Abdominal Radiology最新文献

{"title":"Correction to: Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs","authors":"Andrea M. Bejar, Maria Jaramillo Gonzalez, Ziliang Hong, Gorkem Durak, Elif Keles, Halil Ertugrul Aktas, Zheyuan Zhang, Hongyi Pan, Zeynep Sue Jozwiak, Fergan Bol, Lili Zhao, Chao Chen, Concetto Spampinato, Alpay Medetalibeyoglu, Sukru Mehmet Erturk, Gulbiz Dagoglu Kartal, Yury Velichko, Emil Agarunov, Ziyue Xu, Sachin Jambawalikar, Ivo G. Schoots, Marco J. Bruno, Chenchan Huang, Tamas Gonda, Candice Bolan, Frank H. Miller, Michael B. Wallace, Rajesh N. Keswani, Pallavi Tiwari, Ulas Bagci","doi":"10.1007/s00261-026-05466-5","DOIUrl":"10.1007/s00261-026-05466-5","url":null,"abstract":"","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"5333 - 5334"},"PeriodicalIF":2.3,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00261-026-05466-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147535329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-layer detector spectral CT–derived extracellular volume fraction combined with clinical parameters for predicting lymphovascular and perineural invasion in gastric cancer","authors":"Rui Guo,&nbsp;Ke Chang,&nbsp;QingHong Yan,&nbsp;Xinyue Xu,&nbsp;Peng Ma,&nbsp;YunHang Jing","doi":"10.1007/s00261-026-05475-4","DOIUrl":"10.1007/s00261-026-05475-4","url":null,"abstract":"<div><h3>Objective</h3><p>This study aimed to evaluate the feasibility and diagnostic performance of extracellular volume fraction (ECV), derived from iodine concentration (IC) values obtained through dual-layer detector spectral CT, in combination with clinical parameters, for preoperative prediction of lymphovascular invasion and/or perineural invasion (LVI/PNI) in gastric cancer.</p><h3>Methods</h3><p>This retrospective study included clinical and pathologic data of 124 patients diagnosed with GC at our hospital between September 2023 and February 2025. The patients were stratified into LVI/PNI-positive (<i>n</i> = 85) and LVI/PNI-negative (<i>n</i> = 39) groups based on their postoperative histopathologic findings. All patients underwent contrast-enhanced dual-layer detector spectral CT. Preoperative hematocrit levels were obtained, and IC values within the tumor were measured during the delayed phase. Concurrently, IC in the abdominal aorta at the same anatomical level was measured to calculate the ECV using the formula: ECV = (1 − hematocrit) × (IC_tumor / IC_blood). Clinical variables and spectral CT parameters were analyzed using univariate analysis, LASSO selection, and multivariable logistic regression to identify independent predictors of the composite LVI/PNI endpoint. The diagnostic performance of individual parameters and the combined model for preoperatively predicting vascular and neural invasion was assessed using receiver operating characteristic (ROC) curve analysis.</p><h3>Results</h3><p>ECV, CT-N stage, and Borrmann classification were identified as independent predictors of the composite LVI/PNI endpoint in patients with GC (all <i>P</i> &lt; 0.05). Among the candidate models, the combined model incorporating ECV, CT-N stage, and Borrmann classification achieved the best predictive performance, with AUCs of 0.920 in the training cohort and 0.917 in the validation cohort. DeLong analysis showed that the combined model significantly outperformed the CT-N stage model and the Borrmann classification model in both cohorts. However, its improvement over the ECV-only model was not statistically significant.</p><h3>Conclusions</h3><p>Delayed-phase dual-layer detector spectral CT-derived ECV is a promising noninvasive biomarker for identifying the composite LVI/PNI endpoint in gastric cancer. A combined model integrating ECV, CT-N stage, and Borrmann classification provides the best overall diagnostic performance, although its incremental benefit over ECV alone appears limited.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"4802 - 4812"},"PeriodicalIF":2.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147535428","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":"DWI-derived intratumoral, peritumoral, and habitat features for preoperative prediction of lymph node metastasis in early-stage cervical cancer using machine learning method","authors":"Tao Yang,&nbsp;Tianhui Zhang,&nbsp;Weihao Yan,&nbsp;Xian Chen,&nbsp;Shujian Li,&nbsp;Zhijun Ye,&nbsp;zi Yang,&nbsp;zhihan Yan,&nbsp;Xue Wang","doi":"10.1007/s00261-026-05463-8","DOIUrl":"10.1007/s00261-026-05463-8","url":null,"abstract":"<div><h3>Objectives</h3><p>This study aimed to develop a novel radiomic model by incorporating features from both habitat subregions and peritumoral regions to preoperatively predict lymph node metastasis (LNM) in early-stage cervical cancer using diffusion-weighted imaging (DWI).</p><h3>Methods</h3><p>433 early-stage cervical cancer patients from four hospitals undergoing DWI were enrolled. Peritumoral regions were delineated by 1–4 mm expansion, and habitat analysis identified two intratumoral subregions, named Habitat 1 and Habitat 2 respectively. Intratumoral, peritumoral, and habitat features were extracted for model development. Prediction models included: Intra, Peri 1–4 mm, Habitat (1 and 2), and Fusion model. Performance was assessed via receiver operating characteristic curve, calibration, and decision curve analyses.</p><h3>Results</h3><p>Among the peritumoral models, the 3 mm peritumoral model demonstrated the best performance for LNM prediction, with AUCs of 0.867 (95% CI: 0.805–0.929), 0.747 (95% CI: 0.608–0.886), and 0.815 (95% CI: 0.743–0.887) in the training, validation, and test set, respectively. The Habitat 1 model also showed favorable performance, achieving AUCs of 0.838 (95% CI: 0.774–0.901), 0.712 (95% CI: 0.556–0.867), and 0.782 (95% CI: 0.694–0.869) in the training, validation, and test groups, respectively. Notably, Fusion model, combining Peri 3 mm and Habitat 1 features, achieved the best overall performance, with AUCs of 0.910 (95% CI: 0.868–0.953), 0.747 (95% CI: 0.600–0.894), and 0.837 (95% CI: 0.767–0.907) across the training, validation, and test sets, respectively and outperformed other models in calibration and decision curve analyses.</p><h3>Conclusion</h3><p>The Fusion model enables superior and noninvasive prediction of LNM in early-stage cervical cancer patients.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"5222 - 5234"},"PeriodicalIF":2.3,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147535432","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":"Demystifying the imaging diagnoses of sclerosing cholangitis","authors":"Divij Agarwal,&nbsp;Shivani Sharma,&nbsp;Yashant Aswani,&nbsp;Rimlee Dutta,&nbsp;Rajesh V. Helavar,&nbsp;Sai Krishna Gadwal,&nbsp;Chandan J. Das","doi":"10.1007/s00261-026-05397-1","DOIUrl":"10.1007/s00261-026-05397-1","url":null,"abstract":"<div><p>Sclerosing cholangitis encompasses a group of conditions eventually leading to chronic cholestasis, and are characterized by inflammation, fibrosis, and segmental strictures of the intrahepatic and/or extrahepatic ducts. These can be either primary or secondary in origin. Primary sclerosing cholangitis (PSC) has no identifiable cause, while secondary sclerosing cholangitis (SSC) has various causes, producing similar chronic progressive changes in the biliary ducts. It is vital to differentiate between the two, as PSC is difficult to treat (with liver transplantation being the definitive treatment), while many causes of SSC are treatable, and even reversible. Therefore, it is imperative to exclude secondary causes of sclerosing cholangitis before establishing the diagnosis of PSC. While MRCP is the non-invasive imaging gold standard, ERCP, clinical correlation, and biopsy are frequently required to establish a definitive diagnosis. This review article presents a systematic approach to the imaging-based differential diagnoses of sclerosing cholangitis, integrating key clinical, laboratory, and radiological features.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"4902 - 4919"},"PeriodicalIF":2.3,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518241","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":"Preoperative CT-derived extracellular volume fraction and volumetry can predict the post-hepatectomy liver failure and overall survival of patients with hepatocellular carcinoma after resection","authors":"Yali Cai,&nbsp;Jiyang Liu,&nbsp;Xiaoqi Sun,&nbsp;Huaijie Cai,&nbsp;Jieyun Chen","doi":"10.1007/s00261-026-05468-3","DOIUrl":"10.1007/s00261-026-05468-3","url":null,"abstract":"<div><h3>Objective</h3><p>To develop and validate nomogram models based on preoperative CT-derived extracellular volume fraction (ECV) and volumetry for predicting post-hepatectomy liver failure (PHLF) and overall survival (OS) in hepatocellular carcinoma (HCC) patients.</p><h3>Methods</h3><p>Between January 2010 and June 2020, a total of 265 patients with resectable HCC were retrospectively enrolled and divided into a training cohort (<i>n</i> = 157) and an independent validation cohort (<i>n</i> = 108). CT-derived ECV, spleen volume (SV), liver volume (LV), future liver remnant volume (FLRV), body surface area (BSA) and ratios (SV/LV, FLRV/LV, LV/BSA, SV/BSA) were analyzed. Independent predictors for PHLF and OS were identified to construct nomograms. Model performance was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).</p><h3>Results</h3><p>Of 265 patients, 77 (29.0%) developed PHLF. Median follow-up was 63 months (49–66 months), with 1-/3-/5-year OS rates of 95.5%, 83.4%, and 68.7%. The nomogram for PHLF prediction, which integrated ECV (<i>P</i> &lt; 0.001), FLRV/LV (<i>P</i> = 0.002), and SV/BSA (<i>P</i> = 0.014), demonstrated superior discrimination than conventional clinical models in both cohorts. The nomogram for OS prediction, combining ECV (<i>P</i> &lt; 0.001) and AST (<i>P</i> = 0.006), also outperformed conventional clinical models in both cohorts.</p><h3>Conclusions</h3><p>The nomograms contributes to the preoperative prediction of PHLF (combining ECV, FLRV/LV, and SV/BSA) and OS (combining ECV and AST) in patients with resectable HCC.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"4890 - 4901"},"PeriodicalIF":2.3,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518225","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":"Artificial intelligence and radiomics in bladder cancer MRI: a scoping review of applications, performance, and barriers to clinical translation","authors":"Giuseppe Salvaggio,&nbsp;Albert Comelli,&nbsp;Domenico Albano,&nbsp;Massimo Galia,&nbsp;Neeraj Lalwani","doi":"10.1007/s00261-026-05457-6","DOIUrl":"10.1007/s00261-026-05457-6","url":null,"abstract":"<div><p>Artificial intelligence (AI) and radiomics show significant potential to augment bladder cancer (BC) MRI but face a critical translational gap. This scoping review of 79 studies maps a rapidly growing field dominated by retrospective, single-center designs lacking external validation. While diagnostic performance for staging is promising (AUC up to 0.99 in ideal settings), robust multi-center studies report more realistic AUCs of 0.85–0.95, positioning AI as a complement to VI-RADS, especially for equivocal cases. Evidence supports integration via simplified protocols (T2w-only) and workflow tools. However, systemic barriers impede progress: poor reporting transparency, a near-total absence of prospective utility trials, and a narrow focus on pre-treatment diagnosis. The current evidence suggests a high risk of translational stagnation. Realizing the transformative potential requires a fundamental reorientation toward rigorous, multi-center evaluation and transparency. Priorities must include mandatory adherence to reporting standards (TRIPOD + AI), the creation of public multi-center benchmarks, and a redesign of validation paradigms to prioritize prospective studies measuring clinical utility - such as impact on treatment decisions - over retrospective diagnostic accuracy alone. Only by prioritizing transparency, interdisciplinary collaboration, and patient-centered outcomes can AI evolve from a promising research instrument into a reliable clinical tool for BC management.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"5099 - 5112"},"PeriodicalIF":2.3,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518229","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":"From conventional to next-generation diagnostics in cystic echinococcosis: integrating serology, ultrasonography, and molecular tools: a review","authors":"Mitra Salehi,&nbsp;Seyedeh Nastaran Asadzadeh,&nbsp;Mostafa Gholamrezaei,&nbsp;Shahrzad Havakhah,&nbsp;Nima Firouzeh","doi":"10.1007/s00261-026-05447-8","DOIUrl":"10.1007/s00261-026-05447-8","url":null,"abstract":"<div><p>This review provides the relative roles of serology and ultrasonography in the diagnosis of cystic echinococcosis (CE), emphasizing their performance, limitations, and integration within current and emerging diagnostic strategies. CE is a chronic zoonotic disease caused by <i>Echinococcus granulosus</i>, predominantly affecting the liver and lungs in endemic livestock-raising regions. It is often a silent clinical course, variable clinical presentation, and potential for severe complications that contribute to underdiagnosis and underreporting. Ultrasonography is the first-line tool for hepatic CE, enabling cyst detection and staging according to the WHO-IWGE classification (CE1–CE5) and guiding clinical management. Serological assays (e.g., ELISA, IHA, Western blot) detect antibodies against <i>E. granulosus</i> antigens and perform best for active hepatic cysts (CE1–CE3). Their sensitivity is reduced in inactive or heavily calcified cysts (CE4–CE5), and specificity is limited by cross-reactivity with other helminths and by inter-individual variation in immune responses. Ultrasonography offers high sensitivity and specificity for hepatic cysts, yet is operator-dependent and less effective for small or extrahepatic cysts. Combined use of serology and ultrasonography, as recommended by WHO and CDC algorithms, improves overall diagnostic accuracy and follow-up. Advances in recombinant antigen–based serology, point-of-care tests, portable ultrasound, AI-assisted imaging, and molecular methods (PCR, NGS, and parasite-derived biomarkers) are enabling more sensitive, specific, and accessible CE diagnostics, particularly in resource-limited endemic settings.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"5269 - 5278"},"PeriodicalIF":2.3,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518231","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":"The diagnostic value of magnetic resonance defecography in differentiating evacuation disorders among patients with manometric dyssynergia","authors":"Esin Ölçücüoğlu,&nbsp;İlyas Tenlik,&nbsp;Muhammet Batuhan Gökhan,&nbsp;Halil Tekdemir,&nbsp;Yasin Celal Güneş,&nbsp;Eren Çamur,&nbsp;Mercan Taştemur,&nbsp;Ömer Öztürk","doi":"10.1007/s00261-026-05465-6","DOIUrl":"10.1007/s00261-026-05465-6","url":null,"abstract":"<div><h3>Purpose</h3><p>This study aimed to evaluate patients exhibiting a dyssynergic pattern on anorectal manometry (ARM) using Magnetic Resonance Defecography (MRD) to distinguish “true” Dyssynergic Defecation (DD) from structural Non-Dyssynergic Evacuation Disorders (NDED) and to identify the most reliable radiological diagnostic criteria.</p><h3>Materials and methods</h3><p>This retrospective cross-sectional study included 252 patients who exhibited a dyssynergic pattern on ARM. Patients were re-evaluated using MRD and classified into two groups: those confirmed as DD and those reclassified as NDED. The prevalence of structural abnormalities (rectocele, cystocele, descent of the anorectal Junction [DAJ]) and the diagnostic accuracy of MRD parameters (incomplete evacuation, paradoxical sphincter contraction with abnormal anorectal angle [ARA] changes, absence of perineal descent) were analyzed.</p><h3>Results</h3><p>Of the 252 patients diagnosed with dyssynergia by ARM, only 142 (56.3%) were confirmed as DD by MRD, while 110 (43.7%) were diagnosed with NDED. DD was significantly more prevalent in males (69.2%, <i>p</i> = 0.006), whereas structural pathologies were predominant in the NDED group. The presence of cystocele, rectocele, and DAJ significantly reduced the likelihood of a DD diagnosis (<i>p</i> &lt; 0.001). Among diagnostic criteria, the combination of “paradoxical sphincter contraction with abnormal ARA” demonstrated the highest diagnostic validity (Sensitivity: 97.1%, Specificity: 99.1%, Accuracy: 98%). In contrast, incomplete evacuation alone showed high sensitivity (94.3%) but low specificity (45.4%).</p><h3>Conclusion</h3><p>ARM alone is insufficient for diagnosis due to high false-positive rates, particularly in patients with structural abnormalities. MRD plays a crucial complementary role in identifying ‘true’ DD and accurately distinguishing it from structural evacuation disorders. This integrated approach prevents unnecessary biofeedback therapy for patients requiring structural correction.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 9","pages":"4560 - 4571"},"PeriodicalIF":2.3,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518234","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":"Deep learning reconstruction enables accelerated T2-weighted MRI for rectal cancer staging: a prospective study of diagnostic consistency across NEX value reduction","authors":"Weijie Yan,&nbsp;Shuang Li,&nbsp;Huilou Liang,&nbsp;Bo Zhang,&nbsp;Miaoqi Zhang,&nbsp;Bing Wu,&nbsp;Xin Fang","doi":"10.1007/s00261-026-05460-x","DOIUrl":"10.1007/s00261-026-05460-x","url":null,"abstract":"<div><h3>Objectives</h3><p>To evaluate the image quality, interpretation consistency, and scanning efficiency of deep learning-based reconstruction (DLR) algorithm (AIR™ Recon DL; GE Healthcare) compared with conventional reconstruction (ConR) in T2-weighted MRI for rectal cancer across different number of excitations (NEX) values.</p><h3>Methods</h3><p>This prospective study enrolled consecutive patients undergoing MRI for primary staging of rectal cancer between July 2022 and April 2023. Each patient underwent T2-weighted MRI with three NEX values (4, 2, and 1), reconstructed using both ConR and DLR methods, generating six image sets. Image quality was assessed quantitatively (signal-to-noise ratio, contrast-to-noise ratio) and qualitatively using a 5-point scale for tissue contrast, edge sharpness, rectal wall morphology, tumor characteristics, overall image quality, and noise. Two radiologists independently evaluated staging parameters including T stage, N stage, extramural vascular invasion (EMVI), and mesorectal fascia (MRF) status. Inter-observer and inter-sequence agreement were analyzed.</p><h3>Results</h3><p>Thirty-five patients completed all six imaging protocols. Acquisition times were 3 min 55s (NEX = 4), 2 min 1s (NEX = 2, 49% reduction), and 1 min 4s (NEX = 1, 73% reduction). For qualitative assessment, DLR sequences demonstrated significantly superior image quality compared to ConR sequences across all NEX values (all <i>P</i> &lt; 0.001). Among all sequences, FSE_NEX2−DLR achieved the highest qualitative scores for tissue contrast, edge sharpness, tumor characteristics, and overall image quality. Quantitative analysis showed FSE_NEX4−DLR demonstrated the highest SNR and CNR values, followed by FSE_NEX2−DLR, with all DLR sequences significantly outperforming their ConR counterparts (all <i>P</i> &lt; 0.001). Inter-sequence agreement analysis revealed substantial to almost perfect consistency for all staging parameters (mean κ: 0.736-0.800). DLR sequences demonstrated significantly higher inter-sequence agreement (range: 0.715–0.964) compared to ConR sequences (range: 0.505–0.811).</p><h3>Conclusion</h3><p>DLR with reduced NEX values maintains diagnostic consistency in rectal cancer staging. FSE_NEX2−DLR, achieving 49% scanning time reduction while preserving diagnostic consistency, may offer a reasonable balance between acquisition efficiency and image quality. Future studies with histopathological correlation are needed to validate diagnostic accuracy.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"4790 - 4801"},"PeriodicalIF":2.3,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518219","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":"Agreement of axial T2-MRI versus Contrast-Enhanced CT for solid renal mass active surveillance","authors":"Bendik Breen,&nbsp;Khalefa Alghofaily,&nbsp;Heidi Søgaard Christensen,&nbsp;Jens Brøndum Frøkjær,&nbsp;Ole Graumann,&nbsp;Mats Kleivane,&nbsp;Lise Medrud,&nbsp;Rasa Mikalone,&nbsp;Stig Müller,&nbsp;Gintare Naujokaite,&nbsp;Anne Negård,&nbsp;Ivar Mjåland Salte,&nbsp;Nicola Schieda,&nbsp;Jens Borgbjerg","doi":"10.1007/s00261-026-05376-6","DOIUrl":"10.1007/s00261-026-05376-6","url":null,"abstract":"<div><h3>Purpose</h3><p>Active surveillance (AS) of small renal masses (SRMs) typically relies on contrast-enhanced CT for serial size measurement. Non-contrast (NC) MRI protocols may provide an alternative. To assess the agreement of maximum axial tumor diameter and tumor nearness between NC MRI and contrast-enhanced CT for SRM surveillance, and to compare diagnostic confidence in tumor delineation across modalities.</p><h3>Methods</h3><p>We retrospectively identified 50 patients (mean age, 62.9 years; 44% women) who underwent both contrast-enhanced CT and axial T2-weighted MRI within 3 months as part of AS protocol. Nine radiologists from five hospitals independently assessed maximum tumor diameter (axial plane) and tumor nearness to the collecting system (TN, three-tier scale) and subjectively graded their diagnostic confidence in delineating the tumor contour (DC, 5-point scale) in 50 CT and MRI scans across four randomized reading sessions. Observer agreement for diameter assessment was quantified using the limits of agreement with the mean (LOAM), whereas TN and DC were analyzed using Gwet’s AC2.</p><h3>Results</h3><p>Mean tumor diameters were 19.9 mm for CT and 19.5 mm for MRI (mean difference, 0.4 mm; <i>p</i> = 0.051). Reproducibility LOAMs were ± 2.9 [2.7–3.2] mm (CT) and ± 3.1 [2.9–3.4] mm (MRI), with overlapping confidence intervals. TN ratings and agreement were similar for CT and MRI (<i>p</i> = 0.61; <i>p</i> = 0.86, respectively), whereas DC ratings were higher and interobserver agreement was better for CT (<i>p</i> &lt; 0.001 and <i>p</i> = 0.009, respectively)..</p><h3>Conclusion</h3><p>NC axial T2-weighted MRI demonstrated tumor diameter agreement comparable to contrast-enhanced CT, along with comparable TN ratings and agreement, whereas CT yielded higher DC and better interobserver agreement for DC.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"51 10","pages":"4779 - 4789"},"PeriodicalIF":2.3,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00261-026-05376-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}