Insights into Imaging最新文献

{"title":"Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.","authors":"Ying Zhu, Yaru Wei, Zhongwei Chen, Xiang Li, Shiwei Zhang, Caiyun Wen, Guoquan Cao, Jiejie Zhou, Meihao Wang","doi":"10.1186/s13244-024-01795-5","DOIUrl":"10.1186/s13244-024-01795-5","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.</p><p><strong>Methods: </strong>A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed. Twelve radiomics models were constructed using Lasso-Logistic or Lasso-Cox to evaluate and four annotation methods, 2D detailed annotation along tumour boundaries (2D), 3D detailed annotation along tumour boundaries (3D), 2D bounding box (2D_BB), and 3D bounding box (3D_BB) on T2-weighted images, were compared. Radiomics models were used to establish combined models incorporating clinical risk factors. The DeLong test was performed to compare the performance of models using the receiver operating characteristic curves.</p><p><strong>Results: </strong>For radiomics models, the area under the curve values ranged from 0.627 (0.518-0.728) to 0.811 (0.705-0.917) in the internal validation cohort and from 0.619 (0.469-0.754) to 0.824 (0.689-0.918) in the external validation cohort. Most radiomics models based on four annotations did not differ significantly, except between the 3D and 3D_BB models for pLNM (p = 0.0188) in the internal validation cohort. For combined models, only the 2D model significantly differed from the 2D_BB (p = 0.0372) and 3D models (p = 0.0380) for pDFS.</p><p><strong>Conclusion: </strong>Radiomics and combined models constructed with 2D and bounding box annotations showed comparable performances to those with 3D and detailed annotations along tumour boundaries in rectal cancer characterisation and prognosis prediction.</p><p><strong>Critical relevance statement: </strong>For quantitative analysis of radiological images, the selection of 2D maximum tumour area or bounding box annotation is as representative and easy to operate as 3D whole tumour or detailed annotations along tumour boundaries.</p><p><strong>Key points: </strong>There is currently a lack of discussion on whether different annotation efforts in radiomics are predictively representative. No significant differences were observed in radiomics and combined models regardless of the annotations (2D, 3D, detailed, or bounding box). Prioritise selecting the more time and effort-saving 2D maximum area bounding box annotation.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"211"},"PeriodicalIF":4.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055496","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":"Publishing in open access journals.","authors":"Emilio Quaia, Chiara Zanon, Alberto Vieira, Christian Loewe, Luis Marti-Bonmatí","doi":"10.1186/s13244-024-01794-6","DOIUrl":"10.1186/s13244-024-01794-6","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"212"},"PeriodicalIF":4.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055500","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":"Digital breast tomosynthesis in breast cancer screening: an ethical perspective.","authors":"Simon Rosenqvist, Johan Brännmark, Magnus Dustler","doi":"10.1186/s13244-024-01790-w","DOIUrl":"10.1186/s13244-024-01790-w","url":null,"abstract":"<p><p>Although digital breast tomosynthesis has higher sensitivity than digital mammography and at least as high specificity, digital mammography remains the most common method for conducting mammographic screening. At the same time, mammography systems are now delivered \"DBT-ready\" and can be used for either digital mammography or digital breast tomosynthesis. In this paper, we ask whether it is ethically permissible to use such equipment for digital mammography, given its lower sensitivity. We argue it is not, and that clinics are ethically required to use their DBT-ready equipment to screen with digital breast tomosynthesis whenever this is practically possible. Our argument relies on a comparison between digital breast tomosynthesis and a hypothesized improvement in the image quality of digital mammography. CRITICAL RELEVANCE STATEMENT: Women may lose out on the benefits of screening with digital breast tomosynthesis when DBT-ready equipment is used to screen with digital mammography; we argue that this practice is ethically problematic. KEY POINTS: Digital breast tomosynthesis finds more cases of breast cancer than digital mammography. Mammography equipment can often be used to screen with both digital breast tomosynthesis and digital mammography. When they can, clinics are ethically required to use existing equipment to screen with digital breast tomosynthesis instead of digital mammography.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"213"},"PeriodicalIF":4.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055497","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 extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.","authors":"Adalgisa Guerra, Helen Wang, Matthew R Orton, Marianna Konidari, Nickolas K Papanikolaou, Dow Mu Koh, Helena Donato, Filipe Caseiro Alves","doi":"10.1186/s13244-024-01776-8","DOIUrl":"10.1186/s13244-024-01776-8","url":null,"abstract":"<p><p>The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.</p><p><strong>Critical relevant statement: </strong>The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.</p><p><strong>Protocol of systematic review registration: </strong>PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .</p><p><strong>Key points: </strong>Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"217"},"PeriodicalIF":4.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055499","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":"Decoding MRI-informed brain age using mutual information.","authors":"Jing Li, Linda Chiu Wa Lam, Hanna Lu","doi":"10.1186/s13244-024-01791-9","DOIUrl":"10.1186/s13244-024-01791-9","url":null,"abstract":"<p><strong>Objective: </strong>We aimed to develop a standardized method to investigate the relationship between estimated brain age and regional morphometric features, meeting the criteria for simplicity, generalization, and intuitive interpretability.</p><p><strong>Methods: </strong>We utilized T1-weighted magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience project (N = 609) and employed a support vector regression method to train a brain age model. The pre-trained brain age model was applied to the dataset of the brain development project (N = 547). Kraskov (KSG) estimator was used to compute the mutual information (MI) value between brain age and regional morphometric features, including gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF) volume, and cortical thickness (CT).</p><p><strong>Results: </strong>Among four types of brain features, GMV had the highest MI value (8.71), peaking in the pre-central gyrus (0.69). CSF volume was ranked second (7.76), with the highest MI value in the cingulate (0.87). CT was ranked third (6.22), with the highest MI value in superior temporal gyrus (0.53). WMV had the lowest MI value (4.59), with the insula showing the highest MI value (0.53). For brain parenchyma, the volume of the superior frontal gyrus exhibited the highest MI value (0.80).</p><p><strong>Conclusion: </strong>This is the first demonstration that MI value between estimated brain age and morphometric features may serve as a benchmark for assessing the regional contributions to estimated brain age. Our findings highlighted that both GMV and CSF are the key features that determined the estimated brain age, which may add value to existing computational models of brain age.</p><p><strong>Critical relevance statement: </strong>Mutual information (MI) analysis reveals gray matter volume (GMV) and cerebrospinal fluid (CSF) volume as pivotal in computing individuals' brain age.</p><p><strong>Key points: </strong>Mutual information (MI) interprets estimated brain age with morphometric features. Gray matter volume in the pre-central gyrus has the highest MI value for estimated brain age. Cerebrospinal fluid volume in the cingulate has the highest MI value. Regarding brain parenchymal volume, the superior frontal gyrus has the highest MI value. The value of mutual information underscores the key brain regions related to brain age.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"216"},"PeriodicalIF":4.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055494","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":"Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer.","authors":"Christian M Heidt, Jonas R Bohn, Róbert Stollmayer, Oyunbileg von Stackelberg, Stephan Rheinheimer, Farastuk Bozorgmehr, Karsten Senghas, Kai Schlamp, Oliver Weinheimer, Frederik L Giesel, Hans-Ulrich Kauczor, Claus Peter Heußel, Gudula Heußel","doi":"10.1186/s13244-024-01787-5","DOIUrl":"10.1186/s13244-024-01787-5","url":null,"abstract":"<p><strong>Objective: </strong>Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features.</p><p><strong>Methods: </strong>This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS).</p><p><strong>Results: </strong>Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04_(pfs)/0.005_(tr)), gradient short-run emphasis (p = 0.06_(pfs)/0.01_(tr)), and zone percentage (p = 0.02_(pfs)/0.01_(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01_(pfs)/0.04_(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival.</p><p><strong>Conclusion: </strong>Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early.</p><p><strong>Critical relevance statement: </strong>Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging.</p><p><strong>Key points: </strong>Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"218"},"PeriodicalIF":4.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055495","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":"Hepatic focal nodular hyperplasia during follow-up of patients after cyclophosphamide- or oxaliplatin-based chemotherapy: differentiation from liver metastasis.","authors":"Fan Yang, Wenjing Peng, Shuang Chen, Lijuan Wan, Rui Zhao, Xiangchun Liu, Feng Ye, Hongmei Zhang","doi":"10.1186/s13244-024-01793-7","DOIUrl":"10.1186/s13244-024-01793-7","url":null,"abstract":"<p><strong>Objectives: </strong>Newly detected hepatic nodules during follow-up of cancer survivors receiving chemotherapy may pose a diagnostic dilemma. We investigated a series of hepatic focal nodular hyperplasia (FNH) diagnosed by either typical MRI features and follow-up or pathology in cancer survivors.</p><p><strong>Methods: </strong>This retrospective study evaluated 38 patients with tumours who developed new hepatic FNH after cyclophosphamide-based (n = 19) and oxaliplatin-based (n = 19) chemotherapies. The main tumour types were breast cancer (n = 18) and colorectal cancer (n = 17). MRI findings, clinical features, and temporal evolution of all target hepatic lesions (n = 63) were reported. In addition, the two chemotherapy drug groups were compared.</p><p><strong>Results: </strong>The median interval between chemotherapy completion and FNH detection was 30.4 months (12.9, 49.4). Six patients underwent biopsy or surgery, while the remaining patients were diagnosed based on typical MRI features and long-term follow-up. Among the patients, 60.5% (23/38) presented with multiple nodules and 63 target lesions were detected. The median size of target lesions was 11.5 mm (8.4, 15.1). The median follow-up time was 32.5 months (21.2, 48.6), and 15 patients experienced changes in their lesions during the follow-up period (11 increased and 4 decreased). The cyclophosphamide-based treatment group had a younger population, a greater proportion of females, and a shorter time to discovery than the oxaliplatin-based chemotherapy group (all p ≤ 0.016).</p><p><strong>Conclusions: </strong>FNH may occur in cancer survivors after cyclophosphamide- or oxaliplatin-based chemotherapy. Considering a patient's treatment history and typical MRI findings can help avoid misdiagnosis and unnecessary invasive treatment.</p><p><strong>Clinical relevance statement: </strong>When cancer survivors develop new hepatic nodules during follow-up, clinicians should think of the possibility of focal nodular hyperplasia in addition to liver metastasis, especially if the cancer survivors were previously treated with cyclophosphamide or oxaliplatin.</p><p><strong>Key points: </strong>Cancer survivors, after chemotherapy, can develop hepatic focal nodular hyperplasia. Cyclophosphamide and oxaliplatin are two chemotherapeutic agents that predispose to focal nodular hyperplasia development. Focal nodular hyperplasia occurs at shorter intervals in patients treated with cyclophosphamide.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"215"},"PeriodicalIF":4.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055498","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":"CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy.","authors":"Yujia Xia, Jie Zhou, Xiaolei Xun, Jin Zhang, Ting Wei, Ruitian Gao, Bobby Reddy, Chao Liu, Geoffrey Kim, Zhangsheng Yu","doi":"10.1186/s13244-024-01784-8","DOIUrl":"10.1186/s13244-024-01784-8","url":null,"abstract":"<p><strong>Objectives: </strong>To develop a deep learning model combining CT scans and clinical information to predict overall survival in advanced hepatocellular carcinoma (HCC).</p><p><strong>Methods: </strong>This retrospective study included immunotherapy-treated advanced HCC patients from 52 multi-national in-house centers between 2018 and 2022. A multi-modal prognostic model using baseline and the first follow-up CT images and 7 clinical variables was proposed. A convolutional-recurrent neural network (CRNN) was developed to extract spatial-temporal information from automatically selected representative 2D CT slices to provide a radiological score, then fused with a Cox-based clinical score to provide the survival risk. The model's effectiveness was assessed using a time-dependent area under the receiver operating curve (AUC), and risk group stratification using the log-rank test. Prognostic performances of multi-modal inputs were compared to models of missing modality, and the size-based RECIST criteria.</p><p><strong>Results: </strong>Two-hundred seven patients (mean age, 61 years ± 12 [SD], 180 men) were included. The multi-modal CRNN model reached the AUC of 0.777 and 0.704 of 1-year overall survival predictions in the validation and test sets. The model achieved significant risk stratification in validation (hazard ratio [HR] = 3.330, p = 0.008), and test sets (HR = 2.024, p = 0.047) based on the median risk score of the training set. Models with missing modalities (the single-modal imaging-based model and the model incorporating only baseline scans) can still achieve favorable risk stratification performance (all p < 0.05, except for one, p = 0.053). Moreover, results proved the superiority of the deep learning-based model to the RECIST criteria.</p><p><strong>Conclusion: </strong>Deep learning analysis of CT scans and clinical data can offer significant prognostic insights for patients with advanced HCC.</p><p><strong>Critical relevance statement: </strong>The established model can help monitor patients' disease statuses and identify those with poor prognosis at the time of first follow-up, helping clinicians make informed treatment decisions, as well as early and timely interventions.</p><p><strong>Key points: </strong>An AI-based prognostic model was developed for advanced HCC using multi-national patients. The model extracts spatial-temporal information from CT scans and integrates it with clinical variables to prognosticate. The model demonstrated superior prognostic ability compared to the conventional size-based RECIST method.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"214"},"PeriodicalIF":4.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055493","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":"Dual-layer spectral-detector CT for detecting liver steatosis by using proton density fat fraction as reference.","authors":"Min Wang, Hongyu Chen, Yue Ma, Ruobing Bai, Sizhe Gao, Linlin Yang, Wenli Guo, Cong Zhang, Chengjun Kang, Yu Lan, Yanqiu Sun, Yonggao Zhang, Xigang Xiao, Yang Hou","doi":"10.1186/s13244-024-01716-6","DOIUrl":"10.1186/s13244-024-01716-6","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the diagnostic accuracy of liver dual-layer spectral-detector CT (SDCT) derived parameters of liver parenchyma for grading steatosis with reference to magnetic resonance imaging-based proton density fat fraction (MRI-PDFF).</p><p><strong>Methods: </strong>Altogether, 320 consecutive subjects who underwent MRI-PDFF and liver SDCT examinations were recruited and prospectively enrolled from four Chinese hospital centers. Participants were classified into normal (n = 152), mild steatosis (n = 110), and moderate/severe(mod/sev) steatosis (n = 58) groups based on MRI-PDFF. SDCT liver parameters were evaluated using conventional polychromatic CT images (CT_poly), virtual mono-energetic images at 40 keV (CT_40kev), the slope of the spectral attenuation curve (λ), the effective atomic number (Zeff), and liver to spleen attenuation ratio (L/S ratio). Linearity between SDCT liver parameters and MRI-PDFF was examined using Spearman correlation. Cutoff values for SDCT liver parameters in determining steatosis grades were identified using the area under the receiver-operating characteristic curve analyses.</p><p><strong>Results: </strong>SDCT liver parameters demonstrated a strong correlation with PDFF, particularly Zeff (r_s = -0.856; p < 0.001). Zeff achieved an area under the curve (AUC) of 0.930 for detecting the presence of steatosis with a sensitivity of 89.4%, a specificity of 82.4%, and an AUC of 0.983 for detecting mod/sev steatosis with a sensitivity of 93.1%, a specificity of 93.5%, the corresponding cutoff values were 7.12 and 6.94, respectively. Zeff also exhibited good diagnostic performance for liver steatosis grading in subgroups, independent of body mass index.</p><p><strong>Conclusion: </strong>SDCT liver parameters, particularly Zeff, exhibit excellent diagnostic accuracy for grading steatosis.</p><p><strong>Critical relevance statement: </strong>Dual-layer SDCT parameter, Zeff, as a more convenient and accurate imaging biomarker may serve as an alternative indicator for MRI-based proton density fat fraction, exploring the stage and prognosis of liver steatosis, and even metabolic risk assessment.</p><p><strong>Key points: </strong>Liver biopsy is the standard for grading liver steatosis, but is limited by its invasive nature. The diagnostic performance of liver steatosis using SDCT-Zeff outperforms conventional CT parameters. SDCT-Zeff accurately and noninvasively assessed the grade of liver steatosis.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"210"},"PeriodicalIF":4.1,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11327236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982242","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":"Trends and hotspots of energy-based imaging in thoracic disease: a bibliometric analysis.","authors":"Yufan Chen, Ting Wu, Yangtong Zhu, Jiawei Chen, Chen Gao, Linyu Wu","doi":"10.1186/s13244-024-01788-4","DOIUrl":"10.1186/s13244-024-01788-4","url":null,"abstract":"<p><strong>Objective: </strong>To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention.</p><p><strong>Methods: </strong>The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source's production over time, corresponding author's countries, and a treemap of authors' keywords.</p><p><strong>Results: </strong>A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the \"European Radiology\" (181 articles, h-index = 46), followed by the \"European Journal of Radiology\" (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest.</p><p><strong>Conclusion: </strong>Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future.</p><p><strong>Critical relevance statement: </strong>This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice.</p><p><strong>Key points: </strong>Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"209"},"PeriodicalIF":4.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982252","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}