with a 1-day history of fever without dizziness, cough, and headaches. On presentation, his temperature was 38•1°C. Laboratory tests showed a C-reactive protein concentration of 0•56 mg/dL (normal range 0•00-0•60] mg/dL). Complete blood count showed elevated leukocytes (10 060 cells per μL [normal range 3500-9500 cells per μL]), neutrophils (7550 cells per μL [1800-6300 cells per μL]), and monocytes (990 cells per μL [100-600 cells per μL]), while the lymphocyte count (1490 cells per μL) was in the normal range (1100-3200 cells per μL). The patient was negative for influenza A and B viruses, adenovirus, respiratory syncytial virus, and parainfluenza 1, 2, and 3 viruses. Chest CT showed multiple ground-glass opacities in the lower lobes bilaterally.The patient was given antibacterial, antiviral, and corticosteroid treatments (moxifloxacin [0•4 g/day] for 5 days, followed by ribavirin [0•5 g/day] and methylprednisolone [40 mg/day] for 5 days) via intravenous drop infusion. However, after 10 days, the patient had persistent fever (highest temperature 38•5°C), cough, and shortness of breath. The patient was diagnosed with coronavirus Contributors CZ and CG contributed to data analysis, data interpretation, the literature search, and manuscript drafting. YX contributed to data collection, data analysis, and figure preparation. MX contributed to study design and reviewed the final draft. All authors read and approved the manuscript.

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Objective: To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm. Approach: With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11 b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters, Dm , α, and β and three IVIM parameters Ddiff , Dperf , and f were estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25%, and 75% quantiles were extracted for each parameter from the regions-of-interest. Feature significance was calculated using the Boruta algorithm using Benjamin Hochberg False Discover Rate and Bonferroni correction for hypothesis testing. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers. Main Results: The 75% quantile, and median of Dm ; 75% quantile of f; mean, median, and skewness of β; kurtosis of Dperf ; and 75% quantile of Ddiff were the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers. Significance: Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.

Background: About 20%-40% of patients diagnosed with ductal carcinoma in situ (DCIS) by core needle biopsy (CNB) will develop invasive cancer at the time of excision. Improving the preoperative diagnosis of DCIS is important for surgical planning. Purpose: To establish an MRI-based radiomics nomogram for preoperatively evaluating the upstaging of DCIS patients and help with risk stratification. Study Type: Retrospective. Population: A total of 227 patients (50.5 AE 9.7 years; 67 upstaged DCIS) were divided into training (n = 109), internal (n = 47), and external (n = 71) validation cohort. Field Strength/Sequence: 1.5-T or 3-T, dynamic contrast-enhanced (DCE) imaging, and diffusion-weighted imaging (DWI). Assessment: DCIS lesions were manually segmented using ITK-SNAP software and 1304 radiomic features were extracted from DCE, DWI, and apparent diffusion coef-ficient (ADC) maps, respectively. A radscore was calculated by a random forest algo-rithm based on DCIS upstaging-related radiomic features, which selected by a coarse-to-fine method including interclass correlation coefficient, single-factor anal-ysis, and the least absolute shrinkage and selection operator (LASSO) method. Uni-variate and multivariate logistic regression was used to analyze the independent risk factors, including age, location, lesion size, estrogen receptor (ER) status, and other clinico-pathologic factors. Finally, Mann-Whitney U tests were performed to com-pare the differences in radscore between low/intermediate and high nuclear grade groups for pure DCIS patients. Statistical Tests: Student's t-tests or Mann-Whitney U tests, chi-square-tests, or Fisher's-tests, univariate and multivariate logistic regression analysis, calibration curve, Youden index, the area under the curve (AUC), Delong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) analyses. Results: Eight important radiomic features (two from ADC, three from DWI, and three from DCE) were selected for calculating radscore. Clinical model including age and ER was established with AUCs of 0.747 and 0.738 in the internal and external validation cohorts, respectively. A combined model integrating age, estrogen receptor (ER), and radscore were also constructed with AUCs of 0.887 and 0.881. Further subgroup analysis showed that pure DCIS patients with different nuclear grade have significant differences in radscore. Data Conclusion: Multisequence MRI radiomics may preoperatively evaluate the upstaging of DCIS and might provide personalized image-based clinical decision support. Evidence Level: 4. Technical Efficacy: Stage 2.

Background Benign and malignant diagnosis of nonpalpable breast imaging reporting and data system (BI-RADS) category 0 lesions on digital mammograms (DMs) is very important. We compared the diagnostic performance of non-contrast-enhanced magnetic resonance imaging (MRI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for them. We sought to evaluate BI-RADS category 0 lesions using 3 MRI sequences: short tau inversion recovery (STIR), STIR combined with high b value diffusion-weighted imaging (STIR-DWI), and DCE-MRI. Methods We retrospectively reviewed 114 breast DMs rated as nonpalpable BI-RADS category 0 lesions in 112 patients from January 2014 to June 2019. STIR, high b value DWI, and DCE-MRI were performed for all patients. Two breast radiologists read individual sequences (STIR, DWI, DCE-MRI) and pairs of sequences (STIR-DWI) to detect BI-RADS category 0 lesions in DMs. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance according to a best valuable comparator that combined MRI imaging, clinical, and pathological data. Results Among of 114 lesions (the median age of patients was 47 years; the median size of the lesion was 19 mm), 32 (48.5%) malignant lesions were missed by STIR, 9 (13.6%) malignant lesions were missed by STIR-DWI, and 3 (4.5%) malignant lesions were missed by DCE-MRI. The principal finding of our study was that STIR-DWI and DCE-MRI showed higher diagnostic accuracy than did STIR (P<0.01). STIR-DWI showed higher accuracy [area under the curve (AUC) =0.858; sensitivity =87.8%] for BI-RADS category 0 lesions in DMs than did STIR (AUC =0.754; sensitivity =51.5%), while the performance was comparable to that of DCE-MRI (AUC =0.884; sensitivity =95.5%). Conclusions Using pairs of sequences (STIR-DWI) is a non-contrast-enhanced MRI technique and had an equal diagnostic performance in distinguishing benign from malignant lesions among nonpalpable BI-RADS category 0 lesions to that of DCE-MRI. As a result, STIR-DWI as having the potential to improve the safety and efficacy in of breast cancer screening, especially in nonpalpable BI-RADS category 0 lesions at in DMs.