Clinical radiology最新文献

{"title":"MRI-based radiomics virtual biopsy for BCL6 in primary central nervous system lymphoma","authors":"J. Liu ,&nbsp;J. Tu ,&nbsp;L. Yao ,&nbsp;L. Peng ,&nbsp;R. Fang ,&nbsp;Y. Lu ,&nbsp;F. He ,&nbsp;J. Xiong ,&nbsp;Y. Li","doi":"10.1016/j.crad.2024.106746","DOIUrl":"10.1016/j.crad.2024.106746","url":null,"abstract":"<div><h3>Aim</h3><div>To establish a machine learning model based on a radiomic signature for predicting B-cell lymphoma 6 (BCL-6) rearrangement in primary central nervous system lymphoma (PCNSL).</div></div><div><h3>Materials and Methods</h3><div>Retrospective study on 102 PCNSL patients (31 with BCL-6 rearrangement positive, 71 with BCL-6 rearrangement negative) were randomly divided into the training and validation sets at a ratio of 7:3. Radiomics models based on contrast-enhanced T1-weighted imaging (CE-T1WI) and fluid-attenuated inversion recovery (FLAIR) in different regions, including VOI_{tumour core} and VOI_{peritumoural oedema.} Radiomics features were extracted and selected using LASSO regression, and radiomics score (rad-score) were calculated using the weighted coefficients. Four machine learning models (logistic regression, random forest, support vector machine, K-nearest neighbours) were developed and evaluated based on rad-score. The optimal radiomics model was integrated into the clinical or radiological factors to construct a predictive model through logistic regression analysis. A nomogram was constructed based on independent significant features for individualised prediction.</div></div><div><h3>Results</h3><div>All rad-scores based on CE-T1WI and FLAIR sequences were significantly associated with BCL6 rearrangement (<em>p</em> &lt; 0.05) in univariate regression analysis. The logistic regression machine learning model performed best with AUCs of 0.935 (training) and 0.923 (validation). Rad-scores from CE-T1WI tumour core and peritumoural oedema were independent significant predictors.</div></div><div><h3>Conclusion</h3><div>Radiomics signatures based on CE-T1WI and FLAIR sequences have significant value in distinguishing BCL6 rearrangement. The CE-T1WI radiomics model based on VOI_{tumour core} and VOI_{peritumoural oedema} are robust markers for identifying BCL6 rearrangement.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106746"},"PeriodicalIF":2.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The quandary of the indeterminate thyroid nodule: commentary on “comparison of British thyroid association and thyroid imaging reporting and data system (TIRADS) classifications and their impact on the radiological and surgical management of indeterminate thyroid nodules”","authors":"G. Madani","doi":"10.1016/j.crad.2024.106744","DOIUrl":"10.1016/j.crad.2024.106744","url":null,"abstract":"","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106744"},"PeriodicalIF":2.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative plaque characteristics and pericoronary fat attenuation index enhance risk prediction of unstable angina in nonobstructive lesions","authors":"D. Li ,&nbsp;H. Li ,&nbsp;Y. Wang ,&nbsp;T. Zhu","doi":"10.1016/j.crad.2024.106742","DOIUrl":"10.1016/j.crad.2024.106742","url":null,"abstract":"<div><h3>AIM</h3><div>The role of quantitative plaque characterization and pericoronary fat attenuation index (FAI) in nonobstructive lesions is uncertain. Hence, this study aimed to investigate artificial intelligence (AI)-based plaque characterization and pericoronary FAI in patients with nonobstructive lesions to enhance risk prediction of unstable angina.</div></div><div><h3>MATERIALS AND METHODS</h3><div>This study was conducted using the clinical data of 408 patients with cardiovascular disease diagnosed with angina pectoris. A coronary computed tomography angiography examination was performed, and quantitative plaque characteristics and pericoronary FAI were analyzed.</div></div><div><h3>Results</h3><div>Of the 408 patients with angina, 130 had nonobstructive lesions and 278 had obstructive ones. No significant difference in pericoronary FAI was observed between patients with nonobstructive and obstructive lesions. In patients with nonobstructive lesions, the plaque length and pericoronary FAI were significantly higher in patients with unstable angina than in those with stable angina. In patients with obstructive lesions, the plaque fibrolipid volume and percentage were significantly higher in patients with unstable angina than in those with stable angina, and the narrowest lumen area was significantly smaller. Left anterior descending peripheral (peri-LAD) FAI &gt; -83 HU or total plaque length &gt;20.17 mm were independent predictors of unstable angina in patients with nonobstructive lesions. In patients with obstructive lesions, peri-LAD FAI &gt; -77 HU, total lipid volume &gt;12.6 mm³, and narrowest lumen area ≤2.25 mm² were independent predictors of unstable angina.</div></div><div><h3>CONCLUSION</h3><div>Pericoronary FAI and total plaque length may be suitable imaging biomarkers for AI-based prediction of the occurrence of unstable angina in patients with nonobstructive lesions.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106742"},"PeriodicalIF":2.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The ‘Tree trunk and root’ model: key imaging findings may anatomically differentiate axial psoriatic arthritis and DISH from axial spondyloarthropathy","authors":"M. Hussein ,&nbsp;C. Giraudo ,&nbsp;D. McGonagle ,&nbsp;W.J. Rennie","doi":"10.1016/j.crad.2024.08.014","DOIUrl":"10.1016/j.crad.2024.08.014","url":null,"abstract":"<div><div>Variable axial skeleton inflammation and axial skeleton tissue remodelling with aberrant ligamentous soft-tissue ossification occurs across the axial spondyloarthritis (ax-SpA) axial psoriatic arthritis (ax-PsA) and the diffuse idiopathic skeletal hyperostosis (DISH) spectrum. In this article, we show how imaging has resulted in an enthesis-centric model for different disease pathology compartmentalisation or a ‘root and trunk’ model for pathological process development. Whilst ankylosing spondylitis is predominantly characterised by early entheseal bony anchorage-related osteitis (root inflammation) and DISH is characterised by ligamentous soft-tissue ossification, ax-PsA is more heterogenous. Whilst ax-PsA may share an identical osteitis pattern to ax-SpA, a substantial proportion of ax-PsA cases have a soft tissue or tree trunk pathology that manifests as back pain with lack of osteitis but prominent ligamentous trunk ossification at later stages. We illustrate this using different imaging modalities to create a base for imaging research to elucidate this pattern of pathology.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106673"},"PeriodicalIF":2.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peritumoral and intratumoral radiomics for predicting visceral pleural invasion in lung adenocarcinoma based on preoperative computed tomography (CT)","authors":"Y-Q. Zuo ,&nbsp;D. Gao ,&nbsp;J-J. Cui ,&nbsp;Y-L. Yin ,&nbsp;Z-H. Gao ,&nbsp;P-Y. Feng ,&nbsp;Z-J. Geng ,&nbsp;X. Yang","doi":"10.1016/j.crad.2024.10.010","DOIUrl":"10.1016/j.crad.2024.10.010","url":null,"abstract":"<div><h3>AIM</h3><div>To evaluate the prediction of peritumoral and intratumoral radiomics for visceral pleural invasion (VPI) in lung adenocarcinoma cancer (LAC) based on preoperative computed tomography (CT) radiomics.</div></div><div><h3>MATERIALS AND METHODS</h3><div>In total, 350 patients with LAC confirmed by surgery pathology were enrolled in The Second Hospital of Hebei Medical University, including 281 VPI negative patients and 69 VPI positive patients, were divided into the training cohort (n = 280) and validation cohort (n=70) at random with a ratio of 8:2. We extracted the radiomics features from the 3 region of interest (ROI), including gross tumor volume (GTV), the gross peritumoral tumor volume (GPTV) and the gross volume of the tumor rim (included the outer 4 mm of the tumor and 4mm of the tumor adjacent lung tissue on either side of the tumor contour boundary, GTR).The maximal redundancy minimal relevance (mMRM) algorithm and the least absolute shrinkage and selection operator (LASSO) was performed to reduce feature dimensionality and the radiomics score (Rad score) of the best radiomics model was combined with CT morphological characteristics with statistical significance in the univariable analysis to construct the combined model. The performance of the models was evaluated based on receiver operating characteristics (ROC) curve, calibration, and clinical usefulness. DeLong's test was used to assess differences in area under curve (AUC) between different models.</div></div><div><h3>RESULTS</h3><div>There were no statistically significant differences in patient's gender, age, and BMI between the VPI positive group and VPI negative group (all <em>p</em>＞0.05). There were statistically significant differences in the tumor maximum diameter, tumor CT image type, vacuole sign, and pleural indentation sign between the VPI positive group and VPI negative group (all <em>p</em> &lt; 0.05). The models of radiomics of GTV, GPTV, and GTR showed high predictive value in the training cohort (All AUC &gt; 0.75). Compared with GTV, GTR radiomics models, the GPTV radiomics model constructed via the logistic regression (LR) method exhibited better prediction performance with the AUCs of 0.819, 0.827; accuracy of 0.757,0.743; sensitivity of 0.800,0.786; specificity of 0.747,0.732 in the training and validation cohorts, respectively. The LR model of GPTV radiomics was defined as the optimal model for predicting VPI, since its excellent performance in both ROC, calibration curve and decision curve analysis (DCA).</div></div><div><h3>CONCLUSION</h3><div>Preoperative CT-based radiomics models can predict VPI in patients with LAC; the LR algorithm combined the GPTV radiomics was the optimal choice, demonstrating high sensitivity, specificity, accuracy and clinical usefulness.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106729"},"PeriodicalIF":2.1,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive ability of magnetic resonance imaging (MRI) for detecting prostate cancer and its clinical significance in MRI-targeted biopsy for prostate imaging reporting and data system (PI-RADS) ≥3 lesions","authors":"A. Erkan ,&nbsp;S.G. Gur Ozcan ,&nbsp;M. Erkan ,&nbsp;D. Barali ,&nbsp;A. Koc","doi":"10.1016/j.crad.2024.10.012","DOIUrl":"10.1016/j.crad.2024.10.012","url":null,"abstract":"<div><h3>AIM</h3><div>Identifying the index lesion in prostate cancer (PCa) is vital for its treatment. Therefore, various coefficients and parameters are used to improve the diagnostic accuracy of magnetic resonance imaging (MRI). This study aimed to analyze MRI data, utilized as a triage test before prostate biopsy, to identify independent risk factors affecting negative biopsy results in PCa and investigate the ability of these factors to predict clinically significant and insignificant PCa (csPCa and ciPCa, respectively).</div></div><div><h3>MATERIALS AND METHODS</h3><div>A retrospective analysis was conducted on data from 364 patients with a prostate imaging reporting and data system (PI-RADS) v2.1 score of 3 or higher, who underwent cognitive MRI-targeted biopsy (MRI-TB). Of the patients, 226 (62.1%) had benign lesions, 75 (20.6%) were diagnosed with ciPCa, and 63 (17.3%) with csPCa. The study assessed patients' demographic, biochemical, and radiologic characteristics, including apparent diffusion coefficient (ADC) and ADC coefficient of variation (ADC_CoV) values.</div></div><div><h3>RESULTS</h3><div>The multivariate analysis performed to differentiate PCa from benign pathologies revealed that only MRI parameters, specifically the presence of PI-RADS 4 and 5 lesions (odds ratio [OR]: 12, p &lt; 0.001 and OR: 73, p = 0.008, respectively), a lower ADC value (OR: 0.996, p = 0.041) and a higher ADC_CoV value (OR: 1.07, p = 0.003) were independent risk factors. No MRI findings had significant predictive power for csPCa, with total prostate-specific antigen (PSA) (OR: 1.17, p = 0.019) found to be the only independent risk factor.</div></div><div><h3>CONCLUSION</h3><div>The results of this study suggest that data obtained from MRI can predict PCa but not csPCa.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106731"},"PeriodicalIF":2.1,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"18Fluorodeoxyglucose positron emission tomography (18F-FDG PET)–derived tumoral and peritumoral radiomic parameters can predict pathological subtype and survival in esophageal carcinoma","authors":"K.A. Kucuker ,&nbsp;A. Aksu ,&nbsp;A. Alacacioglu ,&nbsp;B. Turgut","doi":"10.1016/j.crad.2024.10.011","DOIUrl":"10.1016/j.crad.2024.10.011","url":null,"abstract":"<div><h3>AIM</h3><div>The aim of this study is to investigate the importance of the quantitative parameters of the tumoral and peritumoral regions in prediction of pathological subtypes and 1-year survival in patients with esophageal carcinoma.</div></div><div><h3>MATERIALS AND METHODS</h3><div>A total of 103 patients with esophageal squamous cell carcinoma (SCC) and adenocarcinoma (AC) and in whom ¹⁸fluorodeoxyglucose positron emission tomography/computerized tomography (¹⁸F-FDG PET/CT) was performed were included in the study. One-year progression-free survival (PFS) and overall survival times of all patients were noted. Primary tumor and peritumoral area were drawn with manual segmentation on the ¹⁸F-FDG PET images. Seventy-three quantitative parameters were extracted from tumoral and peritumoral volumes of interest by using software. The differences of parameters of tumoral and peritumoral regions were determined statistically between pathological subtypes and between 1-year survivors and non-survivors.</div></div><div><h3>RESULTS</h3><div>Diagnostic models were created with the statistically significant parameters. The model that consists of a tumoral and a peritumoral parameter could classify the pathological subtypes in 61% of the patients correctly (area under the curve [AUC]: 0.706, 61.2% accuracy, 53.7% sensitivity, and 75% specificity). The model that was created with 2 tumoral parameters could classify the 1-year survival in 66% of the patients correctly (AUC: 0.695, 66% accuracy, 73.6% sensitivity, and 56.1% specificity). The model consisting of a tumoral and a peritumoral parameter detected 1-year PFS in 66% of the patients accurately (AUC: 0.687, 66% accuracy, 72.4% sensitivity, and 55.6% specificity).</div></div><div><h3>CONCLUSION</h3><div>The quantitative parameters obtained from tumoral and peritumoral regions can provide information about pathological subtypes and 1-year survival in esophageal carcinoma.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106730"},"PeriodicalIF":2.1,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"‘Is it time to standardise elective nephrostomies as day case procedures?’- a single-centre retrospective study","authors":"N. Murali ,&nbsp;L. Senathirajah ,&nbsp;M. Taylor ,&nbsp;J. Lynch ,&nbsp;K. Tan ,&nbsp;T. Ali","doi":"10.1016/j.crad.2024.10.009","DOIUrl":"10.1016/j.crad.2024.10.009","url":null,"abstract":"<div><h3>AIM</h3><div>Elective percutaneous nephrostomies are performed in stable patients commonly to create urinary diversion following obstruction. The main complications are bleeding and urosepsis. There is currently minimal evidence on complication rates following elective nephrostomies and no national guidelines regarding admission length. The aim of this study is to establish complication rates following elective nephrostomy insertion and determine safety as a potential day-case procedure.</div></div><div><h3>MATERIALS AND METHODS</h3><div>A retrospective study of patients undergoing elective nephrostomies within a 10-year period at our centre was included. Those with elective nephrostomies with additional ureteric or ileal conduit stents were also included. Complications were included if they occurred within 30 days of the nephrostomy.</div></div><div><h3>RESULTS</h3><div>239 patients underwent an elective nephrostomy in the 10-year period. The most common indication was cancer (91 patients). Overall complication rate was 8.39%. Three cases reported bleeding requiring transfusion and extended stay (1.26%). Seventeen cases needed IV antibiotics for presumed urosepsis (7.11%). There were no cases requiring embolisation, and no deaths were reported. There was no significant association between ureteric stenting and risk of complication.</div></div><div><h3>CONCLUSION</h3><div>Given the complication rate of elective nephrostomies remain relatively low, there remains a realistic possibility of standardising it as a day-case procedure. The use of IV antibiotics needs to be rationalised, and local protocols could be introduced to determine which patients are truly septic and require antibiotics. Those with a high risk of complications, such as those draining pus, may be admitted though the chance of this is likely to be low.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106728"},"PeriodicalIF":2.1,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based CT radiomics approach for predicting occult peritoneal metastasis in advanced gastric cancer preoperatively","authors":"Z.-N. Zhu ,&nbsp;Q.-X. Feng ,&nbsp;Q. Li,&nbsp;W.-Y. Xu,&nbsp;X.-S. Liu","doi":"10.1016/j.crad.2024.10.008","DOIUrl":"10.1016/j.crad.2024.10.008","url":null,"abstract":"<div><h3>Aim</h3><div>To develop a machine learning-based CT radiomics model to preoperatively diagnose occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.</div></div><div><h3>Materials and methods</h3><div>A total of 177 AGC patients were retrospectively analyzed. Four regions of interest (ROIs) along the largest area of tumor (core ROI) and corresponding tumor mesenteric fat space (peri ROI) were manually delineated on the arterial (A-core and A-peri) and venous phase (V-core and V-peri) of CT images. A total of 1316 radiomics features were extracted from each ROI. Then, ten machine learning classification algorithms were used to develop the model. An integrated radiomics nomogram was established to predict OPM individually.</div></div><div><h3>Results</h3><div>For the radiomics of tumor mesenteric fat space, the AUCs of A-peri in training and test sets were 0.881 and 0.800, respectively. And the AUCs of V-peri were 0.838 and 0.815, respectively. In terms of primary tumor’ s radiomics signature, the AUCs of A-core in training and test sets were 0.862 and 0.691, respectively. The AUCs of V-core were 0.831 and 0.620. Integrated radiomics model showed the highest AUC value when it compared to each single radiomics score in the training (0.943 vs 0.831–0.881) and test set (0.835 vs 0.620–0.815). Radiomics nomogram demonstrated good diagnostic accuracy with a C-index of 0.948.</div></div><div><h3>Conclusion</h3><div>Both the radiomics of tumor mesenteric fat space and primary tumor were associated with OPM. A CT radiomics nomogram had a relatively good predictive performance for detecting OPM in patients with AGC.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106727"},"PeriodicalIF":2.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic value of myocardial tissue characterization by cardiac magnetic resonance imaging using T1 mapping in nonischemic dilated cardiomyopathy: a systematic review and meta-analysis","authors":"B.S. Berdibekov,&nbsp;S.A. Alexandrova,&nbsp;N.I. Bulaeva,&nbsp;E.Z. Golukhova","doi":"10.1016/j.crad.2024.10.007","DOIUrl":"10.1016/j.crad.2024.10.007","url":null,"abstract":"<div><h3>Aim</h3><div>This study aimed to evaluate the prognostic value of T1 mapping techniques via cardiac magnetic resonance (CMR) in nonischemic dilated cardiomyopathy (NICM) patients.</div></div><div><h3>Materials and Methods</h3><div>PubMed and Google Scholar were searched for studies examining the prognostic value of myocardial tissue characterization via CMR imaging with T1 mapping in NICM. Major adverse cardiac events (MACE) included cardiac death, ventricular arrhythmia/sudden cardiac death (SCD) events, and heart failure events.</div></div><div><h3>Results</h3><div>Ten studies with a total of 3,384 patients (mean age 50.4 years; mean follow-up 28.0 months) were analyzed. The meta-analysis demonstrated that in patients with MACE, the mean extracellular volume (ECV) was greater than in those without MACE (MD: -5.40%; 95% CI: -7.91 to -2.90%; <em>p</em> &lt; 0.0001). Furthermore, in patients with MACE, the native T1 value was also greater than in those without MACE (MD: - 38.87 ms; 95% CI: -59.01 to -18.74 ms; <em>p</em> = 0.0002). A meta-analysis showed a significant relationship between ECV and the risk of MACE (HRunadjusted: 1.19 per 1% ECV; 95% CI: 1.10–1.28; <em>p</em> &lt; 0.001). After adjusting for baseline characteristics, higher ECV remained strongly associated with MACE risk (HRadjusted: 1.21 per 1% ECV; 95% CI: 1.11–1.31; <em>p</em> &lt; 0.001). Higher native T1 time was also significantly associated with MACE development (HRunadjusted: 1.09 per 10 ms T1 time; 95% CI:1.02–1.15; <em>p</em> = 0.007). After adjustments, the association remained significant (HR adjusted: 1.01 per 10 ms T1 time; 95% CI: 1.00–1.03; <em>p</em> = 0.02).</div></div><div><h3>Conclusions</h3><div>Meta-analysis demonstrates the risk of MACE being significantly associated with a higher mean ECV fraction and native T1 time, suggesting these indices as novel risk markers to identify high-risk NICM patients.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"80 ","pages":"Article 106726"},"PeriodicalIF":2.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}