Background In patients with cystic fibrosis (CF), pulmonary structures with high MRI T2 signal intensity relate to inflammatory changes in the lung and bronchi. These areas of pathologic abnormalities can serve as imaging biomarkers. The feasibility of automated quantification is unknown. Purpose To quantify the MRI T2 high-signal-intensity lung volume and T2-weighted volume-intensity product (VIP) by using a black-blood T2-weighted radial fast spin-echo sequence in participants with CF. Materials and Methods Healthy individuals and study participants with CF were prospectively enrolled between January 2017 and November 2017. All participants underwent a lung MRI protocol including T2-weighted radial fast spin-echo sequence. Participants with CF also underwent pulmonary function tests the same day. Participants with CF exacerbation underwent repeat MRI after their treatment with antibiotics. Two observers supervised automated quantification of T2-weighted high-signal-intensity volume (HSV) and T2-weighted VIP independently, and the average score was chosen as consensus. Statistical analysis used the Mann-Whitney test for comparison of medians, correlations used the Spearman test, comparison of paired medians used the Wilcoxon signed rank test, and reproducibility was evaluated by using intraclass correlation coefficient. Results In 10 healthy study participants (median age, 21 years [age range, 18-27 years]; six men) and 12 participants with CF (median age, 18 years [age range, 9-40 years]; eight men), T2-weighted HSV was equal to 0% and 4.1% (range, 0.1%-17%), respectively, and T2-weighted VIP was equal to 0 msec and 303 msec (range, 39-1012 msec), respectively ( < .001). In participants with CF, T2-weighted HSV or T2-weighted VIP were associated with forced expiratory volume in 1 second percentage predicted (ρ = -0.88 and ρ = -0.94, respectively; < .001). In six participants with CF exacerbation and follow-up after treatment, a decrease in both T2-weighted HSV and T2-weighted VIP was observed ( = .03). The intra- and interobserver reproducibility of MRI were good (intraclass correlation coefficients, >0.99 and >0.99, respectively). Conclusion In patients with cystic fibrosis (CF), automated quantification of lung MRI high-signal-intensity volume was reproducible and correlated with pulmonary function testing severity, and it improved after treatment for CF exacerbation. © RSNA, 2019 See also the editorial by Revel and Chassagnon in this issue.
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http://dx.doi.org/10.1148/radiol.2019190797 | DOI Listing |
Acad Radiol
January 2025
Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, PR China (J.H.L.); Department of Social medicine, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China (J.H.L.); Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, School of Public Health, Soochow University, Suzhou, Jiangsu 215123, PR China (J.H.L.).
Rationale And Objectives: To systematically review the diagnostic efficacy of abbreviated magnetic resonance imaging sequence (AMRI) screening for hepatocellular carcinoma (HCC).
Materials And Methods: Medline (via PubMed), EMbase, The Cochrane Library, Web of Science, CNKI, WanFang Data, and VIP databases were electronically searched to collect studies on the diagnostic efficacy of AMRI screening for HCC from inception to August 10th, 2024. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies using the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2), then, the meta-analysis with a bivariate mixed-effects regression model was performed by using Stata 14.
Abdom Radiol (NY)
October 2024
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Purpose: To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H).
Methods: A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3.
World J Clin Cases
September 2024
Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100700, China.
Background: Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC.
Aim: To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.
Cancer Imaging
September 2024
Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, Guangdong, 511400, China.
Background: This study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences.
Methods: This study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1.
Eur Radiol
August 2024
Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Objectives: This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions.
Methods: This study included 1711 adult patients (875 benign and 836 malignant) with sinonasal tumors or tumor-like lesions from three institutions. Patients from institution 1 (n = 1367) constituted both the training and validation cohorts, while those from institution 2 and 3 (n = 158/186) made up the test cohorts.
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