver-to-muscle SI ratios (SIR) were measured. According to the Scheuer classification of histological fibrosis stages, the correlation about the SI value, SIR value and the histological fibrosis stages was investigated by using the Spearman correlation test. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of SWI for staging HF on the basis of the histopathologic analysis of HF. RESULTS: There were fifteen rabbits in pathological staging F0, the value of SIR and SI was 0.974 ± 0.018, 374±44, SIR values of pathological staging from F1 to F4 were 0.963 ± 0.018, 0.796 ± 0.023, 0.786 ± 0.025, 0.512±0.024 respectively. SI values of pathological staging from F1 to F4 were 372±18, 376±22, 346±15, 288±19 respectively. In the early period of liver fibrosis, there were no statistical differences in the SI value between F0 and F1, F1 and F2 stage.With progression of hepatic fibrosis, from F2 to F4, SI value decreased, the difference was statistically significant (P<0.05). With the progress of liver fibrosis, SIR value was reduced. It was negatively correlated with the HF stages and SIR value(r=-0.896, P<0.05). ROC curve analysis showed that the efficiency of SI value diagnosis in liver fibrosis was high in the late stage of liver fibrosis, but it was low in the early stage.The performance of liver-to-muscle SI ratio on SWI was high in the early stage. Liver-to-muscle SI ratio had a higher diagnostic performance than SI in the diagnosis of liver fibrosis stages. CONCLUSION: SWI can be a safe, reliable method for staging hepatic fibrosis and provide quantitative imaging basis for clinical treatment.