Journal of Computer Assisted Tomography最新文献

{"title":"Neuroendocrine Malignancies of the Cervix: What Radiologists Need to Know.","authors":"Anu P Gayathri, Anand Patel, Love Shah, Thanchanok Chaiprasit, Elizabeth M Jensen, Xianzhong Ding, Margaret R Liotta, Jeanne Horowitz","doi":"10.1097/RCT.0000000000001862","DOIUrl":"10.1097/RCT.0000000000001862","url":null,"abstract":"<p><p>Neuroendocrine malignancies of the cervix (NEM) are an aggressive tumor with early metastasis and recurrence, resulting in poor prognosis and 5-year mortality ranging from 10% to 25% for later stages of disease. These are a rare malignancy of middle-aged and elderly women with an incidence of <2%. The clinical presentation is like the common cervical cancer-squamous cell carcinoma, presenting with nonspecific vaginal bleeding. Pap smear has very low sensitivity and accuracy for NEM, leading to delayed diagnosis. Due to its low incidence, the lack of specific clinical findings, and the limited sensitivity of Pap smears for diagnosing NEM, delays in diagnosis are common, contributing to a higher number of late-stage presentations. Hence, diagnosis by imaging using features such as intratumoral necrosis and hemorrhage, lower ADC values with a cutoff of 0.9×10 -3  mm 2 /s, lymphadenopathy, parametrial and vascular invasion, infiltrative features, and pathologic sampling with immunohistochemical staining is crucial and of paramount importance. We aim to discuss the clinical, imaging, and pathologic findings of NEM and describe the current management options for patients with this aggressive malignancy.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"566-576"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI Evaluation of Indeterminate Pulmonary Nodules.","authors":"Mark M Hammer, Rachna Madan","doi":"10.1097/RCT.0000000000001844","DOIUrl":"10.1097/RCT.0000000000001844","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate MRI features for differentiating pulmonary hamartomas from other nodule pathologies, including carcinoid tumors and non-small cell lung cancer (NSCLC).</p><p><strong>Materials and methods: </strong>This retrospective study analyzed chest MRIs of 48 patients with pulmonary nodules from 2017 to 2025. Patients had either pathologic diagnosis or stable follow-up confirming hamartoma. Two blinded radiologists reviewed images and recorded enhancement pattern (solid, rim, or speckled/mesh). The signal intensity index (SII) was calculated using chemical shift imaging.</p><p><strong>Results: </strong>Hamartomas (n=19, 40%), carcinoids (n=17, 35%), and adenocarcinomas (n=9, 19%) were the most common diagnoses. Rim and speckled/mesh enhancement patterns were nearly exclusive to hamartomas (22% and 39% vs. 0% and 4%, respectively, P <0.001). An SII >10% showed 61% sensitivity and 100% specificity for hamartomas. Combining rim or speckled/mesh enhancement patterns and SII >10% yielded 79% sensitivity and 96.6% specificity for hamartomas.</p><p><strong>Conclusions: </strong>Although chemical shift imaging is diagnostic for hamartomas, signal dropout is present in just over half of cases. Rim or speckled/mesh enhancement patterns are also highly suggestive. Combining these MRI features significantly improves diagnostic accuracy for pulmonary hamartomas, potentially reducing further follow-up or invasive procedures for indeterminate nodules.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"650-655"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145756782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Could Calcium Score Serve as a Screening Tool to Rule Out Significant Coronary Artery Stenosis in Pre-liver and Pre-renal Transplant Patients?","authors":"Yuval Liberman, Om Biju Panta, Nitai Bar, Alexander Brook, Rachael Kirkbride, Talal Al-Otaibi, Ariane Fraiche, Anne-Marie Anagnostopoulos, Michael Gavin, Diana Litmanovich","doi":"10.1097/RCT.0000000000001842","DOIUrl":"10.1097/RCT.0000000000001842","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to assess the diagnostic value of the coronary artery calcium score (CACS) to predict significant coronary artery stenosis (SCAS) in presolid organ transplant patients with a moderate and high risk of coronary artery disease.</p><p><strong>Methods: </strong>In this retrospective HIPAA-compliant study, all pre-liver/kidney transplant patients with intermediate or high risk of coronary artery disease who underwent cardiac CT angiogram (CCTA) and CACS between January 1, 2018 and December 31, 2022 were reviewed. CACS was assessed according to the Agatston score. SCAS was defined as ≥50% diameter stenosis on CCTA. The potential to predict SCAS was assessed by computing the area under the receiver curve (AUC), and the sensitivity and specificity of CACS in diagnosing SCAS were calculated at various CACS thresholds.</p><p><strong>Results: </strong>A total of 291 patients (81; 28% female) with a mean age of 57.7±9.9 years, were included. The median CACS was 772 (IQR: 320 to 1892) in patients with SCAS versus 23 (IQR: 0 to 187) in those without SCAS ( P <0.01). CACS demonstrated efficacy in predicting SCAS, with an AUC of 0.88. In the subgroup analysis, mean CACS differed significantly between pre-liver and pre-kidney solid organ transplant patients, but the difference between AUC curves was not significant. With the use of traditional CACS thresholds, the sensitivity dropped below 95%, but with optimized thresholds of CACS ≤62 or ≥869 for 95% sensitivity and specificity, 63% of pretransplant patients could be attributed either to a low-risk or high-risk for SCAS, respectively.</p><p><strong>Conclusions: </strong>Our study shows that CACS can accurately predict SCAS in a large proportion of pre-kidney and liver transplant patients with intermediate or high risk of coronary artery disease. CACS can be considered as a screening tool to identify patients with low likelihood of SCAS, who might not require CCTA, as well as those with a high likelihood in whom a proactive approach should be considered.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"616-625"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145911937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abdominal Imaging Interpretive Errors in a Quality Assurance Database: One Institution's Experience Over a 5-Year Period.","authors":"Michio Taya, Theresia Aschauer, Javier Villanueva-Meyer, K Pallav Kolli, Benjamin M Yeh, Spencer C Behr","doi":"10.1097/RCT.0000000000001856","DOIUrl":"10.1097/RCT.0000000000001856","url":null,"abstract":"<p><strong>Objectives: </strong>We hypothesized that interpretive error location and patient harm are significantly associated in a quality assurance database.</p><p><strong>Methods: </strong>We reviewed all interpretive errors reported in the abdomen and pelvis in a quality assurance database over a 5-year period. Imaging exam data, interpretive error, anatomic location, and clinical outcome by the Agency for Healthcare Research and Quality Harm Scale were recorded. Multivariable logistic regression modeling was performed.</p><p><strong>Results: </strong>263 interpretive error reports were identified. The most reported error locations included hepatobiliary (15%), gastrointestinal tract (13%), bone (13%), reproductive organs (12%), and peritoneum/mesentery/omentum (12%). Of the 263, 165 (63%) errors were found to result in any patient harm, compared with 98/263 (37%) errors with no harm. Anatomic category was significantly associated with harm when adjusted for age, sex, exam setting, and exam priority in our logistic regression model ( P =0.003). When compared with hepatobiliary errors, errors in the kidney/ureter/bladder, gastrointestinal tract, and reproductive organs were associated with greater odds of harm. Errors in the peritoneum/mesentery/omentum and bone were associated with smaller odds of harm. Retroperitoneum/body wall and vascular errors had similar odds of harm to hepatobiliary errors.</p><p><strong>Conclusion: </strong>Interpretive error location is significantly associated with patient harm. Our results may inform an evidence-based approach toward improving the process by which errors are reviewed in radiology quality assurance programs. The most reported interpretive error locations in an abdominal imaging quality assurance system are not necessarily the most harmful to patients.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"662-671"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147344526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation of Automated in Vivo Image Quality With Radiologist's Performance in Abdomen Computed Tomography Across Conventional and Deep Learning Reconstructions.","authors":"Mojtaba Zarei, Francesco Ria, Corey T Jensen, Xinming Liu, Craig K Abbey, Ehsan Samei","doi":"10.1097/RCT.0000000000001845","DOIUrl":"10.1097/RCT.0000000000001845","url":null,"abstract":"<p><strong>Objective: </strong>Image quality evaluation in radiology is most relevant when reflects radiologists' performance. This study assessed how image quality measurement in terms of in vivo -characterized detectability index ( ) for low-contrast liver lesion assessment in CT is correlated with radiologists' performance across 2 different CT reconstructions.</p><p><strong>Methods: </strong>Fifty-one contrast-enhanced abdominal studies for investigating colorectal liver metastases were prospectively performed using 2 radiation dose exposures and reconstructed with Filtered back projection (FBP) and deep learning image reconstruction (DL) algorithms for a total of 161 noncalcified hypoattenuating lesions for 3 lesion size (D) subsets (<6 mm, 6 to 10 mm, and >10 mm). Images were assessed by expert radiologists for hepatic lesion detection task and likelihood of malignancy across the 2 imaging conditions. All cases were also evaluated automatically in terms of in vivo as a metric of task-based performance, both using a conventional technique and a new formalism of an added frequency term in the internal noise component of to accommodate the nonlinearity of the DL reconstruction ( adj ).</p><p><strong>Results: </strong>The study found conventionally defined d' well-reflective of radiologists' evaluation of FBP images but not well-aligned with that of DL images. The new formalism provided more consistent reflection of performance across reconstruction techniques. In particular, in the lesion group D <=6 mm, the difference between radiologists' accuracy in images acquired with DL and images acquired with FBP was -26%, and the related adj difference was -9%, whereas the was 34%. Analogously, for the lesion group 6 mm < D <=10 mm, the differences were -15%, -13%, and 29%, respectively. Lastly, for the lesion group D>10 mm, radiologists showed the same accuracy in both FPB and DL images, difference in adj was -11%, and difference in was 31%.</p><p><strong>Conclusion: </strong>The new formalism can robustly reflect CT systems clinical performance irrespective of reconstruction algorithm. The methodology can be more readily applied to assess the real-world performance of CT systems.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"656-661"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145970782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EATV, EAT-CT Attenuation, and PACT-FAI as Predictors of Dual Cardiovascular Outcomes: Postoperative Atrial Fibrillation and Graft Occlusion.","authors":"Zerui Wang, Hui Wang, Tiantian Yang, Hairong Yu, Kun Li, Peiji Song","doi":"10.1097/RCT.0000000000001852","DOIUrl":"10.1097/RCT.0000000000001852","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the predictive value of epicardial adipose tissue volume (EATV), EAT-CT attenuation, and pericoronary adipose tissue fat attenuation index (PCAT-FAI) for postoperative atrial fibrillation (POAF) and graft occlusion after coronary artery bypass grafting (CABG).</p><p><strong>Methods: </strong>In this single-center retrospective study, 83 patients undergoing CABG with preoperative CCTA were enrolled. EATV, EAT-CT attenuation, pericoronary adipose tissue volume (PCAT volume), and PCAT-FAI were measured. Multivariate logistic regression and ROC analysis were used to assess their predictive performance.</p><p><strong>Results: </strong>During a 13-month follow-up, 20.4% developed POAF and 39.8% had graft occlusion. The combination of EATV, EAT-CT attenuation, and PCAT-RCA-FAI predicted POAF with an AUC of 0.880, superior to any single parameter. It also showed improved prediction for graft occlusion (AUC=0.814).</p><p><strong>Conclusion: </strong>EATV, EAT-CT attenuation, and PCAT-RCA-FAI provide complementary predictive value for POAF and graft occlusion after CABG. Combined use improves preoperative risk stratification and supports clinical decision-making.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"637-645"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147616172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship Between Pericoronary Fat Attenuation Index on Baseline CT and Target Vessel Revascularization in Patients After Percutaneous Coronary Intervention.","authors":"Chentao Zhu, Ke Shi, Na Li, Xiaolin Dong, Tong Zhang","doi":"10.1097/RCT.0000000000001841","DOIUrl":"10.1097/RCT.0000000000001841","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the association of the pericoronary fat attenuation index (FAI) derived from coronary computed tomography angiography (CCTA) with target vessel revascularization (TVR) in symptomatic postprocedure patients.</p><p><strong>Methods: </strong>A retrospective analysis was conducted, including 154 patients with 191 lesions scheduled for stenting who underwent invasive coronary angiography (ICA) after preinterventional CCTA. The proximal pericoronary FAI of the 3 major coronary arteries and lesion-specific pericoronary FAI were measured on preprocedure CCTA using semi-automated software. Lesions were randomly allocated to a training set (n=133) and a test set (n=58). Multivariate logistic regression analyses were performed to identify independent variables associated with TVR in the training cohort. Analyses were performed on the patient and vessel levels.</p><p><strong>Results: </strong>A total of 154 patients (age 60.9±9.5 y, 68.8% male) with 191 lesions scheduled for stenting were included. On the basis of patient-level analysis, patients with TVR showed higher pericoronary FAI compared with patients without TVR. In vessel-level analysis, the regression model incorporating 1 cm 2 mm lesion-specific pericoronary FAI demonstrated superior diagnostic performance in both cohorts (training set AUC 0.814, 95% CI: 0.721-0.907; test set AUC 0.794, 95% CI: 0.659-0.928). The optimal cutoff value of -70.49 HU for the 1 cm 2 mm lesion-specific pericoronary FAI was determined by maximizing Youden's index, achieving a sensitivity of 75.0% and specificity of 63.0% in the test set. The model exhibited excellent calibration and clinical utility as confirmed by calibration curves and decision curve analysis (DCA). Multivariate logistic regression analysis showed that 1 cm 2 mm lesion-specific pericoronary FAI (OR 1.2, 95% CI: 1.01-1.42, P =0.036) was an independent predictor of TVR.</p><p><strong>Conclusions: </strong>CCTA-derived pericoronary FAI is significantly associated with TVR in postprocedural patients. The 1 cm 2 mm lesion-specific pericoronary FAI, with an optimal cutoff of -70.49 HU, represents an effective tool for TVR risk stratification in this patient population.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"626-636"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Identification of Clear Cell Renal Cell Carcinoma on MR Imaging.","authors":"Junyu Guo, Gaelle Haddad, Yin Xi, Lauren Hinojosa, Keith Hulsey, Emin Albayrak, Ivan Pedrosa","doi":"10.1097/RCT.0000000000001848","DOIUrl":"10.1097/RCT.0000000000001848","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the performance of various convolutional neural networks (CNNs) in identifying clear renal cell carcinoma (ccRCC) on MRI and to compare them with radiologists using the clear cell likelihood score (ccLS) algorithm.</p><p><strong>Methods: </strong>A total of 480 CNN models were retrospectively trained using 1 or 3 (fusion) different types of MR images obtained from 310 patients with pathologically confirmed renal masses (cT1, ≤7 cm). MR images included T2-weighted (T2w), T1-weighted opposed-phase/in-phase (T1wOPIP), and corticomedullary phase (CMphase) contrast-enhanced MR acquisitions. To increase the robustness of classification, a 5-fold cross-validation was performed, and the averaged area under curve (AUC) values were compared among the CNN models. The best performing CNN models were compared with radiologists' performance using the ccLS algorithm.</p><p><strong>Results: </strong>The performance of CNN models in ccRCC classification was significantly influenced by the choice of models, the type of input images, and image size. The best CNN models for the diagnosis of ccRCC using T2w and all 3 types of images (fusion CNN models) achieved an AUC of 0.79 and 0.8, respectively. Combining these models using logistic regression produced a slightly higher AUC value of 0.83. Radiologists using the ccLS achieved an AUC value of 0.85, which was not statistically different than the T2w+fusion model ( P >0.05). Combining the T2w+fusion model and the ccLS achieved an AUC value of 0.86, which was not different than the AUC value using ccLS alone ( P >0.05).</p><p><strong>Conclusions: </strong>A CNN model integrating three different types of MR images demonstrated performance comparable to that of radiologists in the diagnosis of ccRCC.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"555-565"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12888796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146142407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Routine CT Interpretation for Comprehensive Ventricular Myocardial Assessment.","authors":"Elizabeth Lee, Prabhakar S Rajiah, Nicholas S Burris, William Truesdell, Chiemezie Amadi, Kavita Rajkotia, Prachi P Agarwal","doi":"10.1097/RCT.0000000000001851","DOIUrl":"10.1097/RCT.0000000000001851","url":null,"abstract":"<p><p>Chest CTs done for unrelated reasons have the potential to reveal unsuspected abnormalities and provide complementary information to help direct appropriate management. By thoroughly understanding the diagnostic role and limitations of CT in assessing ventricular size and morphology as well as myocardial attenuation, enhancement, and thickness, radiologists will be better equipped to differentiate between normal and abnormal findings and provide precise, clinically appropriate recommendations.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"577-587"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146125239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI Proton Density Fat Fraction From PET/MRI Elucidates the Biphasic Impact of Hepatic Steatosis on Liver 18 F-FDG Uptake.","authors":"Zhaoting Meng, Mu Lin, TungChin Chu, Dandan Zhu, Liling Peng, Mingxiang Sun, Sisi Peng, Gang Feng, Xin Gao","doi":"10.1097/RCT.0000000000001849","DOIUrl":"10.1097/RCT.0000000000001849","url":null,"abstract":"<p><strong>Objective: </strong>The effect of hepatic steatosis on liver 18 F-FDG uptake remains unclear, as previous PET/CT-based studies have reported inconsistent findings. This study aimed to investigate the impact of hepatic steatosis on 18 F-FDG uptake and to identify the factors influencing the liver standardized uptake value (SUV) using PET/MRI.</p><p><strong>Methods: </strong>A total of 188 participants who underwent PET/MRI for cancer screening between January 2017 and December 2021 were evaluated. The liver fat fraction was quantified using MRI proton density fat fraction (PDFF). Participants were classified into 3 groups based on PDFF thresholds: normal (<6%), mild steatosis (6% to 17%), and moderate-to-severe steatosis (>17%). Liver SUV max and SUV mean were measured and analyzed for correlations with age, body mass index (BMI), serum lipids, PDFF, and iron deposition. Multivariate and segmented regression analyses were performed to identify independent predictors of liver SUV.</p><p><strong>Results: </strong>Liver SUV max and SUV mean increased with mild steatosis but decreased with moderate-to-severe steatosis ( P <0.001). Triglyceride (β=0.068, P =0.002), high-density lipoprotein cholesterol (β=-0.295, P =0.001), body mass index (β=0.018, P =0.037), and age (β=0.007, P =0.007) independently predicted liver SUV mean . PDFF was positively associated with SUV mean in normal livers but negatively associated with fatty livers.</p><p><strong>Conclusions: </strong>PET/MRI demonstrated that hepatic steatosis, as assessed by PDFF, significantly influenced liver 18 F-FDG uptake in a biphasic manner. These findings underscore the importance of integrating fat quantification into PET interpretation to improve the accuracy of oncologic imaging.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"534-541"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147276393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}