Insights into Imaging最新文献

{"title":"Diagnostic performance of Sonazoid-enhanced CEUS in identifying definitive hepatocellular carcinoma in cirrhotic patients according to KLCA-NCC 2022 and APASL 2017 guidelines.","authors":"Zhe Huang, Rong-Hua Zhu, Shan-Shan Li, Hong-Chang Luo, Kai-Yan Li","doi":"10.1186/s13244-024-01838-x","DOIUrl":"10.1186/s13244-024-01838-x","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to assess the diagnostic performance of Sonazoid-contrast-enhanced ultrasound (CEUS) in identifying definitive HCC within hepatic nodules in cirrhotic patients, comparing the KLCA-NCC 2022 and APASL 2017 diagnostic guidelines.</p><p><strong>Materials and methods: </strong>This retrospective study analyzed cirrhotic patients who underwent Sonazoid-CEUS for liver lesion evaluation between October 2019 and October 2023. HCC diagnosis was based on the KLCA-NCC 2022 and APASL 2017 guidelines. Inter-reader agreement on CEUS imaging features and the diagnostic accuracy of the guidelines were evaluated. Sensitivity and specificity comparisons were made using McNemar's test.</p><p><strong>Results: </strong>Among 400 patients with 432 lesions, CEUS showed excellent inter-reader consistency in detecting arterial phase hyperenhancement and Kupffer defects. The KLCA-NCC 2022 criteria notably enhanced sensitivity to 96.2%, with specificity and accuracy of 93.8% and 95.8%, respectively. APASL 2017 achieved the highest sensitivity at 97.8%, although specificity dropped to 46.9%, resulting in an accuracy of 90.3%. The KLCA-NCC 2022 guidelines demonstrated significantly higher specificity than APASL 2017 (p < 0.001), while APASL 2017 exhibited the highest sensitivity at 97.8%. Notably, the KLCA-NCC 2022 guidelines also demonstrated an impressive positive predictive value of 98.9%.</p><p><strong>Conclusion: </strong>Sonazoid-enhanced CEUS, particularly when applied using the KLCA-NCC 2022 guidelines, is an effective diagnostic tool for HCC.</p><p><strong>Critical relevance statement: </strong>Perfluorobutane CEUS, particularly in accordance with the KLCA-NCC 2022 guidelines, emerges as a valuable adjunct for diagnosing HCC in cirrhotic patients. It demonstrates superior positive predictive value and specificity compared to APASL 2017, underscoring its potential as an effective diagnostic tool.</p><p><strong>Key points: </strong>Contrast-enhanced (CE)US using Sonazoid with KLCA-NCC 2022 guidelines is highly effective for HCC diagnosis. KLCA-NCC 2022 criteria showed high accuracy, 96.2% sensitivity, and 98.9% PPV. CEUS demonstrated excellent inter-reader consistency in detecting arterial phase hyperenhancement and Kupffer defects.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"263"},"PeriodicalIF":4.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142557753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of microvascular invasion in hepatocellular carcinoma with conventional ultrasound, Sonazoid-enhanced ultrasound, and biochemical indicator: a multicenter study.","authors":"Dan Lu, Li-Fan Wang, Hong Han, Lin-Lin Li, Wen-Tao Kong, Qian Zhou, Bo-Yang Zhou, Yi-Kang Sun, Hao-Hao Yin, Ming-Rui Zhu, Xin-Yuan Hu, Qing Lu, Han-Sheng Xia, Xi Wang, Chong-Ke Zhao, Jian-Hua Zhou, Hui-Xiong Xu","doi":"10.1186/s13244-024-01743-3","DOIUrl":"10.1186/s13244-024-01743-3","url":null,"abstract":"<p><strong>Purpose: </strong>To develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.</p><p><strong>Methods: </strong>From May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model's performance.</p><p><strong>Results: </strong>Logistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses.</p><p><strong>Conclusions: </strong>The combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm.</p><p><strong>Critical relevance statement: </strong>Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making.</p><p><strong>Key points: </strong>MVI status is important for patients with a single HCC ≤ 5 cm. The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction. The combined model has potential for preoperative prediction of MVI status.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"261"},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of image quality and lesion conspicuity between conventional and deep learning reconstruction in gadoxetic acid-enhanced liver MRI.","authors":"Jeong Hee Yoon, Jeong Eun Lee, So Hyun Park, Jin Young Park, Jae Hyun Kim, Jeong Min Lee","doi":"10.1186/s13244-024-01825-2","DOIUrl":"10.1186/s13244-024-01825-2","url":null,"abstract":"<p><strong>Objective: </strong>To compare the image quality and lesion conspicuity of conventional vs deep learning (DL)-based reconstructed three-dimensional T1-weighted images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI).</p><p><strong>Methods: </strong>This prospective study (NCT05182099) enrolled participants scheduled for gadoxetic acid-enhanced liver MRI due to suspected focal liver lesions (FLLs) who provided signed informed consent. A liver MRI was conducted using a 3-T scanner. T1-weighted images were reconstructed using both conventional and DL-based (AIR^TM Recon DL 3D) reconstruction algorithms. Three radiologists independently reviewed the image quality and lesion conspicuity on a 5-point scale.</p><p><strong>Results: </strong>Fifty participants (male = 36, mean age 62 ± 11 years) were included for image analysis. The DL-based reconstruction showed significantly higher image quality than conventional images in all phases (3.71-4.40 vs 3.37-3.99, p < 0.001 for all), as well as significantly less noise and ringing artifacts than conventional images (p < 0.05 for all), while also showing significantly altered image texture (p < 0.001 for all). Lesion conspicuity was significantly higher in DL-reconstructed images than in conventional images in the arterial phase (2.15 [95% confidence interval: 1.78, 2.52] vs 2.03 [1.65, 2.40], p = 0.036), but no significant difference was observed in the portal venous phase and hepatobiliary phase (p > 0.05 for all). There was no significant difference in the figure-of-merit (0.728 in DL vs 0.709 in conventional image, p = 0.474).</p><p><strong>Conclusion: </strong>DL reconstruction provided higher-quality three-dimensional T1-weighted imaging than conventional reconstruction in gadoxetic acid-enhanced liver MRI.</p><p><strong>Critical relevance statement: </strong>DL reconstruction of 3D T1-weighted images improves image quality and arterial phase lesion conspicuity in gadoxetic acid-enhanced liver MRI compared to conventional reconstruction.</p><p><strong>Key points: </strong>DL reconstruction is feasible for 3D T1-weighted images across different spatial resolutions and phases. DL reconstruction showed superior image quality with reduced noise and ringing artifacts. Hepatic anatomic structures were more conspicuous on DL-reconstructed images.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"257"},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The prognostic value of radiogenomics using CT in patients with lung cancer: a systematic review.","authors":"Yixiao Jiang, Chuan Gao, Yilin Shao, Xinjing Lou, Meiqi Hua, Jiangnan Lin, Linyu Wu, Chen Gao","doi":"10.1186/s13244-024-01831-4","DOIUrl":"10.1186/s13244-024-01831-4","url":null,"abstract":"<p><p>This systematic review aimed to evaluate the effectiveness of combining radiomic and genomic models in predicting the long-term prognosis of patients with lung cancer and to contribute to the further exploration of radiomics. This study retrieved comprehensive literature from multiple databases, including radiomics and genomics, to study the prognosis of lung cancer. The model construction consisted of the radiomic and genomic methods. A comprehensive bias assessment was conducted, including risk assessment and model performance indicators. Ten studies between 2016 and 2023 were analyzed. Studies were mostly retrospective. Patient cohorts varied in size and characteristics, with the number of patients ranging from 79 to 315. The construction of the model involves various radiomic and genotic datasets, and most models show promising prediction performance with the area under the receiver operating characteristic curve (AUC) values ranging from 0.64 to 0.94 and the concordance index (C-index) values from 0.28 to 0.80. The combination model typically outperforms the single method model, indicating higher prediction accuracy and the highest AUC was 0.99. Combining radiomics and genomics in the prognostic model of lung cancer may improve the predictive performance. However, further research on standardized data and larger cohorts is needed to validate and integrate these findings into clinical practice. CRITICAL RELEVANCE STATEMENT: The combination of radiomics and genomics in the prognostic model of lung cancer improved prediction accuracy in most included studies. KEY POINTS: The combination of radiomics and genomics can improve model performance in most studies. The results of establishing prognosis models by different methods are discussed. The combination of radiomics and genomics may be helpful to provide better treatment for patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"259"},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large vessel vasculitis evaluation by CTA: impact of deep-learning reconstruction and \"dark blood\" technique.","authors":"Ning Ding, Xi-Ao Yang, Min Xu, Yun Wang, Zhengyu Jin, Yining Wang, Huadan Xue, Lingyan Kong, Zhiwei Wang, Daming Zhang","doi":"10.1186/s13244-024-01843-0","DOIUrl":"10.1186/s13244-024-01843-0","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the performance of the \"dark blood\" (DB) technique, deep-learning reconstruction (DLR), and their combination on aortic images for large-vessel vasculitis (LVV) patients.</p><p><strong>Materials and methods: </strong>Fifty patients diagnosed with LVV scheduled for aortic computed tomography angiography (CTA) were prospectively recruited in a single center. Arterial and delayed-phase images of the aorta were reconstructed using the hybrid iterative reconstruction (HIR) and DLR algorithms. HIR or DLR DB image sets were generated using corresponding arterial and delayed-phase image sets based on a \"contrast-enhancement-boost\" technique. Quantitative parameters of aortic wall image quality were evaluated.</p><p><strong>Results: </strong>Compared to the arterial phase image sets, decreased image noise and increased signal-noise-ratio (SNR) and CNR_outer (all p < 0.05) were obtained for the DB image sets. Compared with delayed-phase image sets, dark-blood image sets combined with the DLR algorithm revealed equivalent noise (p > 0.99) and increased SNR (p < 0.001), CNR_outer (p = 0.006), and CNR_inner (p < 0.001). For overall image quality, the scores of DB image sets were significantly higher than those of delayed-phase image sets (all p < 0.001). Image sets obtained using the DLR algorithm received significantly better qualitative scores (all p < 0.05) in all three phases. The image quality improvement caused by the DLR algorithm was most prominent for the DB phase image sets.</p><p><strong>Conclusion: </strong>DB CTA improves image quality and provides better visualization of the aorta for the LVV aorta vessel wall. The DB technique reconstructed by the DLR algorithm achieved the best overall performance compared with the other image sequences.</p><p><strong>Critical relevance statement: </strong>Deep-learning-based \"dark blood\" images improve vessel wall image wall quality and boundary visualization.</p><p><strong>Key points: </strong>Dark blood CTA improves image quality and provides better aortic wall visualization. Deep-learning CTA presented higher quality and subjective scores compared to HIR. Combination of dark blood and deep-learning reconstruction obtained the best overall performance.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"260"},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Software-assisted structured reporting and semi-automated TNM classification for NSCLC staging in a multicenter proof of concept study.","authors":"Maurice M Heimer, Yevgeniy Dikhtyar, Boj F Hoppe, Felix L Herr, Anna Theresa Stüber, Tanja Burkard, Emma Zöller, Matthias P Fabritius, Lena Unterrainer, Lisa Adams, Annette Thurner, David Kaufmann, Timo Trzaska, Markus Kopp, Okka Hamer, Katharina Maurer, Inka Ristow, Matthias S May, Amanda Tufman, Judith Spiro, Matthias Brendel, Michael Ingrisch, Jens Ricke, Clemens C Cyran","doi":"10.1186/s13244-024-01836-z","DOIUrl":"10.1186/s13244-024-01836-z","url":null,"abstract":"<p><strong>Objectives: </strong>In this multi-center study, we proposed a structured reporting (SR) framework for non-small cell lung cancer (NSCLC) and developed a software-assisted tool to automatically translate image-based findings and annotations into TNM classifications. The aim of this study was to validate the software-assisted SR tool for NSCLC, assess its potential clinical impact in a proof-of-concept study, and evaluate current reporting standards in participating institutions.</p><p><strong>Methods: </strong>A framework for SR and staging of NSCLC was developed in a multi-center collaboration. SR annotations and descriptions were used to generate semi-automated TNM classification. The SR and TNM classification tools were evaluated by nine radiologists on n = 20 representative [18F]FDG PET/CT studies and compared to the free text reporting (FTR) strategy. Results were compared to a multidisciplinary team reference using a generalized linear mixed model (GLMM). Additionally, participants were surveyed on their experience with SR and TNM classification.</p><p><strong>Results: </strong>Overall, GLMM analysis revealed that readers using SR were 1.707 (CI: 1.137-2.585) times more likely to correctly classify TNM status compared to FTR strategy (p = 0.01) resulting in increased overall TNM correctness in 71.9% (128/178) of cases compared to 62.8% (113/180) FTR. The primary source of variation in classification accuracy was explained by case complexity. Participants rated the potential impact of SR and semi-automated TNM classification as positive across all categories with improved scores after template validation.</p><p><strong>Conclusion: </strong>This multi-center study yielded an effective software-assisted SR framework for NSCLC. The SR and semi-automated classification tool improved TNM classification and were perceived as valuable.</p><p><strong>Critical relevance statement: </strong>Software-assisted SR provides robust input for semi-automated rule-based TNM classification in non-small-cell lung carcinoma (NSCLC), improves TNM correctness compared to FTR, and was perceived as valuable by radiology physicians.</p><p><strong>Key points: </strong>SR and TNM classification are underutilized across participating centers for NSCLC staging. Software-assisted SR has emerged as a promising strategy for oncologic assessment. Software-assisted SR facilitates semi-automated TNM classification with improved staging accuracy compared to free-text reports in NSCLC.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"258"},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study.","authors":"Zongjie Wei, Xuesong Bai, Yingjie Xv, Shao-Hao Chen, Siwen Yin, Yang Li, Fajin Lv, Mingzhao Xiao, Yongpeng Xie","doi":"10.1186/s13244-024-01840-3","DOIUrl":"10.1186/s13244-024-01840-3","url":null,"abstract":"<p><strong>Objective: </strong>To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation.</p><p><strong>Methods: </strong>In this retrospective study, 207 patients with pathologically confirmed BCa were enrolled and divided into the training set (n = 154) and test set (n = 53). Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in the training set. Five radiomics-based ML models, namely logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost) and random forest (RF), were developed. The predictive performance of established ML models was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley additive explanation (SHAP) was used to analyze the interpretability of ML models.</p><p><strong>Results: </strong>A total of 1218 radiomics features were extracted from the nephrographic phase CT images, and 11 features were filtered for constructing ML models. In the test set, the AUCs of LR, SVM, KNN, XGBoost, and RF were 0.803, 0.709, 0.679, 0.794, and 0.815, with corresponding accuracies of 71.7%, 69.8%, 60.4%, 75.5%, and 75.5%, respectively. RF was identified as the optimal classifier. SHAP analysis showed that texture features (gray level size zone matrix and gray level co-occurrence matrix) were significant predictors of HER2 status.</p><p><strong>Conclusions: </strong>The radiomics-based interpretable ML model provides a noninvasive tool to predict the HER2 status of BCa with satisfactory discriminatory performance.</p><p><strong>Critical relevance statement: </strong>An interpretable radiomics-based machine learning model can preoperatively predict HER2 status in bladder cancer, potentially aiding in the clinical decision-making process.</p><p><strong>Key points: </strong>The CT radiomics model could identify HER2 status in bladder cancer. The random forest model showed a more robust and accurate performance. The model demonstrated favorable interpretability through SHAP method.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"262"},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic double inversion recovery imaging for rectal cancer T staging evaluation: imaging quality and added value to T2-weighted imaging.","authors":"Zi Wang, Zhuozhi Dai, Xinyi Zhou, Jiankun Dai, Yuxi Ge, Shudong Hu","doi":"10.1186/s13244-024-01796-4","DOIUrl":"https://doi.org/10.1186/s13244-024-01796-4","url":null,"abstract":"<p><strong>Objective: </strong>To assess the image quality of synthetic double inversion recovery (SyDIR) imaging and enhance the value of T2-weighted imaging (T2WI) in evaluating T stage for rectal cancer patients.</p><p><strong>Methods: </strong>A total of 112 pathologically confirmed rectal cancer patients were retrospectively selected after undergoing MRI, including synthetic MRI. The image quality of T2WI and SyDIR imaging was compared based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall picture quality, presence of motion artifacts, lesion edge sharpness, and conspicuity. The concordance between MRI and pathological staging results, using T2WI alone and the combination of T2WI and SyDIR for junior and senior radiologists, was assessed using the Kappa test. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic efficacy of extramural infiltration in rectal cancer patients.</p><p><strong>Results: </strong>No significant differences in imaging quality were observed between conventional T2WI and SyDIR (p = 0.07-0.53). The combination of T2WI and SyDIR notably improved the staging concordance between MRI and pathology for both junior (kappa value from 0.547 to 0.780) and senior radiologists (kappa value from 0.738 to 0.834). In addition, the integration of T2WI and SyDIR increased the AUC for diagnosing extramural infiltration for both junior (from 0.842 to 0.918) and senior radiologists (from 0.917 to 0.938).</p><p><strong>Conclusion: </strong>The combination of T2WI and SyDIR increased the consistency of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration, which would benefit treatment selection.</p><p><strong>Critical relevance statement: </strong>SyDIR sequence provides additional diagnostic value for T2WI in the T staging of rectal cancer, improving the agreement of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration.</p><p><strong>Key points: </strong>Synthetic double inversion recovery (SyDIR) and T2WI have comparable image quality. SyDIR provides rectal cancer anatomical features for extramural infiltration detections. The combination of T2WI and SyDIR improves the accuracy of T staging in rectal cancer.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"256"},"PeriodicalIF":4.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of MRI in radiotherapy planning: a narrative review \"from head to toe\".","authors":"Simona De Pietro, Giulia Di Martino, Mara Caroprese, Angela Barillaro, Sirio Cocozza, Roberto Pacelli, Renato Cuocolo, Lorenzo Ugga, Francesco Briganti, Arturo Brunetti, Manuel Conson, Andrea Elefante","doi":"10.1186/s13244-024-01799-1","DOIUrl":"https://doi.org/10.1186/s13244-024-01799-1","url":null,"abstract":"<p><p>Over the last few years, radiation therapy (RT) techniques have evolved very rapidly, with the aim of conforming high-dose volume tightly to a target. Although to date CT is still considered the imaging modality for target delineation, it has some known limited capabilities in properly identifying pathologic processes occurring, for instance, in soft tissues. This limitation, along with other advantages such as dose reduction, can be overcome using magnetic resonance imaging (MRI), which is increasingly being recognized as a useful tool in RT clinical practice. This review has a two-fold aim of providing a basic introduction to the physics of MRI in a narrative way and illustrating the current knowledge on its application \"from head to toe\" (i.e., different body sites), in order to highlight the numerous advantages in using MRI to ensure the best therapeutic response. We provided a basic introduction for residents and non-radiologist on the physics of MR and reported evidence of the advantages and future improvements of MRI in planning a tailored radiotherapy treatment \"from head to toe\". CRITICAL RELEVANCE STATEMENT: This review aims to help understand how MRI has become indispensable, not only to better characterize and evaluate lesions, but also to predict the evolution of the disease and, consequently, to ensure the best therapeutic response. KEY POINTS: MRI is increasingly gaining interest and applications in RT planning. MRI provides high soft tissue contrast resolution and accurate delineation of the target volume. MRI will increasingly become indispensable for characterizing and evaluating lesions, and to predict the evolution of disease.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"255"},"PeriodicalIF":4.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11499544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reproducibility of ultrasound-derived fat fraction in measuring hepatic steatosis.","authors":"Danlei Song, Pingping Wang, Jiahao Han, Huihui Chen, Ruixia Gao, Ling Li, Jia Li","doi":"10.1186/s13244-024-01834-1","DOIUrl":"10.1186/s13244-024-01834-1","url":null,"abstract":"<p><strong>Purpose: </strong>Steatotic liver disease (SLD) has become the most common cause of chronic liver disease. Nevertheless, the non-invasive quantitative diagnosis of steatosis is still lacking in clinical practice. This study aimed to evaluate the reproducibility of the new parameter for steatosis quantification named ultrasound-derived fat fraction (UDFF).</p><p><strong>Materials and methods: </strong>The UDFF values were independently executed by two operators in two periods. In the process, repeated measurements of the same patient were performed by the same operator under different conditions (liver segments, respiration, positions, and dietary). Finally, the results of some subjects (28) were compared with the MRI-derived proton density fat fraction (PDFF). The concordance analysis was mainly achieved by the intraclass correlation coefficient (ICC) and Bland-Altman.</p><p><strong>Results: </strong>One hundred-five participants were included in the study. UDFF had good reliability in measuring the adult liver (ICC_{intra-observer} = 0.96, ICC_{inter-observer} = 0.94). Meanwhile, the ICC of the two operators increased over time. The variable measurement states did not influence the UDFF values on the surface, but they affected the coefficient of variation (Cov) of the results. Segment 8 (S8), end-expiratory, supine, and fasting images had the most minor variability. On the other hand, the UDFF value of S8 displayed satisfied consistency with PDFF (mean difference, -0.24 ± 1.44), and the results of both S5 (mean difference: -0.56 ± 3.95) and S8 (mean difference: 0.73 ± 1.87) agreed well with the whole-liver PDFF.</p><p><strong>Conclusion: </strong>UDFF measurements had good reproducibility. Furthermore, the state of S8, end-expiration, supine, and fasting might be the more stable measurement approach.</p><p><strong>Critical relevance statement: </strong>UDFF is the quantitative ultrasound parameter of hepatic steatosis and has good reproducibility. It can show more robust performance under specific measurement conditions (S8, end-expiratory, supine, and fasting).</p><p><strong>Trial registration: </strong>The research protocol was registered at the Chinese Clinical Trial Registry on October 9, 2023 ( http://www.chictr.org.cn/ ). The registration number is ChiCTR 2300076457.</p><p><strong>Key points: </strong>There is a lack of non-invasive quantitative measurement options for hepatic steatosis. UDFF demonstrated excellent reproducibility in measuring hepatic steatosis. S8, end-expiratory, supine, and fasting may be the more stable measuring condition. Training could improve the operators' measurement stability. Variable measurement state affects the repeatability of the UDFF values (Cov).</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"254"},"PeriodicalIF":4.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11496408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}