Abdominal Radiology最新文献

{"title":"DWI of the rectum with deep learning reconstruction: comparison of PROPELLER, reduced FOV, and conventional DWI.","authors":"Shohei Matsumoto, Takahiro Tsuboyama, Hiromitsu Onishi, Koki Kaketaka, Tetsuya Wakayama, Xinzeng Wang, Atsushi Nakamoto, Takashi Ota, Hideyuki Fukui, Toru Honda, Kengo Kiso, Koji Oba, Noriyuki Tomiyama","doi":"10.1007/s00261-025-04950-8","DOIUrl":"https://doi.org/10.1007/s00261-025-04950-8","url":null,"abstract":"<p><strong>Purpose: </strong>To compare the image quality and diagnostic performance of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER), reduced field-of-view (rFOV), and conventional diffusion-weighted imaging (cDWI) combined with deep learning reconstruction (DLR) for evaluating rectal tumors.</p><p><strong>Methods: </strong>This prospective study included 42 MRI examinations of 38 patients with rectal tumors who underwent initial staging and/or restaging MRI. PROPELLER-DWI, rFOV-DWI, and cDWI obtained with DLR were reviewed by two radiologists and compared for image quality and diagnostic performance for local tumor extent at staging and restaging and response to chemoradiotherapy at restaging.</p><p><strong>Results: </strong>PROPELLER-DWI had significantly the least artifacts and distortions, but the worst perceptive noise, while rFOV-DWI had significantly the best sharpness for both readers (P < 0.01). For overall image quality and rectal/tumor conspicuity, PROPELLER-DWI and rFOV-DWI were significantly superior to cDWI in both readers (P < 0.01). The incidence of suboptimal image quality was significantly lower with PROPELLER-DWI and rFOV-DWI than with cDWI (5 and 1 patients with PROPELLER-DWI, 14 and 6 with rFOV-DWI, and 29 and 25 with cDWI by the 2 readers, P < 0.01). Although there were no significant differences in the accuracy of staging and restaging among the 3 types of DWI, inter-reader agreement was highest for PROPELLER-DWI (weighted kappa, 0.62-0.71) compared with cDWI (weighted kappa, 0.38-0.52) and rFOV-DWI (weighted kappa, 0.47-0.61).</p><p><strong>Conclusions: </strong>PROPELLER-DWI and rFOV-DWI with DLR may improve the image quality of rectal DWI by reducing artifacts and distortions or increasing sharpness, although the impact on diagnostic accuracy was not significant.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963395","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-derived extracellular volume to assess liver fibrosis in patients with metabolic-associated steatotic liver disease.","authors":"Flavia Vernin de Oliveira Terzi, Daniella Braz Parente, Gabriel Cordeiro Camargo, Ana Maria Pittella, Gilberto Silva-Junior, Gabrielle Gonçalves de Novaes, Jaime Araújo Oliveira Neto, Julia Machado Barroso, Martha Valéria Tavares Pinheiro, Adriana Soares Xavier-de-Brito, Renée Sarmento de Oliveira, Rosana Souza Rodrigues, Ronir Raggio Luiz, Andréa Silvestre-Sousa, Renata Mello Perez, Renata Junqueira Moll-Bernardes","doi":"10.1007/s00261-025-04945-5","DOIUrl":"https://doi.org/10.1007/s00261-025-04945-5","url":null,"abstract":"<p><strong>Purpose: </strong>Metabolic associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease, and non-invasive fibrosis staging still represents a challenge. Our main objective was to estimate the degree of liver fibrosis in these patients using T1 mapping and the extracellular volume (ECV) by MRI in comparison with liver stiffness assessed by MR elastography (MRE).</p><p><strong>Methods: </strong>In a single-center cross-sectional study, patients with MASLD were prospectively enrolled and underwent MRI with liver T1 mapping and ECV calculations. Groups with and without significant liver fibrosis assessed by MRE were compared with the Mann-Whitney test, chi-square test, and Fisher's exact test. Correlation analysis was conducted using Spearman's test, and a receiver operating characteristic (ROC) curve was generated to assess the ability of ECV to differentiate between groups.</p><p><strong>Results: </strong>This study evaluated 54 patients, 37% were men, with a mean age of 58.0 ± 12.0 years. Mild liver fibrosis (F0-F1) was present in 38 patients, and significant fibrosis (F2-F4) was detected in 16 patients. Patients with significant fibrosis presented higher native T1 (954 ± 126 vs. 820 ± 123; p < 0.001) and ECV (37.9% vs. 29.1%; p < 0.001) values than those with no/mild fibrosis. Liver stiffness was correlated with native T1 (r = 0.512, p < 0.001) and ECV (r = 0.443, p < 0.001). The native liver T1 and ECV differentiated patients with and without significant liver fibrosis on MRE (AUC = 0.85 and 0.84, respectively).</p><p><strong>Conclusion: </strong>Native T1 and ECV show potential as an alternative method for the non-invasive staging of fibrosis in patients with MASLD, although further validation in larger cohorts is needed.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959496","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":"Assessment of endometrial cancer with microcystic, elongated, and fragmented pattern invasion using multiparametric MRI.","authors":"Koki Kaketaka, Takahiro Tsuboyama, Hideyuki Fukui, Shohei Matsumoto, Atsushi Nakamoto, Takashi Ota, Toru Honda, Kengo Kiso, Kansuke Kido, Noriyuki Tomiyama","doi":"10.1007/s00261-025-04937-5","DOIUrl":"https://doi.org/10.1007/s00261-025-04937-5","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the MRI findings of endometrial cancer with microcystic, elongated, and fragmented (MELF) pattern invasion and to evaluate the optimal sequences to detect deep myometrial invasion with MELF.</p><p><strong>Materials and methods: </strong>This retrospective single-center case-control study included 85 patients with endometrial cancer, including 17 patients with MELF, between December 2020 and January 2023. Preoperative MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) with equilibrium phase contrast-enhanced (CE) MRI were reviewed by three radiologists. DWI signal gradation with DWI-CE mismatch (DG-DCM) and tumor-myometrium synchronous early enhancement (TME) were evaluated, as well as the diagnostic performance for deep myometrial invasion, first with T2WI + CE alone and then with the addition of DWI + DCE. Pathology was used as the reference standard.</p><p><strong>Results: </strong>The sensitivity and specificity of DG-DCM were 41.2-76.5% and 89.7-98.5%, and those of TME were 70.7-82.4% and 94.1-95.6%, respectively, for MELF by the three readers. For the diagnosis of deep myometrial invasion with MELF, the addition of DWI + DCE to T2WI + CE significantly improved the sensitivity for two readers (from 16.7 to 91.7% for Reader 1, from 16.7 to 83.3% for Reader 2, p < 0.01) and the accuracy for one reader (from 35.3 to 82.4% for Reader 1, p < 0.01). In contrast, sensitivity, specificity and accuracy did not change with the addition of DWI + DCE in tumors without MELF.</p><p><strong>Conclusion: </strong>Endometrial cancer with MELF may show characteristic MRI findings of DG-DCM and TME. The value of DWI and DCE in detecting deep myometrial invasion may be high for MELF pattern invasion.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958772","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":"Endometriosis: a journey from infertility to fertility.","authors":"Pamela Causa Andrieu, Kelsey Stewart, Rebecca Chun, Madison Breiland, Luciana P Chamie, Kristine Burk, Michael F Ii Neblett, Zaraq Khan, Jeannette Lager, Wendaline VanBuren, Liina Poder","doi":"10.1007/s00261-025-04935-7","DOIUrl":"https://doi.org/10.1007/s00261-025-04935-7","url":null,"abstract":"<p><p>Endometriosis, a chronic and multifocal inflammatory condition with a substantial estrogen-dependent component, is often linked to infertility. Some patients with endometriosis may require surgical intervention or assisted reproductive technologies to conceive. Although many patients who achieve pregnancy have relatively uncomplicated outcomes because of the progesterone-induced regression of endometriotic lesions, complications can still arise during pregnancy and the peripartum period. Complications include the decidualization of endometriosis implants, with site-specific implications (e.g., decidualized endometrioma mimicking ovarian cancer, decidualized deep endometriosis infiltrating the myometrium leading to uterine rupture, spontaneous hemoperitoneum), placenta previa, preterm labor and premature rupture of membranes, postpartum hemorrhage, or systemic conditions such as hypertensive or coagulation disorders. Herein, we review the background of these conditions and the expected radiologic findings. Additionally, we review essential clinical concepts about the treatment available and the information needed to make health care decisions. This review aims to equip radiologists with essential insights into the challenges faced by patients with endometriosis, from infertility diagnosis through postpartum care. By enhancing radiologists' understanding of these aspects and relevant imaging findings, we aspire to improve maternal and fetal outcomes affected by this complex condition.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963538","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":"Quantitative MRI radiomics approach for evaluating muscular alteration in Crohn disease: development of a machine learning-nomogram composite diagnostic tool.","authors":"Lin Yu, Yong Cai, Shaowei Lin, Huijuan Zhang, Shun Yu","doi":"10.1007/s00261-025-04896-x","DOIUrl":"https://doi.org/10.1007/s00261-025-04896-x","url":null,"abstract":"<p><strong>Background: </strong>Emerging evidence underscores smooth muscle hyperplasia and hypertrophy, rather than fibrosis, as the defining characteristics of fibrostenotic lesions in Crohn disease (CD). However, non-invasive methods for quantifying these muscular changes have yet to be fully explored.</p><p><strong>Aims: </strong>To explore the application value of radiomics based on magnetic resonance imaging (MRI) post-contrast T1-weighted images to identify muscular alteration in CD lesions with significant inflammation.</p><p><strong>Methods: </strong>A total of 68 cases were randomly assigned in this study, with 48 cases allocated to the training dataset and the remaining 20 cases assigned to the independent test dataset. Radiomic features were extracted and constructed a diagnosis model by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Construct a nomogram based on multivariate logistic regression analysis, integrating radiomics signature, MRI features and clinical characteristics.</p><p><strong>Results: </strong>The radiomics model constructed based on the selected features of the post-contrasted T1-weighted images has good diagnostic performance, which yielded a sensitivity of 0.880, a specificity of 0.783, and an accuracy of 0.833 [AUC = 0.856, 95% confidence interval (CI) = 0.765-0.947]. Moreover, the nomogram representing the integrated model achieved good discrimination performances, which yielded a sensitivity of 0.836, a specificity of 0.892, and an accuracy of 0.864 (AUC = 0.926, 95% CI = 0.865-0.988), and it was better than that of the radiomics model alone.</p><p><strong>Conclusions: </strong>The radiomics based on post-contrasted T1-weighted images provides additional biomarkers for Crohn disease. Additionally, integrating DCE-MRI, radiomics, and clinical data into a comprehensive model significantly improves diagnostic accuracy for identifying muscular alteration.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963543","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 ADC-based nomogram model for predicting the prognostic factors in preoperative clinical early-stage cervical cancer patients.","authors":"Xiaoliang Ma, Lu Zhang, Jingjing Lu, Pengju Xu, Liheng Liu, Mengsu Zeng, Jianjun Zhou, Songqi Cai, Minhua Shen","doi":"10.1007/s00261-025-04944-6","DOIUrl":"https://doi.org/10.1007/s00261-025-04944-6","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the feasibility of ADC-based nomogram models for predicting cervical cancer (CC) subtype, lymphovascular space invasion (LVSI) and lymph node metastases (LNM) status in preoperative clinical early-stage CC patients.</p><p><strong>Materials and methods: </strong>A total of 535 CC patients from three independent centers [center A (n = 251) for model training, and centers B (n = 193) and C (n = 91) for external validation] were included. Volumetric ADC histogram metrics (volume, minADC, meanADC, maxADC, skewness, kurtosis, entropy, P10_ADC, P25_ADC, P50_ADC, P75_ADC, and P90_ADC) derived the whole-tumor were calculated. Univariate and multivariate analyses were used to screen the independent predictors and develop nomogram models, with the area under the receiver operating characteristic curve (AUC) for predicting performance estimation.</p><p><strong>Results: </strong>In differentiating adenosquamous carcinoma (ASC)/adenocarcinoma (AC) from squamous cell carcinoma (SCC), the independent predictors of P25_ADC, SCC antigen (SCC-Ag), and CA199 constructed the nomogram_1 model, with AUCs of 0.900 and 0.873 in training and validation sets, respectively. In differentiating AC from ASC, the independent predictors of P50_ADC and SCC-Ag constructed the nomogram_2 model, with AUCs of 0.837 and 0.829 in training and validation sets, respectively. Tumor volume is the only independent predictor of LVSI(+) and LNM(+), with AUCs of 0.608 and 0.694 in the training set, and 0.553 and 0.656 in the validation set, respectively.</p><p><strong>Conclusion: </strong>The ADC-based nomogram models can effectively predict the CC subtypes, but might be insufficient in predicting the LVSI and LNM status in preoperative clinical early-stage patients.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961292","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":"Advancing endometriosis detection in daily practice: a deep learning-enhanced multi-sequence MRI analytical model.","authors":"Mana Moassefi, Shahriar Faghani, Ceylan Colak, Shannon P Sheedy, Pamela L Causa Andrieu, Sherry S Wang, Rachel L McPhedran, Kristina T Flicek, Garima Suman, Hiroaki Takahashi, Candice A Bookwalter, Tatnai L Burnett, Bradley J Erickson, Wendaline M VanBuren","doi":"10.1007/s00261-025-04942-8","DOIUrl":"https://doi.org/10.1007/s00261-025-04942-8","url":null,"abstract":"<p><strong>Background and purpose: </strong>Endometriosis affects 5-10% of women of reproductive age. Despite its prevalence, diagnosing endometriosis through imaging remains challenging. Advances in deep learning (DL) are revolutionizing the diagnosis and management of complex medical conditions. This study aims to evaluate DL tools in enhancing the accuracy of multi-sequence MRI-based detection of endometriosis.</p><p><strong>Method: </strong>We gathered a patient cohort from our institutional database, composed of patients with pathologically confirmed endometriosis from 2015 to 2024. We created an age-matched control group that underwent a similar MR protocol without an endometriosis diagnosis. We used sagittal fat-saturated T1-weighted (T1W FS) pre- and post-contrast and T2-weighted (T2W) MRIs. Our dataset was split at the patient level, allocating 12.5% for testing and conducting seven-fold cross-validation on the remainder. Seven abdominal radiologists with experience in endometriosis MRI and complex surgical planning and one women's imaging fellow with specific training in endometriosis MRI reviewed a random selection of images and documented their endometriosis detection.</p><p><strong>Results: </strong>395 and 356 patients were included in the case and control groups respectively. The final 3D-DenseNet-121 classifier model demonstrated robust performance. Our findings indicated the most accurate predictions were obtained using T2W, T1W FS pre-, and post-contrast images. Using an ensemble technique on the test set resulted in an F1 Score of 0.881, AUROCC of 0.911, sensitivity of 0.976, and specificity of 0.720. Radiologists achieved 84.48% and 87.93% sensitivity without and with AI assistance in detecting endometriosis. The agreement among radiologists in predicting labels for endometriosis was measured as a Fleiss' kappa of 0.5718 without AI assistance and 0.6839 with AI assistance.</p><p><strong>Conclusion: </strong>This study introduced the first DL model to use multi-sequence MRI on a large cohort, showing results equivalent to human detection by trained readers in identifying endometriosis.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054253","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":"Novel ultrasound scoring system to guide cognitive fusion-targeted biopsy: a prospective study.","authors":"Fei Qin, Jingyun Wu, Jianguo Ma, Shaojuan Tian, Derun Li, Shuyuan Chen, Yi Liu, Xuesong Li","doi":"10.1007/s00261-025-04903-1","DOIUrl":"https://doi.org/10.1007/s00261-025-04903-1","url":null,"abstract":"<p><strong>Purpose: </strong>To develop and validate a novel ultrasound scoring system (USS) for assisting cognitive fusion-targeted biopsy (cTB).</p><p><strong>Methods: </strong>We prospectively collected a study cohort consisting of 452 patients with biopsy-naïve, PSA ≤ 20 ng/ml and their 531 Prostate Imaging Reporting and Data System (PI-RADS) v2.1 ≥ 3 lesions. All MRI regions of interest were scored as USS 0, 1, 2, and 3 for the corresponding lesion or region on TRUS. The cumulative cancer detection rate of the biopsy cores was assessed according to USS. Subgroup analysis was conducted to assess the csPCa detection rate following the re-stratification of PI-RADS using USS. Receiver operating characteristics (ROC) analysis was performed for USS, PI-RADS and USS + PI-RADS. The area under the curve (AUC), sensitivity, and specificity were calculated at the cut-off selected by the Youden index.</p><p><strong>Results: </strong>The overall cancer detection rates for USS scores of 0 to 3 were 0% (0/67), 66% (111/166), 83% (176/210), and 100% (59/59), respectively. For USS 2 and USS 3 lesions, the detection rates in targeting the 3rd core (79%, P = 0.774) and 2nd core (93%, P = 0.125) did not significantly increase with subsequent biopsy cores. In the subgroup analysis, the csPCa positive rate for USS 0 was zero across all PI-RADS categories. In contrast, USS 1, 2, and 3 enhanced the csPCa positive rate within each PI-RADS strata. In ROC analysis, the AUC (95% CI) for the combined USS + PI-RADS 0.85 (0.82-0.89) outperformed PI-RADS 0.77 (0.73-0.81) alone (P < 0.001). USS + PI-RADS sensitivity (95% CI) was 80.7% (75.6-84.9) compared to PI-RADS 72.5% (67.6-77.0).</p><p><strong>Conclusion: </strong>In cTB, USS has good performance in cancer risk re-stratification, with higher USS scores correlating with an increased likelihood of cancer and improved diagnostic accuracy.</p><p><strong>Clinical trial registration: </strong>No. 2023-272-002, July 14, 2023.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959148","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":"Influence variables of ultrasound-derived fat fraction in liver fat content measurement: preprandial and postprandial states.","authors":"Shuai Cheng, Wenhao Lv, Tingjing You, Shengmin Zhang","doi":"10.1007/s00261-025-04909-9","DOIUrl":"https://doi.org/10.1007/s00261-025-04909-9","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate whether there is any effect of preprandial and postprandial states of patients on Ultrasound-derived fat fraction(UDFF) in liver fat content measurement.</p><p><strong>Methods: </strong>A retrospective study was conducted on 1596 patients who underwent UDFF from January to September 2024; UDFF measurements were performed by a sonographer, who repeated each measurement 5 times before and after meals, respectively, and finally expressed them as the mean; then paired t-tests and analyses of variance (ANOVA) were used to compare the differences in preprandial and postprandial UDFF and Auto p-SWE( Auto point shear wave elastography) values among groups, and linear regression was used to analyze the differences in preprandial and postprandial UDFF and Auto p-SWE values between each group.</p><p><strong>Results: </strong>The study enrolled 1036 patients(491 males and 545 females), aged 18-89 years, mean age (56.50 ± 14.67) years. The differences in UDFF and Auto p-SWE values between the group eating protein and fatty foods (n = 613) and the group eating light foods (n = 423) were not statistically significant (p > 0.05); the differences in UDFF and Auto p-SWE values between the group with a body mass index(BMI) < 25 kg/m2 (n = 703) and the group with a BMI ≥ 25 kg/m2 (n = 333) were statistically significant (p < 0.05). Preprandial and postprandiall UDFF and Auto p-SWE values were highly positively correlated in the eating group, the protein and greasy food group, and the light food group (r = 0.985, 0.983, 0.988, r = 0.834, 0.849, 0.810, all p < 0.001).</p><p><strong>Conclusions: </strong>UDFF has good consistency in the measurement of liver fat content in preprandial and postprandial states.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952611","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":"Biological characteristics prediction of endometrial cancer based on deep convolutional neural network and multiparametric MRI radiomics.","authors":"Zhichao Wang, Chuchu He, Zhen Liu, Haifeng Luo, Jingjing Li, Jinyuan Xie, Chao Li, Xiandong Wu, Yan Hu, Jun Cai","doi":"10.1007/s00261-025-04929-5","DOIUrl":"https://doi.org/10.1007/s00261-025-04929-5","url":null,"abstract":"<p><p>The exploration of deep learning techniques for predicting various biological characteristics of endometrial cancer (EC) is of significant importance. The objective of this study was to develop an optimized radiomics scheme combining multiparametric magnetic resonance imaging (MRI), deep learning, and machine learning to predict biological features including myometrial invasion (MI), lymph-vascular space invasion (LVSI), histologic grade (HG), and estrogen receptor (ER). This retrospective study involved 201 EC patients, who were divided into four groups according to the specific tasks. The proposed radiomics scheme extracted quantitative imaging features and multidimensional deep learning features from multiparametric MRI. Several classifiers were employed to predict biological features. Model performance and interpretability were assessed using traditional classification metrics, Gradient-weighted Class Activation Mapping (Grad-CAM), and SHapley Additive exPlanation (SHAP) techniques. In the deep MI (DMI) prediction task, the proposed protocol achieved an area under the curve (AUC) value of 0.960 (95% CI 0.9005-1.0000) in the test cohort. In the LVSI prediction task, the AUC of the proposed scheme in the test cohort was 0.924 (95% CI 0.7760-1.0000). In the HG prediction task, the AUC value of the proposed scheme in the test cohort was 0.937 (95% CI 0.8561-1.0000). In the ER prediction task, the AUC value of the proposed scheme in the test cohort was 0.929 (95% CI 0.7991-1.0000). The proposed radiomics scheme outperformed the comparative scheme and effectively extracted imaging features related to the expression of EC biological characteristics, providing potential clinical significance for accurate diagnosis and treatment decision-making.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952987","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}