Abdominal Radiology最新文献

{"title":"The influence of fasting on abdominal ultrasound image quality - a randomized controlled trial.","authors":"Maximilian Schreiner, Hendrik Thien, Trixi Braasch, Ildiko Hoffmann, Ulrich Wesemann, Daniel Merkel, Michael Ludwig","doi":"10.1007/s00261-025-04947-3","DOIUrl":"https://doi.org/10.1007/s00261-025-04947-3","url":null,"abstract":"<p><strong>Background: </strong>Many patients are instructed to fast before abdominal ultrasound examinations in order to obtain better image quality. However, the few available studies on this topic have so far failed to demonstrate an advantage of several hours of fasting. Prolonged fasting can lead to patient distress, hypoglycemia (e.g. in insulin-treated patients), and malnutrition. Therefore, unnecessary fasting should be avoided.</p><p><strong>Methods: </strong>For this prospective investigator-blinded study, we recruited 250 inpatients who were registered for abdominal ultrasonography in our hospital and randomized them into one of three groups: fasting for several hours, regular breakfast, or a liquid oral nutritional supplement. 215 patients could be evaluated. The sonographies were performed consecutively by inexperienced and experienced examiners who independently rated the image quality of seven anatomical structures (gallbladder, common hepatic duct [i.e., proximal part of the common duct], common bile duct [i.e., distal part of the common duct], pancreatic head, pancreatic body, pancreatic tail, and origin of the superior mesenteric artery) on a Likert scale from 1 to 5. The mean of these scores was calculated as a parameter for the diagnostic quality of the abdominal ultrasound examination.</p><p><strong>Results: </strong>No significant difference was found for sonographic image quality after eating breakfast vs. fasting in both experienced and inexperienced examiners. In inexperienced examiners, image quality was rated slightly better after intake of liquid nutritional supplements compared to a normal breakfast (p = 0.023) or fasting (p = 0.047). However, the effect size was small.</p><p><strong>Conclusion: </strong>A general recommendation for several hours of fasting before abdominal sonography is not necessary.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI radiomics prediction modelling for pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis.","authors":"Jason Rai, Dinh V C Mai, Ioanna Drami, Edward T Pring, Laura E Gould, Phillip F C Lung, Thomas Glover, Joshua D Shur, Brandon Whitcher, Thanos Athanasiou, John T Jenkins","doi":"10.1007/s00261-025-04953-5","DOIUrl":"https://doi.org/10.1007/s00261-025-04953-5","url":null,"abstract":"<p><strong>Purpose: </strong>Predicting response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is challenging. Organ preservation strategies can be offered to patients with complete clinical response. We aim to evaluate MRI-derived radiomics models in predicting complete pathological response (pCR).</p><p><strong>Methods: </strong>Search included MEDLINE, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR) for studies published before 1st February 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess quality of included study. The research protocol was registered in PROSPERO (CRD42024512865). We calculated pooled area under the receiver operating characteristic curve (AUC) using a random-effects model. To compare AUC between subgroups the Hanley & McNeil test was performed.</p><p><strong>Results: </strong>Forty-four eligible studies (12,714 patients) were identified for inclusion in the systematic review. We selected thirty-five studies including 10,543 patients for meta-analysis. The pooled AUC for MRI radiomics predicted pCR in LARC was 0.87 (95% CI 0.84-0.89). In the subgroup analysis 3 T MRI field intensity had higher pooled AUC 0.9 (95% CI 0.87-0.94) than 1.5 T pooled AUC 0.82 (95% CI 0.80-0.83) p < 0.001. Asian ethnicity had higher pooled AUC 0.9 (95% CI 0.87-0.93) than non-Asian pooled AUC 0.8 (95% CI 0.75-0.84) p < 0.001.</p><p><strong>Conclusion: </strong>We have demonstrated that 3 T MRI field intensity provides a superior predictive performance. The role of ethnicity on radiomics features needs to be explored in future studies. Further research in the field of MRI radiomics is important as accurate prediction for pCR can lead to organ preservation strategy in LARC.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a CT-based nomogram to preoperative prediction of pancreatic neuroendocrine tumors (pNETs) grade.","authors":"Liangqi Wang, Xiangtian Zhao, Wenxia Zhu, Yuan Ji, Mengsu Zeng, Mingliang Wang","doi":"10.1007/s00261-025-04959-z","DOIUrl":"https://doi.org/10.1007/s00261-025-04959-z","url":null,"abstract":"<p><strong>Background/purpose: </strong>It is challenging to determine the pancreatic neuroendocrine tumors (pNETs) malignancy grade noninvasively. We aim to establish a CT - based diagnostic nomogram to predict the tumor grade of pNETs.</p><p><strong>Methods: </strong>The patients with pathologically confirmed pNETs were recruited in two centers between January 2009 and November 2020. PNETs were subdivided into three grades according to the 2017 World Health Organization classification: low-grade G1 NETs, intermediate-grade G2 NETs, and high-grade G3 NETs. The features on the CT images were carefully evaluated. To build the nomogram, multivariable logistic regression analysis was performed on the imaging features selected by LASSO to generate a combined indicator for estimating the tumor grade.</p><p><strong>Results: </strong>A total of 162 pNETs (training set n = 114, internal validation set n = 21, external validation set, n = 48) were admitted, including 73 (45.1%) G1 and 89 (54.9%) G2/3. A nomogram comprising the tumor margin, tumor size, neuroendocrine symptoms and the enhanced ratio on portal vein phase images of tumor was established to predict the malignancy grade of pNETs. The mean AUC for the nomogram was 0.848 (95% CI, 0.918-0.953). Application of the developed nomogram in the internal validation dataset still yielded good discrimination (AUC, 0.835; 95% CI, 0.915-0.954). The externally validated nomogram yielded a slightly lower AUC of 0.770 (95% CI, 0.776-0.789).</p><p><strong>Conclusions: </strong>The nomogram model demonstrated good performance in preoperatively predicting the malignancy grade of pNETs, and can provide clinicians with a simple, practical, and non-invasive tool for personalized management of pNETs patients.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictors of hepatocellular carcinoma in LR-M category lesions, a multi-institutional analysis.","authors":"Marybeth Nedrud, Tanya Wolfson, Brian Allen, Anum Aslam, Lauren Burke, Victoria Chernyak, Kathryn Fowler, Tyler J Fraum, Hong-Il Ha, Elizabeth M Hecht, Tracy Jaffe, Kevin Kalisz, Andrea Siobhan Kierans, Daniel R Ludwig, Jasnit S Makkar, Katrina McGinty, Matthew McInnes, Mishal Mendiratta-Lala, Omobonike Oloruntoba, Damithri Ranathunga, Benjamin Wildman-Tobriner, Anthony C Gamst, Diana M Cardona, Andrew Muir, Mustafa Bashir","doi":"10.1007/s00261-025-04960-6","DOIUrl":"https://doi.org/10.1007/s00261-025-04960-6","url":null,"abstract":"<p><strong>Purpose: </strong>The Liver Imaging Reporting and Data System (LI-RADS, LR) provides a framework for diagnosing hepatocellular carcinoma (HCC, LR-5). However, not all HCCs meet LR-5 criteria and are instead categorized as LR-M, probably or definitely malignant but not specific for HCC, necessitating biopsy for diagnosis. The purpose is to identify factors associated with HCC in LR-M observations.</p><p><strong>Methods: </strong>This is an IRB-approved, retrospective analysis of participants from 8 institutions that had a LR-M observation on CT or MRI with corresponding histopathologic diagnosis. Demographics and biochemical data were examined. Central review using the LI-RADS v2018 algorithm was performed. Kappa statistics defined inter-reader agreement. Random forest and logistic regression analyses generated a model for HCC diagnosis.</p><p><strong>Results: </strong>162 participants with 162 LR-M observations were included. 46% of observations (74/162) were HCC and 37% were cholangiocarcinoma (60/162). Two of 34 imaging features- observation size and intra-lesion iron- showed moderate to strong inter-reader agreement (Kappa ≥ 0.60) while the remainder showed weak or no agreement (Kappa < 0.60). Random forest analysis showed biochemical features to be more predictive of HCC than imaging features. Logistic regression analysis of a model based on INR and AFP provided a 72% sensitivity and 61% specificity for HCC by Youden's index and a 90% specificity threshold yielded 38% sensitivity, 75% positive predictive value, and 66% negative predictive value.</p><p><strong>Conclusions: </strong>Our results show INR and AFP are associated with HCC in LR-M observations. A high-specificity threshold may assist in the non-invasive diagnosis of HCC in the appropriate setting. In certain at-risk patients with a LR-M observation on diagnostic imaging, serum AFP and INR maybe useful tools for the non-invasive diagnosis of HCC.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143955444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.","authors":"John W Garrett, Perry J Pickhardt, Ronald M Summers","doi":"10.1007/s00261-025-04951-7","DOIUrl":"https://doi.org/10.1007/s00261-025-04951-7","url":null,"abstract":"<p><p>Accurate, reproducible body composition analysis from abdominal computed tomography (CT) images is critical for both clinical research and patient care. We present a fully automated, artificial intelligence (AI)-based pipeline that streamlines the entire process-from data normalization and anatomical landmarking to automated tissue segmentation and quantitative biomarker extraction. Our methodology ensures standardized inputs and robust segmentation models to compute volumetric, density, and cross-sectional area metrics for a range of organs and tissues. Additionally, we capture selected DICOM header fields to enable downstream analysis of scan parameters and facilitate correction for acquisition-related variability. By emphasizing portability and compatibility across different scanner types, image protocols, and computational environments, we ensure broad applicability of our framework. This toolkit is the basis for the Opportunistic Screening Consortium in Abdominal Radiology (OSCAR) and has been shown to be robust and versatile, critical for large multi-center studies.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic performance of apparent diffusion coefficient values in differentiating benign from malignant lesions in prostate PI-RADS v2.1 category 1 \"nodules in nodule\".","authors":"Minghua Sun, Fei Li, Xiaoyan Zhang, Rui Wu, Wenya Liu, Li Xu, Mengjie Wu, Yangang Wang","doi":"10.1007/s00261-025-04930-y","DOIUrl":"https://doi.org/10.1007/s00261-025-04930-y","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the associations between the apparent diffusion coefficient (ADC) values of prostate PI-RADS v2.1 category 1 \"nodules in nodule\" and their pathological characteristics.</p><p><strong>Methods: </strong>We retrospectively analyzed the prostate images from 226 male patients who underwent biopsy following MRI from January 2019 to December 2024. Two radiologists evaluated the PI-RADS v2.1 categories of identified nodules, measured the ADC values of the prostate nodules in a double-blind manner, and analyzed the associations between these values and the pathological characteristics of the nodules via independent sample t tests or Mann-Whitney U test.</p><p><strong>Results: </strong>The ADC values of PI-RADS v2.1 category 1 \"nodules in nodule\" pathologically confirmed as clinically significant prostate cancer (csPCa) were lower than those of benign prostate hyperplasia (BPH) ((unit in ×10^- 3 mm²/s)TZ: 0.739 ± 0.15 versus 0.984 ± 0.24; PZ: 0.719 ± 0.17 versus 1.036 ± 0.21, p < 0.001). The AUCs were 0.799 (TZ) with a cutoff of 0.835 × 10^- 3mm²/s, and 0.873(PZ) with a cutoff of 0.795 × 10^- 3mm²/s, respectively. The total prostate-specific antigen (tPSA), free/t PSA, PSA density (PSAD), and prostate gland volume (PGV) differed significantly between patients with PI-RADS v2.1 \"nodules in nodule\" that were pathologically confirmed as csPCa and patients with BPH (all p < 0.05).</p><p><strong>Conclusion: </strong>In patients with PI-RADS v2.1 category 1 \"nodules in nodule\", when the ADC values are less than 0.835 × 10^- 3mm²/s in the TZ, the PI-RADS v2.1 score of the nodule can be upgraded to 3.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State of the art review of AI in renal imaging.","authors":"Ali Sheikhy, Fatemeh Dehghani Firouzabadi, Nathan Lay, Negin Jarrah, Pouria Yazdian Anari, Ashkan Malayeri","doi":"10.1007/s00261-025-04963-3","DOIUrl":"https://doi.org/10.1007/s00261-025-04963-3","url":null,"abstract":"<p><p>Renal cell carcinoma (RCC) as a significant health concern, with incidence rates rising annually due to increased use of cross-sectional imaging, leading to a higher detection of incidental renal lesions. Differentiation between benign and malignant renal lesions is essential for effective treatment planning and prognosis. Renal tumors present numerous histological subtypes with different prognoses, making precise subtype differentiation crucial. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), shows promise in radiological analysis, providing advanced tools for renal lesion detection, segmentation, and classification to improve diagnosis and personalize treatment. Recent advancements in AI have demonstrated effectiveness in identifying renal lesions and predicting surveillance outcomes, yet limitations remain, including data variability, interpretability, and publication bias. In this review we explored the current role of AI in assessing kidney lesions, highlighting its potential in preoperative diagnosis and addressing existing challenges for clinical implementation.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging characteristics and clinical outcomes of spontaneous celiac artery dissection.","authors":"Rika Yoshida, Hisatoshi Araki, Takeshi Yoshizako, Yasushi Kaji","doi":"10.1007/s00261-025-04961-5","DOIUrl":"https://doi.org/10.1007/s00261-025-04961-5","url":null,"abstract":"<p><strong>Purpose: </strong>Spontaneous celiac artery dissection (CeAD) is a rare cause of acute abdominal pain. This study examined computed tomography (CT) scan findings, natural course, and outcomes of spontaneous CeAD.</p><p><strong>Methods: </strong>Sixty patients who underwent CT scan and were diagnosed with CeAD between April 2003 and June 2023 were retrospectively reviewed. Patients with aortic dissection (n = 25), iatrogenic CeAD (n = 6), traumatic CeAD (n = 1), and celiac artery (CeA) aneurysm (n = 4) and those undergoing noncontrast-enhanced CT scans (n = 1) were excluded. The remaining 23 consecutive patients were divided into the acute symptomatic and chronic asymptomatic CeAD groups. Correlations between CT classification, treatment, and outcomes were investigated.</p><p><strong>Results: </strong>Eighteen (78.2%) patients had hypertension requiring treatment. Of 23 patients, 18 were asymptomatic, and five had CeAD-related abdominal pain requiring hospitalization. Two had intraperitoneal bleeding. All hospitalized patients initially received conservative treatment. Upon the initial diagnosis, the mean CeA maximum outer diameter was 11.0 mm (range: 7-16). Ten (43.5%) patients had stenosis at the CeA origin. Based on CT findings from initial diagnosis to final follow-up, the outer diameter remained stable. CT showed that chronic CeAD was mostly Kim classification type IA or IB. In symptomatic patients, the dissection length on initial CT significantly decreased by final CT. No follow-up symptoms were related to CeAD, and the condition remained stable. No patients required vascular repair or surgery, and there were no deaths.</p><p><strong>Conclusion: </strong>Spontaneous CeAD, asymptomatic and symptomatic, remained stable in imaging and clinical outcomes with conservative treatment. The Kim classification on CT plays a key role in identifying chronic CeAD, mostly type IA or IB.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"US-Guided percutaneous core needle biopsy via the complete transhepatic approach: a reliable option for deep abdominal lesions.","authors":"Halil Serdar Aslan, Kadir Han Alver","doi":"10.1007/s00261-025-04958-0","DOIUrl":"https://doi.org/10.1007/s00261-025-04958-0","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the feasibility, reliability, and diagnostic performance of ultrasound (US)-guided percutaneous core needle biopsy (PCNB) performed via the complete transhepatic approach (CTHa) for abdominal lesions.</p><p><strong>Materials and methods: </strong>This study included 71 patients (31 males, 40 females) with a mean age of 64.8 ± 13.9 years (range: 19-93) who underwent US-guided PCNB via the CTHa for abdominal lesions between January 2014 and December 2024. All biopsies were performed by interventional radiologists with at least five years of experience using a coaxial system and an 18-gauge automatic biopsy device. Patients were assessed for technical success, diagnostic yield, and complications, which were classified as major or minor based on the Society of Interventional Radiology (SIR) guidelines.</p><p><strong>Results: </strong>Tissue samples were successfully obtained in all cases, achieving a 100% technical success rate. Adequate material for pathological diagnosis was available in 63 of 71 patients (88.7%), while a definitive diagnosis could not be established in 11.3% of cases. Diagnostic yield was significantly influenced by lesion type (solid or mixed with cystic components) and anatomical location (p = 0.001 and p = 0.032, respectively). Complications occurred in 12.7% of patients, including 11.3% minor and 1.4% major complications. Univariate logistic regression analysis identified a history of malignancy, lesion size along the biopsy path, and the length of liver parenchyma traversed as significant predictors of complications (p = 0.012, 0.027 and 0.003 respectively). In the multivariate model, liver parenchyma length remained the only independent risk factor (p = 0.023).</p><p><strong>Conclusion: </strong>US-guided PCNB via the CTHa is a safe and effective option for abdominal lesions when extrahepatic access is not feasible. While longer liver tissue traversal increases the risk of minor complications, no major adverse events were observed. Careful procedural planning and consideration of lesion location and cystic content are essential to optimize diagnostic yield.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143955157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adrenal lesion classification revisited: validation and adjustment of dual-energy CT derived virtual unenhanced attenuation thresholds.","authors":"Pascale Bernard, Christian Nelles, Philipp Fervers, Joline Schwan, Kaloyan Dankov, David Maintz, David Zopfs, Nils Große Hokamp, Thorsten Persigehl, Simon Lennartz","doi":"10.1007/s00261-025-04939-3","DOIUrl":"https://doi.org/10.1007/s00261-025-04939-3","url":null,"abstract":"<p><strong>Objectives: </strong>Dual-energy CT (DECT)-derived virtual unenhanced (VUE) images have been investigated for adrenal lesion differentiation, yet previously reported thresholds vary, hampering clinical application. We aimed to test previous VUE thresholds for adrenal lesion differentiation in a large retrospective cohort, to provide a cross-validated threshold based on our data, and to investigate the influence of underlying malignancies on differentiation accuracy.</p><p><strong>Methods: </strong>290 patients with 348 adrenal lesions (169 metastases, 179 adenomas) were included. Dual-layer DECT-derived VUE thresholds from 3 previous studies were retrieved, applied to our cohort and corresponding sensitivity/specificity/accuracy was calculated. Optimal threshold based on our data were determined using ROC-analysis with five-fold cross validation. Moreover, a threshold with similar specificity to the 10 HU threshold in unenhanced images was calculated. Subgroup analysis of adrenal lesion differentiation depending on underlying malignancies was performed.</p><p><strong>Results: </strong>The previously suggested thresholds were 20, 22 and 29 HU, and corresponding sensitivity/specificity/accuracy was 0.61/0.92/0.76, 0.67/0.91/0.78, and 0.82/0.59/0.71, respectively. The threshold determined from our cohort was 24.7 HU, yielding a sensitivity/specificity/accuracy of 0.76/0.81/0.79. Differentiation in disease-specific subgroups showed similar sensitivity/specificity/accuracy (Melanoma:0.78/0.84/0.79; Lung cancer:0.78/0.8/0.78; RCC:0.78/1/0.79). The VUE threshold to achieve a 0.98 specificity similar to the unenhanced 10 HU cutoff was 17.3 HU, yielding a sensitivity of 0.49.</p><p><strong>Conclusion: </strong>Previous VUE attenuation thresholds showed a varying accuracy for differentiation between adenomas and metastases. A cross-validated VUE threshold of 24.7 HU yielded a mean accuracy of 0.79, whereas a threshold of 17.3 HU was best for achieving comparable specificity as reported for the 10 HU threshold in unenhanced images.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}