Abdominal Radiology最新文献

{"title":"Differentiating uterine adenosarcoma from endometrial polyps: MRI imaging features.","authors":"Kumi Harada, Yasuhisa Kurata, Yuki Himoto, Aki Kido, Mitsuhiro Kirita, Atsushi Yoshida, Yuka Kuriyama Matsumoto, Junzo Hamanishi, Sachiko Minamiguchi, Hiroaki Ito, Akitoshi Nakashima, Takashi Minamisaka, Sayaka Daido, Kaoru Abiko, Koki Moriyoshi, Kumiko Ando, Shigeki Arizono, Takuya Aoki, Shigeo Hara, Masaki Mandai, Yuji Nakamoto","doi":"10.1007/s00261-025-04905-z","DOIUrl":"https://doi.org/10.1007/s00261-025-04905-z","url":null,"abstract":"<p><strong>Objective: </strong>This study was conducted to identify MRI features distinguishing uterine adenosarcoma from endometrial polyps and to predict the presence or absence of sarcomatous overgrowth (SO), a pathological finding linked to poor prognosis.</p><p><strong>Methods: </strong>This multicenter retrospective study included 11 cases of uterine adenosarcoma, with 5 showing SO and 6 without, and 20 cases of endometrial polyps measuring 3 cm or greater diameter. Quantitative evaluations (tumor volume, cystic lesion size, diffusion-weighted imaging [DWI] signal ratio, apparent diffusion coefficient [ADC] value, and normalized ADC value) and qualitative evaluations (degree of hemorrhage, signal intensity on T2-weighted imaging and DWI, number of cystic lesions, degree of contrast enhancement, and tumor localization) were performed. The evaluation parameters were compared between uterine adenosarcoma and endometrial polyp groups, and between SO and non-SO subgroups within uterine adenosarcoma.</p><p><strong>Results: </strong>Uterine adenosarcoma had significantly larger tumor volumes (median 192,324 mm³ vs. 15,717 mm³, p < 0.001), larger cystic lesions (median 17.7 mm vs. 6.7 mm, p = 0.0074), and higher DWI signal ratios (median 1.50 vs. 1.08, p = 0.008) along with significantly lower ADC values (median 961.5 × 10^- 6 mm²/s vs. 1222.3 × 10^- 6 mm²/s, p = 0.048) and normalized ADC values (median 0.34 vs. 0.49, p = 0.001) compared to endometrial polyps. Gross hemorrhage (9/11 vs. 1/20, p < 0.001) and high signal intensity on DWI (10/11 vs. 4/20, p < 0.001) were observed more frequently with uterine adenosarcomas. No significant difference was found between uterine adenosarcomas with SO and without SO for any quantitative or qualitative evaluation parameter.</p><p><strong>Conclusions: </strong>This study identified key imaging features of uterine adenosarcoma, including larger tumor volume, larger cystic lesions, gross intratumoral hemorrhage, higher DWI signal intensity, and lower ADC values. These findings can facilitate preoperative differentiation of uterine adenosarcoma from endometrial polyps.</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":"143955526","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 of a model combining CEUS LI-RADS and clinical features for predicting glypican-3 expression in hepatocellular carcinoma.","authors":"Fen Huang, Jinshu Pang, Yuquan Wu, Yueting Sun, Rong Wen, Xiumei Bai, Wanxian Nong, Ruizhi Gao, Yun He, Cuiling Li, Guangliang Huang, Hong Yang","doi":"10.1007/s00261-025-04861-8","DOIUrl":"https://doi.org/10.1007/s00261-025-04861-8","url":null,"abstract":"<p><strong>Objective: </strong>To establish a predictive model incorporating clinical features and contrast-enhanced ultrasound (CEUS) liver Imaging Reporting and Data System (LI-RADS) for predicting glypican-3 (GPC3) expression in hepatocellular carcinoma (HCC).</p><p><strong>Methods: </strong>A total of 142 HCC patients between January 2020 to June 2021 in our institution were retrospectively analyzed. All patients underwent CEUS before surgery, and the reference standard was immunohistochemical analysis of surgical specimen. The clinical features, conventional ultrasound features, and CEUS LI-RADS features of patients in the GPC3-positive and GPC3-negative groups were evaluated and compared. The variables screened by multivariable logistic regression were used to develop a model for predicting GPC3 expression and the predictive precision and clinical utility of the model was evaluated using receiver operating characteristic analysis and decision curve analysis.</p><p><strong>Results: </strong>Among the 142 HCC patients, 96 (67.6%) were classified as LR-4/5 lesions, 46 (32.4%) were classified as LR-M lesions, 101 (71.1%) were GPC3-positive and 41 (28.9%) were negative. Multivariable logistic regression analysis showed that younger age (OR = 0.947; 95% CI: 0.910-0.985; p = 0.007), alpha-fetoprotein > 400 ng/ml (OR = 5.202; 95% CI: 1.808-14.966; p = 0.002) and LI-RADS M (OR = 2.822; 95% CI: 1.101-7.236; p = 0.031) was independent risk factors for GPC3-positive HCC. The model combining clinical features and LI-RADS categories showed better performance than single variable, with AUC of 0.759 (p < 0.05). The nomogram and decision curves revealed substantial clinical benefit of the prediction model in predicting GPC3 expression.</p><p><strong>Conclusion: </strong>The combined model incorporating clinical features and CEUS LI-RADS achieved a satisfactory performance for predicting GPC3 expression in HCC patients.</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":"143965477","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 nVR model: prediction of LI-RADS treatment response nonviable recurrence of hepatocellular carcinoma after primary transarterial chemoembolization.","authors":"Shuhang Zhang, Weilang Wang, Xiuming Zhang, Binyan Zhong, Feng Feng, Wu Cai, Binrong Li, Varsha Ajith Menon, Shuwei Zhou, Teng Zhang, Xunjun Chen, Shenghong Ju, Yuan-Cheng Wang","doi":"10.1007/s00261-025-04924-w","DOIUrl":"https://doi.org/10.1007/s00261-025-04924-w","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to build a prediction model based on MRI features and clinical characteristics for early tumor recurrence (< 12 months) in lesions which evaluated as LR-TR nonviable at first follow up (1-2 months) after transaterial chemoembolization (TACE).</p><p><strong>Methods: </strong>This multicenter retrospective study included consecutive patients with hepatocellular carcinoma (HCC) who underwent initial TACE from five centers between February 2015 and October 2022. Additionally, patients from a completed clinical trial (NCT03113955) were also included for this retrospective analysis. Nineteen baseline imaging features and six first follow-up imaging features were evaluated for lesions classified as LR-TR nonviable at the first post-TACE imaging assessment. A predictive model integrating clinical and MRI features for early recurrence of LR-TR nonviable lesions was developed with logistic regression analyses and validated on a 5-fold cross-validation. The model's performance was further validated by analyzing relapse-free survival (RFS) using Kaplan-Meier curves.</p><p><strong>Results: </strong>Non-smooth margin (OR: 4.69; 95% CI: 1.91, 11.56; P = 0.001) and peritumoral hyperintensity on T2-weighted imaging or diffusion-weighted imaging (OR: 6.12; 95% CI: 2.53, 14.80; P < 0.001) were identified as independent risk factors for early recurrence of LR-TR nonviable lesions. The non-Viable Recurrence (nVR) model, developed using these two features, achieved area under the receiver operating characteristic curve of 0.759 (0.734, 0.784) in the training cohort and 0.765 (0.632, 0.898) in the validation cohort. Patients were stratified into high-risk and low-risk groups based on the Youden index, with RFS significantly differing between these groups in both the training (P < 0.001) and validation cohorts (P = 0.009).</p><p><strong>Conclusion: </strong>Based on two radiologic features, the nVR model has demonstrated relatively reliable efficacy in predicting early post-TACE recurrence.</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":"143955950","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":"Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.","authors":"Junghwa Choi, Andrew Gordon, Aydin Eresen, Zhuoli Zhang, Amir Borhani, Ulas Bagci, Robert Lewandowski, Dong-Hyun Kim","doi":"10.1007/s00261-025-04916-w","DOIUrl":"https://doi.org/10.1007/s00261-025-04916-w","url":null,"abstract":"<p><p>The tumor microenvironment (TME) of hepatocellular carcinoma (HCC) has garnered significant attention, especially with the rise of immunotherapy as a treatment strategy. Radiomics, an innovative technique, offers valuable insights into the intricate structure of the TME. This review highlights recent advancements in radiomics for analyzing the HCC TME, identifies key areas that warrant further research, and explores comprehensive multi-omics approaches that extend the potential of radiomics to new frontiers.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143956643","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":"Surgery-confirmed internal hernia with or without Roux-en-Y anastomosis: diagnostic performance of six CT signs.","authors":"Hyun Kyung Yang, Joao Baptista Rezende-Neto, Viviane Willig Brasil, Errol Colak","doi":"10.1007/s00261-025-04927-7","DOIUrl":"https://doi.org/10.1007/s00261-025-04927-7","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the diagnostic performance and generalizability of established CT signs of internal hernias across a broad patient population including those with and without Roux-en Y anastomosis.</p><p><strong>Methods: </strong>Our institutional review board approved this retrospective study. CT scans of 21 patients (11 women, 10 men) with surgically confirmed internal hernia and 52 control patients (23 women, 29 men) in whom internal hernia was suspected on CT but subsequently excluded surgically were reviewed. Six CT signs were evaluated: non-duodenal small bowel (ND-SB) behind the superior mesenteric artery (SMA), right-sided jejunojejunal anastomosis in applicable patients, \"swirl\" sign, superior mesenteric vein compression, \"mushroom\" sign, and clustered small bowel (SB) loops. Sensitivity, specificity, and odds ratios with 95% confidence intervals were calculated for each sign using logistic regression.</p><p><strong>Results: </strong>Logistic regression identified the \"mushroom\" sign, clustered SB, and ND-SB behind the SMA as significant independent predictors of internal hernia with an area under the receiver operating characteristic curve of 0.746. The sensitivity, specificity, and odds ratio of the \"mushroom\" sign, clustered SB, and ND-SB behind the SMA were 38.1%, 86.5%, 3.96 (95% CI, 1.21-12.97), 47.6%, 75.0%, 2.73 (95% CI, 0.94-7.89), and 33.3%, 88.2%, 3.75 (95% CI, 1.08-13.02), respectively.</p><p><strong>Conclusion: </strong>The \"mushroom\" sign, clustered SB, and ND-SB behind the SMA are valuable CT findings in diagnosing internal hernias irrespective of Roux-en-Y anastomosis. In particular, ND-SB behind the SMA and the \"mushroom\" sign are highly specific.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960638","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 pitfalls of fixed-ratio data splitting in radiomics model performance evaluation.","authors":"Haoru Wang","doi":"10.1007/s00261-025-04936-6","DOIUrl":"https://doi.org/10.1007/s00261-025-04936-6","url":null,"abstract":"<p><p>Over the past decade, radiomics has seen exponential growth, with over ten thousand publications in PubMed and a steady increase in related studies in journals like Abdominal Radiology. Despite the potential of radiomics, a major challenge lies in validating radiomics models, as most studies rely on single-center datasets with fixed-ratio splits, which can lead to variability in performance due to randomness in data splitting. Therefore, researchers should adopt more robust cross-validation methods rather than relying solely on the fixed-ratio holdout method to ensure robust and reliable radiomics model performance evaluation.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960901","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 of endometriosis-related infertility.","authors":"Kristine S Burk, Hina Arif-Tiwari, Tanya Chawla, Haatal B Dave, Izabela Pires Franco, Pamela Causa-Andrieu, Liina Poder, Luciana P Chamie","doi":"10.1007/s00261-025-04926-8","DOIUrl":"https://doi.org/10.1007/s00261-025-04926-8","url":null,"abstract":"<p><p>Endometriosis is a common cause of subfertility and infertility through myriad mechanisms. Imaging of endometriosis is critical for its diagnosis, characterization, and treatment. Understanding its imaging appearance, surgical management, and implications for assisted reproductive therapy is essential to providing the most clinically relevant and impactful reports in endometriosis patients. This manuscript will review imaging techniques used to diagnose and characterize endometriosis-related infertility. We will describe relevant imaging findings by anatomic site and review the impact of surgery and ART on its appearance.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957242","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-based biomarkers in acute pancreatitis: the predictive value of adrenal contrast ratios for intensive care unit admission.","authors":"Sevde Nur Emir, Hasan Kumru, Gülbanu Güner, Aylin Acar, Tolga Canbak","doi":"10.1007/s00261-025-04931-x","DOIUrl":"https://doi.org/10.1007/s00261-025-04931-x","url":null,"abstract":"<p><strong>Background: </strong>Early risk stratification is crucial in acute biliary pancreatitis (ABP) to optimize patient management and guide intensive care unit (ICU) admission decisions. Traditional biomarkers and scoring systems have limitations in early severity assessment. This study aimed to evaluate the predictive value of adrenal contrast ratios on contrast-enhanced CT (CECT) as imaging-based biomarkers for ICU admission and prolonged hospitalization in ABP patients.</p><p><strong>Methods: </strong>This retrospective study included 288 ABP patients who underwent CECT within 24 h of admission. Adrenal-to-inferior vena cava (IVC) and adrenal-to-spleen contrast ratios were measured from portal venous phase images. The predictive performance of these ratios for ICU admission was assessed using receiver operating characteristic (ROC) analysis, and their correlation with clinical outcomes was evaluated through regression analysis.</p><p><strong>Results: </strong>ICU-admitted patients had significantly higher adrenal contrast ratios compared to non-ICU patients (adrenal-to-IVC ratio: 1.15 vs. 0.99, p < 0.001; adrenal-to-spleen ratio: 0.97 vs. 0.75, p < 0.001). ROC analysis demonstrated strong predictive accuracy (AUC = 0.74 for adrenal-to-IVC, AUC = 0.81 for adrenal-to-spleen). Additionally, adrenal contrast ratios correlated significantly with prolonged hospital stay (r = 0.49-0.55, p < 0.001).</p><p><strong>Conclusion: </strong>Adrenal contrast ratios serve as promising imaging-based biomarkers for early ICU admission prediction and risk stratification in ABP patients. Their integration into clinical decision-making may enhance early management strategies. Further prospective validation is warranted.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958353","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":"Improving radiologists' diagnostic accuracy for lymphovascular invasion in colorectal cancer: insights from a multicenter CT-based study.","authors":"Wenjun Diao, Kaiqi Hou, Xiaobo Chen, Chaokang Han, Suyun Li, Zhishan Wang, Ruxin Xu, Jiayi Liao, Liuyang Yang, Ruozhen Gu, Ge Zhang, Zaiyi Liu, Yanqi Huang","doi":"10.1007/s00261-025-04884-1","DOIUrl":"https://doi.org/10.1007/s00261-025-04884-1","url":null,"abstract":"<p><strong>Background: </strong>The current standard of subjective assessment by radiologists for lymphovascular invasion (LVI) in colorectal cancer (CRC) using CT images often falls short in diagnostic accuracy. This study introduces an advanced CT-based prediction model aimed at providing support for radiologists' assessment to accurately diagnose LVI.</p><p><strong>Methods: </strong>We conducted a retrospective analysis of 1409 patients with pathologically confirmed CRC from four institutions. Radiomics features were extracted from tumor areas on CT images, and Deep Residual Shrinkage Networks with Channel-wise Thresholds (DRSN-CW) algorithm was utilized to build prediction model. We assessed the model's impact on enhancing radiologists' diagnostic performance and employed Shapley Additive Explanation (SHAP) to interpret the influence of key features on predictions.</p><p><strong>Results: </strong>The prediction model achieved strong prediction performance with AUCs of 0.896 (95% CI: 0.866-0.923), 0.849 (0.782-0.908), 0.845 (0.782-0.901) and 0.799 (0.709-0.881) in the training and validation cohorts. Crucially, when informed by the model, radiologists demonstrated a significant improvement in diagnosing LVI. SHAP analysis provided detailed insights into the model's decision-making process, enhancing its clinical relevance. We also observed that patients predicted to be LVI-negative by the model had significantly longer overall survival (OS) compared to those LVI-positive (training cohort, p = 0.012; internal validation cohort, p = 0.046).</p><p><strong>Conclusions: </strong>This study introduces a CT-based prediction model that significantly enhances radiologists' ability to accurately diagnose LVI in CRC. By improving diagnostic accuracy and demonstrating the association between LVI predictions and OS, the model provides a valuable tool for clinical decision-making, with the potential to improve patient outcomes.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962840","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":"Radiomics-based assessment of HER2 status and prognosis in gastric cancer: a retrospective dual-center CT study.","authors":"Manman Li, Shu Jiang, Siyu Zhou, Wang Chen, Yong Xiao, Yigang Fu, Feng Feng, Guodong Xu","doi":"10.1007/s00261-025-04912-0","DOIUrl":"https://doi.org/10.1007/s00261-025-04912-0","url":null,"abstract":"<p><strong>Purpose: </strong>This research investigated the potential of CT-based radiomics analysis for predicting human epidermal growth factor receptor 2 (HER2) status and assessing the prognosis of patients with gastric cancer (GC).</p><p><strong>Methods: </strong>431 patients with GC from two medical centers were included in this retrospective study, with patients allocated to a training cohort (n = 221), a testing cohort (n = 94), and an external validation cohort (n = 116). Radiomics features and clinical variables associated with HER2 status were identified, and the radiomics score was subsequently derived. A radiomics model was constructed using the radiomics score, and a nomogram was developed by integrating related variables. The predictive accuracy of models was assessed via receiver operating characteristic curves, with the area under the curve (AUC) being computed. Prognostic significance was assessed by exploring the association between nomogram-predicted HER2 status and overall survival (OS).</p><p><strong>Results: </strong>The radiomics model yielded AUCs of 0.801, 0.793, and 0.784 for the training, testing, and external validation cohorts, respectively. A nomogram that integrated sex, CA72-4 levels, and radiomics score exhibited enhanced predictive accuracy, achieving AUCs of 0.847, 0.836, and 0.828 across the cohorts. Decision curve analysis highlighted the clinical utility of the nomogram, illustrating its ability to deliver a higher net benefit. In addition, survival analysis indicated that individuals with nomogram-predicted HER2 positivity experienced significantly shorter OS, providing robust risk stratification and prognostic insights.</p><p><strong>Conclusion: </strong>The CT-based radiomics nomogram demonstrated the ability to non-invasively predict preoperative HER2 status and stratify prognostic risk in this GC cohort.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802126","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}