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Objective@#To compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visualscore in evaluating clinical classification of severity of coronavirus disease (COVID-19). @*Materials and Methods@#We retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji MedicalCollege from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data werecollected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical). A semiquantitative visual score was used to estimate the lesion extent. A three-dimensional slicer was used toprecisely quantify the volume and CT value of the lung and lesions. Correlation coefficients of the quantitative CT parameters,semiquantitative visual score, and clinical classification were calculated using Spearman’s correlation. A receiver operatingcharacteristic curve was used to compare the accuracies of quantitative and semi-quantitative methods. @*Results@#There were 59 patients in group 1 and 128 patients in group 2. The mean age and sex distribution of the two groupswere not significantly different. The lesions were primarily located in the subpleural area. Compared to group 1, group 2 hadlarger values for all volume-dependent parameters (p < 0.001). The percentage of lesions had the strongest correlation withdisease severity with a correlation coefficient of 0.495. In comparison, the correlation coefficient of semiquantitative scorewas 0.349. To classify the severity of COVID-19, area under the curve of the percentage of lesions was the highest (0.807;95% confidence interval, 0.744–0.861: p < 0.001) and that of the quantitative CT parameters was significantly higher thanthat of the semiquantitative visual score (p = 0.001). @*Conclusion@#The classification accuracy of quantitative CT parameters was significantly superior to that of semiquantitativevisual score in terms of evaluating the severity of COVID-19.
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Objective:To investigate the value of texture analysis based on T 2WI and apparent diffusion coefficient (ADC) maps in discriminating low grade from high grade prostate cancer (PCa). Methods:Retrospective analysis was performed on patients who were confirmed to be PCa by pathology after surgery and underwent MRI examination in the department of radiology,Tongji Hospital,Tongji Medical College, Huazhong University of Science and Technology before radical surgery, including routine T 1WI, T 2WI and diffusion weighted imaging (DWI) sequences. 3D data analysis module of the MaZda software was used to manually draw region of interest (ROIs) slice by slice on T 2WI and ADC images, and generate volume of interest (VOI) of the entire tumor. MaZda software was also used to extract texture features. The independent sample t test or Mann-Whitney U test were used to identify the texture features with statistically significant differences between low and high grade PCa groups. Lasso regression model was used to select the best combination of texture features for identifying low and high grade PCa, and then the model was built. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model in both training cohort and test cohort. Results:The best texture feature combination selected by Lasso regression model were the S (1, 0, 0) correlation of T 2WI and the S (1, 0, 0) correlation, S (1, -1, 0) sum entropy and vertical-run length nonuniformity of ADC maps. The area under the ROC curve (AUC) of the model in training cohort was 0.823, and the sensitivity and specificity were 70.4% and 80.8%, respectively, which were better than the single texture feature. The AUC of the model in test cohort was 0.714, which was worse than training cohort. Conclusion:The texture analysis of T 2WI and ADC maps is valuable for the identification of low and high grade PCa.
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Objective To access the diagnostic value of Prostate Imaging Reporting and Data System (PIRADS version 2)for prostate cancer (PCa) in prostate specific antigen (PSA) grey zone(4-10 ng/mL).Methods Treatment naive PCa and BPH patients with an increase of PSA 4-10 ng/mL from 2200 patients underwent prostate MRI from 2012 to 2016 were included,multiparameter magnetic resonance imaging (mp-MRI) prior biopsy or prostatectomy and clinical data were obtained,mp-MRI were retrospectively analyzed quantitatively by a radiology expert with 15 years experience in urogenital system imaging diagnosis and a doctor with 5 years experience in radiology diagnosis blind to the pathology results according to PIRADS v2,PIRADS v2 score and lesion zone were recorded respectively,in case of disagreement,dicision was made through discuss.TRUS guided biopsy or prostatectomy pathology serves as gold reference.Diagnostic value of PIRADS v2 for PSA grey zone PCa was calculated by receiver operating characteristic (ROC) curve,logistic regression analysis was used to access the risk factors of PCa.Results 15 PCa and 30 BPH patients were in eluded.There was no significant difference between these two groups in age,tPSA,fPSA,f/tPSA,prostate volume and PSA density.The area under ROC curve of PIRADS v2 in diagnosing PCa was 0.932[95 ％ CI 0.822-0.984],P＜0.01.Using a cutoff PIRADS＞4,the diagnosis sensitivity was 88.89 ％,specificity 87.10 ％,and positive predictive value 80 ％,negative predictive value 93.10％,respectively.Logistic regression analysis showed that PIRADS v2 score was an independent risk factor for predicting PCa,with a hazard ratio 17.847[3.745-85.078],P＜0.01.There was a positive correlation between PIRADS v2score and gleason score,r=0.585,P=0.022.Conclusion PIRADS v2 has a significantly high diagnosis value in diagnosing PSA grey zone PCa and a good correlation with pathology results.
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Objective To explore the value of readout segmentation of long variable echo-trains (RESOLVE) in the differentiation of prostate cancer from benign prostatic hyperplasia (BPH).Methods Seventy two consecutive patients with suspected prostate cancer were evaluated by 3.0 T MR examination (RESOLVE sequence included,b values=0 and 800 s/mm2) were included in our retrospective study.All the patients had ultrasound guided systemic biopsy with histopathological diagnosis.The patients were divided into group A (23 prostate cancer cases with total 43 malignant lesions) and group B (49 BPH cases with total 64 benign lesions).Two radiologists who were blinded to the clinical data quantitatively analyzed the ADC values of suspicious lesions independently.Inter-reader agreement for ADC values was assessed with Bland and Altman test,and the intra-class correlation coefficient (ICC).Difference of ADC values in two groups was assessed by student's t test.Receiver operating characteristic curve (ROC) was used to determine the best predictor and cutoff value.Results A total of 107 lesions (43 malignant and 64 benign) were identified in 72 patients.ICC was 0.976,P＜0.01.The mean ADC value of prostate cancer is lower than BPH (t=19.223,P＜0.01),(0.74±0.12) × 10 3 and (1.21±0.12) × 10-3mm2/s respectively.Diagnostic cut-off point was 0.946× 10-3mm2/s,diagnostic sensitivity 95.3 ％ (41/43),specificity 98.4％ (63/64),accuracy 97.2％ (104/107).Conclusion RESOLVE ADC value is valuable in the differential diagnosis of prostate cancer and BPH.