Insights into Imaging最新文献

{"title":"ESHNR 2024 Book of Abstracts","authors":"","doi":"10.1186/s13244-024-01789-3","DOIUrl":"https://doi.org/10.1186/s13244-024-01789-3","url":null,"abstract":"<p><b>Open Access</b> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.</p>\u0000<p>Reprints and permissions</p><img alt=\"Check for updates. Verify currency and authenticity via CrossMark\" height=\"81\" loading=\"lazy\" src=\"data:image/svg+xml;base64,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","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"30 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of proliferative hepatocellular carcinoma using the SMARS score and implications for microwave ablation","authors":"Peng Zhou, Yan Bao, De-Hua Chang, Jun-Xiang Li, Tian-Zhi An, Ya-Ping Shen, Wen-Wu Cai, Lu Wen, Yu-Dong Xiao","doi":"10.1186/s13244-024-01792-8","DOIUrl":"https://doi.org/10.1186/s13244-024-01792-8","url":null,"abstract":"To compare therapeutic outcomes of predicted proliferative and nonproliferative hepatocellular carcinoma (HCC) after microwave ablation (MWA) using a previously developed imaging-based predictive model, the SMARS score. This multicenter retrospective study included consecutive 635 patients with unresectable HCC who underwent MWA between August 2013 and September 2020. Patients were stratified into predicted proliferative and nonproliferative phenotypes according to the SMARS score. Overall survival (OS) and recurrence-free survival (RFS) were compared between the predicted proliferative and nonproliferative HCCs before and after propensity score matching (PSM). OS and RFS were also compared between the two groups in subgroups of tumor size smaller than 30 mm and tumor size 30–50 mm. The SMARS score classified 127 and 508 patients into predicted proliferative and nonproliferative HCCs, respectively. The predicted proliferative HCCs exhibited worse RFS but equivalent OS when compared with nonproliferative HCCs before (p < 0.001 for RFS; p = 0.166 for OS) and after (p < 0.001 for RFS; p = 0.456 for OS) matching. Regarding subgroups of tumor size smaller than 30 mm (p = 0.098) and tumor size 30–50 mm (p = 0.680), the OSs were similar between the two groups. However, predicted proliferative HCCs had worse RFS compared to nonproliferative HCCs in the subgroup of tumor size 30–50 mm (p < 0.001), while the RFS did not differ in the subgroup of tumor size smaller than 30 mm (p = 0.141). Predicted proliferative HCCs have worse RFS than nonproliferative ones after MWA, especially in tumor size larger than 30 mm. However, the phenotype of the tumor may not affect the OS. Before performing microwave ablation for hepatocellular carcinoma, the tumor phenotype should be considered because it may affect the therapeutic outcome. ","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"39 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical but commonly neglected factors that affect contrast medium administration in CT.","authors":"Michael C McDermott, Joachim E Wildberger, Kyongtae T Bae","doi":"10.1186/s13244-024-01750-4","DOIUrl":"10.1186/s13244-024-01750-4","url":null,"abstract":"<p><strong>Objective: </strong>Past decades of research into contrast media injections and optimization thereof in radiology clinics have focused on scan acquisition parameters, patient-related factors, and contrast injection protocol variables. In this review, evidence is provided that a fourth bucket of crucial variables has been missed which account for previously unexplained phenomena and higher-than-expected variability in data. We propose how these critical factors should be considered and implemented in the contrast-medium administration protocols to optimize contrast enhancement.</p><p><strong>Methods: </strong>This article leverages a combination of methodologies for uncovering and quantifying confounding variables associated with or affecting the contrast-medium injection. Engineering benchtop equipment such as Coriolis flow meters, pressure transducers, and volumetric measurement devices are combined with small, targeted systematic evaluations querying operators, equipment, and the physics and fluid dynamics that make a seemingly simple task of injecting fluid into a patient a complex and non-linear endeavor.</p><p><strong>Results: </strong>Evidence is presented around seven key factors affecting the contrast-medium injection including a new way of selecting optimal IV catheters, degraded performance from longer tubing sets, variability associated with the mechanical injection system technology, common operator errors, fluids exchanging places stealthily based on gravity and density, wasted contrast media and inefficient saline flushes, as well as variability in the injected flow rate vs. theoretical expectations.</p><p><strong>Conclusion: </strong>There remain several critical, but not commonly known, sources of error associated with contrast-medium injections. Elimination of these hidden sources of error where possible can bring immediate benefits and help to drive standardized and optimized contrast-media injections.</p><p><strong>Critical relevance statement: </strong>This review brings to light the commonly neglected/unknown factors negatively impacting contrast-medium injections and provides recommendations that can result in patient benefits, quality improvements, sustainability increases, and financial benefits by enabling otherwise unachievable optimization.</p><p><strong>Key points: </strong>How IV contrast media is administered is a rarely considered source of CT imaging variability. IV catheter selection, tubing length, injection systems, and insufficient flushing can result in unintended variability. These findings can be immediately addressed to improve standardization in contrast-enhanced CT imaging.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"219"},"PeriodicalIF":4.1,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142080161","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":"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}