European Journal of Radiology最新文献

Corrigendum to “Short occipital circulation time derived from quantitative digital subtraction angiography is associated with headache risk in patients with unruptured brain arteriovenous malformations” [Eur. J. Radiol. 192 (2025) 112402] “定量数字减影血管造影获得的枕循环时间短与未破裂的脑动静脉畸形患者的头痛风险相关”的更正[欧洲]。[j]放射学杂志，1992 (2025)112402 [j]

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-13 DOI: 10.1016/j.ejrad.2026.112719

Yong-Sin Hu , Jr-Wei Wu , Huai-Che Yang , Hsiu-Mei Wu , Cheng-Chia Lee , Feng-Chi Chang , Kang-Du Liu , Chung-Jung Lin

引用次数: 0

The value of a multimodal ultrasound radiomics-based nomogram in predicting central lymph node metastasis of papillary thyroid microcarcinoma 基于多模态超声放射组学的影像学图预测甲状腺乳头状微癌中央淋巴结转移的价值。

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.ejrad.2026.112725

Minhao Lin , Xiaohong Xu , Jianling Peng , Qiuxia Huang , Jiajian Wu , Lijuan Liu

Objectives

This study aimed to develop a multimodal ultrasound-based nomogram integrating radiomic features from grayscale ultrasound (GSUS) and contrast-enhanced ultrasound (CEUS) with clinical-ultrasound factors for the noninvasive prediction of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC), and to evaluate its predictive performance to support clinical decision-making.

Methods

This retrospective cohort study included 449 pathologically confirmed PTMC patients from June 2023 to December 2024, randomly divided into training (n = 314) and validation (n = 135) cohorts. Radiomic features were extracted using PyRadiomics software, and feature selection was performed through Spearman correlation analysis and LASSO regression. Multivariate regression analysis identified independent clinical risk factors for CLNM. A multimodal ultrasound combined model was then developed, serving as the basis for the nomogram. The model’s discriminative ability, calibration performance, and clinical utility were evaluated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).

Results

Multivariate analysis identified male sex, age < 40 years, and capsular invasion as independent risk factors for CLNM. Single-modal models (Clinical, GSUS, CEUS) achieved AUCs ranging from 0.654 to 0.787 in the validation cohort. The Combined model integrating these features significantly outperformed all single-modal ones, with AUCs of 0.925 and 0.885 in the training and validation cohorts. Calibration curves and DCA confirmed its good fit and high clinical net benefit.

Conclusion

We successfully developed and validated a nomogram model based on multimodal ultrasound features for accurately predicting CLNM risk in PTMC patients, highlighting the value of radiomics in clinical risk assessment.

目的：本研究旨在建立一种基于多模态超声的影像学图，将灰度超声（GSUS）和对比增强超声（CEUS）的放射学特征与临床超声因素结合起来，用于无创预测甲状腺乳头状微癌（PTMC）患者的中央淋巴结转移（CLNM），并评估其预测性能，以支持临床决策。方法：本回顾性队列研究纳入2023年6月至2024年12月病理证实的PTMC患者449例，随机分为训练组（n = 314）和验证组（n = 135）。利用PyRadiomics软件提取放射组学特征，通过Spearman相关分析和LASSO回归进行特征选择。多因素回归分析确定了CLNM的独立临床危险因素。然后开发了一个多模态超声组合模型，作为nomogram的基础。采用受试者工作特征（ROC）分析、校准曲线和决策曲线分析（DCA）评估模型的判别能力、校准性能和临床实用性。结论：我们成功建立并验证了基于多模态超声特征的nomogram模型，该模型可准确预测PTMC患者的CLNM风险，突出了放射组学在临床风险评估中的价值。

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引用次数: 0

Interpretable machine learning based on intratumoral and peritumoral ultrasound radiomics for predicting central lymph node metastasis in papillary thyroid carcinoma 基于肿瘤内和肿瘤周围超声放射组学的可解释机器学习预测甲状腺乳头状癌中央淋巴结转移。

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.ejrad.2026.112727

Wanting Yang , Xuejiao Su , Can Yue, Weizheng Chen, Yang Chen, Yan Luo, Buyun Ma

Objectives

This retrospective and single-center study aimed to develop machine learning (ML) model integrating clinical features, ultrasound (US) features, and radiomics signatures extracted from both intratumoral and peritumoral regions to predict central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC). The SHapley Additive exPlanations (SHAP) method was applied to visualize the prediction process and enhance clinical interpretability.

Materials and methods

A total of 879 patients with PTC who underwent preoperative US examination between January 2023 and January 2024 were retrospectively analyzed. Patients were randomly divided into training (n = 615) and test (n = 264) sets. Radiomics signatures were extracted from intratumoral regions and peritumoral regions extending 3 mm and 5 mm beyond the tumor margin. After feature selection, Radscore were computed. Five ML models incorporating clinical features, US features and Radscore were developed. Model performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curves, and the Hosmer–Lemeshow test. SHAP was used to explain ML model predictions.

Results

CLNM occurred in approximately 52% of PTC. Patients with CLNM were younger and more often male (p < 0.001). Multifocal tumors, extrathyroidal extension, and suspicious lymph nodes on US were also associated with higher CLNM risk (p < 0.05). The Radscore derived from intratumoral and peritumoral regions were significantly different between patients with and without CLNM (p < 0.05). Combined ML models outperformed those based on clinical and US features (p < 0.05). The best performing model (XGB) achieved an AUC of 0.868 (sensitivity = 0.777, specificity = 0.803 and accuracy = 0.749) in the training set and an AUC of 0.787 (sensitivity = 0.704, specificity = 0.695 and accuracy = 0.713) in the test set. The XGB model demonstrated superior clinical utility and well-calibrated for CLNM prediction. SHAP analysis identified the Radscore from the combination of intratumoral and 3-mm peritumoral regions as the most CLNM predictor and provided patient-level interpretability.

Conclusions

Intratumoral and peritumoral radiomics features based on US show potential for predicting CLNM in PTC. The integration of SHAP analysis enhances model transparency and may support individualized treatment decision-making.

目的：本回顾性单中心研究旨在建立机器学习（ML）模型，整合临床特征、超声（US）特征以及从肿瘤内和肿瘤周围区域提取的放射组学特征，以预测甲状腺乳头状癌（PTC）的中央淋巴结转移（CLNM）。应用SHapley加性解释（SHAP）方法可视化预测过程，提高临床可解释性。材料与方法：回顾性分析2023年1月至2024年1月行术前超声检查的879例PTC患者。患者随机分为训练组（n = 615）和测试组（n = 264）。放射组学特征从肿瘤内区域和肿瘤周围区域提取，分别超出肿瘤边缘3毫米和5毫米。特征选择完成后，计算Radscore。结合临床特征、US特征和Radscore建立了5个ML模型。采用受试者工作特征（ROC）曲线、决策曲线分析（DCA）、校正曲线和Hosmer-Lemeshow检验评估模型的性能。SHAP用于解释ML模型预测。结果：约52%的PTC发生CLNM。结论：基于US的肿瘤内和肿瘤周围放射组学特征显示了预测PTC中CLNM的潜力。SHAP分析的整合提高了模型的透明度，并可能支持个性化的治疗决策。

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引用次数: 0

Differentiating large-duct pancreatic ductal adenocarcinoma from malignant intraductal papillary mucinous neoplasm: MRI characteristics and diagnostic implications 鉴别大导管胰管腺癌与恶性导管内乳头状粘液瘤：MRI特征及诊断意义。

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-18 DOI: 10.1016/j.ejrad.2026.112735

Se Jin Choi , Dong Wook Kim , Byung-Kwan Jeong , Seung-Mo Hong , Jae Ho Byun , Seung Soo Lee , Hyoung Jung Kim , Jin Hee Kim , Ki Byung Song , Jae Hoon Lee , Dae Wook Hwang

Objective

To evaluate magnetic resonance imaging (MRI) characteristics that differentiate large-duct pancreatic ductal adenocarcinoma (LD-PDAC) from malignant intraductal papillary mucinous neoplasm (IPMN).

Materials and Methods

We retrospectively analyzed preoperative MRI data from 42 LD-PDAC patients, 201 malignant IPMN patients (166 with high-grade dysplasia and 35 with invasive carcinoma), and 8 LD-PDAC arising from IPMN patients. Two radiologists independently assessed MRI features including tumor morphology and accompanying imaging features. Multivariable logistic regression, diagnostic performance of combined imaging predictors, and survival outcomes across disease entities were evaluated.

Results

LD-PDAC predominantly appeared as solid-dominant tumors, either solid masses with internal cystic portions (69.0%) or pure solid masses (16.7%). Malignant IPMN presented mainly as cyst-dominant tumors, either pure cystic masses (55.2%) or cystic masses with internal solid components (42.8%). Multivariable logistic regression analysis identified solid-dominant tumor morphology (odds ratio [OR], 77.89; 95% confidence interval [CI], 4.94–1229.16), peripancreatic infiltration (OR, 34.47; 95% CI, 2.49–476.79), and absence of disproportionate pancreatic duct dilatation (OR, 0.06; 95% CI, 0.01–0.59) as independent imaging features favoring LD-PDAC. LD-PDAC showed significantly shorter overall and recurrence-free survival than malignant IPMN (p < 0.001), while survival did not differ significantly between LD-PDAC and IPMN with associated invasive carcinoma. Overall, 26.2% of LD-PDAC cases were misdiagnosed, mainly due to misinterpretation of cyst wall characteristics and atypical duct dilatation.

Conclusion

MRI may aid in differentiating LD-PDAC from malignant IPMN by integrating tumor morphology and accompanying imaging features, including pancreatic ductal dilatation patterns and peripancreatic infiltration; however, substantial imaging overlap persists, resulting in a clinically meaningful misdiagnosis rate.

目的：探讨大导管胰腺导管腺癌（LD-PDAC）与恶性导管内乳头状粘液瘤（IPMN）的磁共振成像（MRI）特征。材料和方法：回顾性分析42例LD-PDAC患者的术前MRI资料，201例恶性IPMN患者（166例高级别发育不良，35例浸润性癌），8例IPMN患者的LD-PDAC。两名放射科医生独立评估MRI特征，包括肿瘤形态和伴随的影像学特征。对多变量逻辑回归、综合影像学预测指标的诊断性能和跨疾病实体的生存结果进行了评估。结果：LD-PDAC主要表现为实性肿瘤，实性肿块伴内部囊性部分（69.0%）或纯实性肿块（16.7%）。恶性IPMN主要表现为囊肿性肿瘤，可为纯囊性肿块（55.2%）或囊性肿块伴内部实性成分（42.8%）。多变量logistic回归分析确定了实体优势肿瘤形态（优势比[OR]， 77.89； 95%可信区间[CI]， 4.94-1229.16）、胰腺周围浸润（OR, 34.47; 95% CI, 2.49-476.79）和没有不成比例的胰管扩张（OR, 0.06; 95% CI, 0.01-0.59）是支持LD-PDAC的独立影像学特征。与恶性IPMN相比，LD-PDAC的总生存期和无复发生存期明显缩短(p结论：MRI可以通过整合肿瘤形态和伴随的影像学特征，包括胰腺导管扩张模式和胰腺周围浸润，帮助区分LD-PDAC与恶性IPMN，但大量的影像学重叠仍然存在，导致临床上有意义的误诊率。

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引用次数: 0

Gallbladder adenomyomatosis revisited – Does size matter? is follow-up required for large lesions? 胆囊腺肌瘤病再诊——大小重要吗？大病变需要随访吗？

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-11 DOI: 10.1016/j.ejrad.2026.112698

Shirley Shechter, Dana Ben-Ami Shor, Roie Tzadok, Hila Yashar, Sapir Lazar, Yuval Katz, Arthur Chernomorets, Rivka Kessner

Objectives

Adenomyomatosis (ADM) is generally considered a benign condition. However, it can be associated with chronic cholecystitis − a known risk factor for gallbladder cancer. Therefore, studies have proposed follow-up with ultrasound for asymptomatic patients with focal ADM. Currently, there are no formal recommendations regarding the frequency and length of follow-up. The aims of this study were to assess the growth of ADM lesions during follow-up and to examine the differences between larger and smaller ADM lesions.

Methods

144 patients who underwent MRI-MRCP at our institution between the years 2014–2024 were identified through radiological reports as having a diagnosis of ADM. 43 patients had more than one examination. Demographic, clinical and radiological data were collected retrospectively. We divided the cohort into two groups based on the primary lesion size (axial diameter below or above 1.5 cm) and compared between them.

Results

The group of small lesions included 98 patients and the larger lesions group included 46 patients. We did not find a statistically significant correlation between the size of.

ADM and the demographic or clinical characteristics examined. Only 9 ADM lesions grew during follow-up − 6 from the smaller lesions group and 3 from the large lesions group (p > 0.05). The median follow-up period was 35 months. None of our patients developed gallbladder carcinoma.

Conclusions

Our results confirm the common hypothesis that ADM are benign lesions. Therefore, we believe that follow-up is not needed for lesions with a clear diagnosis of focal ADM.

目的：腺肌瘤病（ADM）通常被认为是一种良性疾病。然而，它可能与慢性胆囊炎有关，而慢性胆囊炎是胆囊癌的已知危险因素。因此，有研究建议对无症状的局灶性adm患者进行超声随访。目前，关于随访的频率和时间没有正式的建议。本研究的目的是评估ADM病变在随访期间的生长情况，并检查较大和较小ADM病变之间的差异。方法：144例2014-2024年间在我院接受MRI-MRCP检查的患者通过影像学报告确诊为adm， 43例患者进行了一次以上检查。回顾性收集人口统计学、临床和放射学资料。我们根据原发病变大小（轴向直径小于或大于1.5 cm）将队列分为两组并进行比较。结果：小病变组98例，大病变组46例。我们没有发现统计上显著的相关性。ADM与人口学或临床特征的关系。在随访期间，只有9个ADM病变出现增长，其中6个来自较小病变组，3个来自较大病变组（p < 0.05）。中位随访期为35个月。我们的病人都没有患上胆囊癌。结论：我们的结果证实了ADM是良性病变的普遍假设。因此，我们认为对于明确诊断为局灶性ADM的病变不需要随访。

{"title":"Gallbladder adenomyomatosis revisited – Does size matter? is follow-up required for large lesions?","authors":"Shirley Shechter, Dana Ben-Ami Shor, Roie Tzadok, Hila Yashar, Sapir Lazar, Yuval Katz, Arthur Chernomorets, Rivka Kessner","doi":"10.1016/j.ejrad.2026.112698","DOIUrl":"10.1016/j.ejrad.2026.112698","url":null,"abstract":"<div><h3>Objectives</h3><div>Adenomyomatosis (ADM) is generally considered a benign condition. However, it can be associated with chronic cholecystitis − a known risk factor for gallbladder cancer. Therefore, studies have proposed follow-up with ultrasound for asymptomatic patients with focal ADM. Currently, there are no formal recommendations regarding the frequency and length of follow-up. The aims of this study were to assess the growth of ADM lesions during follow-up and to examine the differences between larger and smaller ADM lesions.</div></div><div><h3>Methods</h3><div>144 patients who underwent MRI-MRCP at our institution between the years 2014–2024 were identified through radiological reports as having a diagnosis of ADM. 43 patients had more than one examination. Demographic, clinical and radiological data were collected retrospectively. We divided the cohort into two groups based on the primary lesion size (axial diameter below or above 1.5 cm) and compared between them.</div></div><div><h3>Results</h3><div>The group of small lesions included 98 patients and the larger lesions group included 46 patients. We did not find a statistically significant correlation between the size of.</div><div>ADM and the demographic or clinical characteristics examined. Only 9 ADM lesions grew during follow-up − 6 from the smaller lesions group and 3 from the large lesions group (p > 0.05). The median follow-up period was 35 months. None of our patients developed gallbladder carcinoma.</div></div><div><h3>Conclusions</h3><div>Our results confirm the common hypothesis that ADM are benign lesions. Therefore, we believe that follow-up is not needed for lesions with a clear diagnosis of focal ADM.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"197 ","pages":"Article 112698"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146200556","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}

引用次数: 0

Interobserver variability of recall decisions between mammography readers in the English NHS breast screening programme: A comparison of interobserver variability measures 在英国NHS乳腺筛查项目中，乳房x光检查阅读者之间回忆决定的观察者间可变性：观察者间可变性测量的比较

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-07 DOI: 10.1016/j.ejrad.2026.112723

Laura Quinn , David Jenkinson , Sian Taylor-Phillips , Yemisi Takwoingi , Alice Sitch

Objectives

To evaluate interobserver variability between mammogram readers’ recall decisions in the English NHS breast screening programme, comparing different variability measures.

Methods

Data from 401,682 women in 22 NHS centres who underwent mammographic screening interpreted independently by two mammogram readers were included. Percentage agreement, prevalence-adjusted bias-adjusted-kappa (PABAK), Gwet’s agreement coefficient (Gwet’s AC) and Cohen’s kappa were reported with 95% confidence intervals. Analyses were performed separately for women at first and subsequent screening appointments, by cancer diagnosis, reader recall rates and age group.

Results

Of 86,287 women at first screening, 6,491 (7.5%) were recalled, compared to 9,488 (3.0%) of 315,395 at subsequent screenings. Percentage agreement, Gwet’s AC, and PABAK were lower for first screening than subsequent (93.6%, 95%CI: 93.4–93.7 vs 97.2%, 95%CI: 97.2–97.3), (92.3, 95%CI:92.1 to 92.5 vs 97.0, 95% CI: 97.0 to 97.1) and (87.2, 95%CI: 86.9–87.4 vs 94.4, 95%CI: 94.3–94.5), whereas Cohen’s kappa, which is biased downwards when prevalence of recall is lower, did not change (61.6, 95%CI: 60.7–62.5 vs 61.8, 95%CI: 61.0–62.5). Percentage agreement, Gwet’s AC, and PABAK were lower for women with cancer detected than without, but Cohen’s kappa showed the opposite pattern, driven by prevalence bias. Percentage agreement, Gwet’s AC, and PABAK were lower when one/both readers had high recall rates, but Cohen’s kappa showed no important pattern.

Conclusions

Percentage agreement, Gwet’s AC, and PABAK showed lower agreement for interpreting the more challenging first screen, without assistance of previous mammograms, when women had cancer and when one/both readers had a high recall rate. Cohen’s kappa was heavily distorted by outcome prevalence. Despite widespread use, Cohen’s kappa is inappropriate for low prevalence settings such as screening, or making comparisons when prevalence varies.

目的评价英国NHS乳腺筛查项目中乳房x线照片阅读者回忆决定的观察者间可变性，比较不同的可变性措施。方法纳入来自22个NHS中心的401682名接受乳房x光检查的妇女的数据，这些妇女由两名乳房x光检查阅读器独立解读。报告一致性百分比、流行校正偏倚校正kappa （PABAK）、Gwet一致系数（Gwet’s AC）和Cohen’s kappa，置信区间为95%。根据癌症诊断、读者回忆率和年龄组，分别对首次和随后的筛查预约的女性进行了分析。结果在首次筛查的86287名女性中，6491名（7.5%）被召回，而在随后的筛查中，315395名女性中有9488名（3.0%）被召回。首次筛查时，一致性百分比、Gwet的AC和PABAK低于后续筛查（93.6%,95%CI: 93.4-93.7 vs 97.2%, 95%CI: 97.2-97.3）、（92.3,95%CI:92.1 - 92.5 vs 97.0, 95%CI: 97.0 - 97.1）和（87.2,95%CI: 86.9-87.4 vs 94.4, 95%CI: 94.3-94.5），而当回忆率较低时，Cohen的kappa没有变化（61.6,95%CI: 60.7-62.5 vs 61.8, 95%CI: 61.0-62.5）。百分比一致，Gwet的AC和PABAK在检测到癌症的女性中低于未检测到癌症的女性，但Cohen的kappa显示出相反的模式，受流行偏差的驱动。当一个/两个读者的回忆率较高时，一致性百分比、Gwet’s AC和PABAK较低，但Cohen’s kappa没有显示出重要的模式。Gwet’s AC和PABAK的百分比一致性显示，当女性患有癌症，以及当其中一个/两个阅读者的回忆率很高时，在没有以前乳房x线照片的帮助下，对更具挑战性的第一次筛查的解释一致性较低。科恩kappa被结果普遍程度严重扭曲。尽管广泛使用，Cohen的kappa并不适用于低患病率环境，如筛查，或在患病率不同时进行比较。

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引用次数: 0

The application of Machine learning in predicting the outcomes of minimally invasive treatments for uterine Fibroids: A systematic review and meta-analysis 机器学习在子宫肌瘤微创治疗预后预测中的应用：一项系统综述和荟萃分析。

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.ejrad.2026.112726

Mohammad-Reza Hosseini-Siyanaki , Seyyed Mohammad Hosseini , Maryam Afshari , Fatemeh Kanaani Nejad , Hoda Mehrabi , Reza Elahi , Ahmadreza Sohrabi-Ashlaghi , Babak Ahmadi , Shakiba Houshi , Fereshteh Yazdanpanah , Zahra Beyzavi , Shams Iqbal

Rationale and Objectives

Uterine fibroids (UFs) are common benign tumors that impact women’s health, particularly through symptoms such as abnormal bleeding or reproductive dysfunction. Interventional radiology (IR) techniques like uterine artery embolization (UAE) and high-intensity focused ultrasound (HIFU) are minimally invasive alternatives to surgery. Machine learning (ML) has shown promise in predicting treatment outcomes, though the optimal model remains uncertain. This systematic review and meta-analysis evaluate models predicting outcomes of minimally invasive treatments for uterine fibroids.

Materials & Methods

A comprehensive search was conducted across five databases (PubMed, Embase, Scopus, Web of Science, and Cochrane) through November 2024, following PRISMA guidelines and registered in PROSPERO. Studies using ML to predict different outcomes of UFs treatment via minimally invasive treatments were included. PROBAST + AI was used to assess study quality. Pooled sensitivity, specificity, and AUC values were calculated using a bivariate random effect model.

Results

Out of 1,114 records, fourteen studies met the inclusion criteria, with 12 focusing on HIFU and two on UAE. Logistic regression was the most commonly used approach, while gradient‑boosting models reported high discrimination in some individual studies; however, external validation was uncommon and risk of bias was frequently high. AUCs for radiomics-based models ranged from 0.668 to 0.887, and combined models ranged from 0.773 to 0.93. Meta-analysis of five HIFU-based radiomics studies demonstrate pooled sensitivity of 75% and specificity of 76% respectively, with an AUC of 0.82.

Conclusion

ML models, particularly those integrating radiomics and clinical data, show strong performance in predicting image-guided treatment outcomes in UFs. These approaches support a promising path toward individualized treatment planning and may improve patient selection in clinical workflow.

基本原理和目的：子宫肌瘤（UFs）是一种常见的影响女性健康的良性肿瘤，特别是通过异常出血或生殖功能障碍等症状。介入放射学（IR）技术，如子宫动脉栓塞（UAE）和高强度聚焦超声（HIFU）是手术的微创替代方案。机器学习（ML）在预测治疗结果方面显示出了希望，尽管最佳模型仍不确定。本系统综述和荟萃分析评估了预测子宫肌瘤微创治疗结果的模型。材料与方法：根据PRISMA指南并在PROSPERO注册，在2024年11月之前对五个数据库（PubMed、Embase、Scopus、Web of Science和Cochrane）进行了全面的检索。包括使用ML预测微创治疗UFs的不同结果的研究。PROBAST + AI用于评估研究质量。使用双变量随机效应模型计算合并敏感性、特异性和AUC值。结果：在1114项记录中，14项研究符合纳入标准，其中12项关注HIFU， 2项关注UAE。逻辑回归是最常用的方法，而梯度增强模型在一些个别研究中报告了高歧视；然而，外部验证并不常见，偏倚风险往往很高。基于放射组学模型的auc范围为0.668 ~ 0.887，组合模型的auc范围为0.773 ~ 0.93。五项基于hifu的放射组学研究的荟萃分析显示，敏感性分别为75%，特异性为76%，AUC为0.82。结论：ML模型，特别是那些整合放射组学和临床数据的模型，在预测UFs的图像引导治疗结果方面表现出色。这些方法为个性化治疗计划提供了一条有希望的途径，并可能改善临床工作流程中的患者选择。

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引用次数: 0

Explainable and evidence-linked recommendations for spine surgery via a retrieval-augmented LLM agent 可解释的和证据相关的建议脊柱手术通过检索增强LLM剂。

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-16 DOI: 10.1016/j.ejrad.2026.112734

Yiren Li, Mingyu Lv, Liang Cheng, Yongxu Xie, Qian Gu, Haozheng He, Zhuguang Chen, Duopei Fang, Xiang Zhou

Background

Clinical decision-making in spinal and spinal cord diseases requires a comprehensive assessment of imaging findings, neurological status, bone integrity, and patient-centered goals. Recently, the emergence of Large Language Models (LLMs) has provided new tools for intelligent decision support; however, their reliability and clinical interpretability remain to be systematically evaluated.

Methods

We propose a novel retrieval augmented generation (RAG)-based framework specifically tailored for spinal disease decision support and systematically evaluated four advanced LLM agents (Gemini 2.5 Flash, DeepSeek, GPT-4o, GPT-4o-mini). The framework integrates domain-specific prompting, structured response formatting, and evidence citation tracking.We used 200 real-world spinal cases, each involving diagnostic, therapeutic, and follow-up tasks. Five spine surgeons independently evaluated model outputs using an 11-dimension rubric; each dimension rated on a 5-point Likert scale to evaluate both clinical and technical performance. Inter-group differences were analyzed using the Kruskal–Wallis and Dunn’s tests (P < 0.05), with radar plots used for multidimensional visualization.

Results

The proposed expert-evaluated framework enables a comprehensive, real-case-based comparison of four LLM agents. Gemini 2.5 Flash achieved the highest overall score (49.25 ± 2.88), significantly outperforming DeepSeek (47.49 ± 3.34, P < 0.001), GPT-4o (45.59 ± 3.89, P < 0.001), and GPT-4o-mini (41.23 ± 5.96, P < 0.001). It demonstrated leading performance particularly in humanistic care, follow-up suggestion, and test recommendation. DeepSeek showed superior capability in differential completeness (mean = 4.76), significantly outperforming the other three models (P < 0.001). Although GPT-4o-mini demonstrated stable system performance (mean = 4.61), it underperformed in core clinical reasoning dimensions. These findings reveal substantial inter-model variability in spine-specific clinical reasoning, an aspect often overlooked in prior non-benchmark LLM evaluations.

Conclusion

Among the four evaluated LLM agents, Gemini 2.5 Flash and DeepSeek demonstrated superior clinical accuracy, comprehensiveness, and usability in spine-related decision support. These findings support the potential of domain-adapted RAG agents to enhance evidence-based spinal care by providing accurate and comprehensive decision support. Future research should focus on multimodal integration data (e.g., imaging and clinical notes) and conducting prospective validation in real-world clinical environments.

背景：脊柱和脊髓疾病的临床决策需要对影像学表现、神经系统状态、骨完整性和以患者为中心的目标进行综合评估。最近，大型语言模型（llm）的出现为智能决策支持提供了新的工具；然而，它们的可靠性和临床可解释性仍有待系统评估。方法：我们提出了一种新的基于检索增强生成（RAG）的框架，专门为脊柱疾病决策支持量身定制，并系统评估了四种先进的LLM药物（Gemini 2.5 Flash, DeepSeek, gpt - 40, gpt - 40 -mini）。该框架集成了特定领域的提示、结构化的响应格式和证据引用跟踪。我们使用了200个真实的脊髓病例，每个病例都涉及诊断、治疗和随访任务。五名脊柱外科医生使用11维标准独立评估模型输出；每个维度都用5分李克特量表来评估临床和技术表现。采用Kruskal-Wallis和Dunn检验分析组间差异（P < 0.05），采用雷达图进行多维可视化。结果：提出的专家评估框架能够对四种LLM药物进行全面的、基于实际案例的比较。Gemini 2.5 Flash获得了最高的总分（49.25±2.88），显著优于DeepSeek（47.49±3.34）。结论：在四种评估的LLM药物中，Gemini 2.5 Flash和DeepSeek在脊柱相关决策支持方面表现出更好的临床准确性、全能性和可用性。这些发现支持领域适应性RAG药物通过提供准确和全面的决策支持来增强循证脊柱护理的潜力。未来的研究应侧重于多模式整合数据（例如，成像和临床记录），并在现实临床环境中进行前瞻性验证。

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引用次数: 0

Development and validation of a nomogram to predict surgical resection after conversion therapy in unresectable hepatocellular carcinoma 不可切除的肝细胞癌转换治疗后预测手术切除的影像学发展和验证。

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-13 DOI: 10.1016/j.ejrad.2026.112724

Yuhao Su , Yuxin Liang , Deyuan Zhong, Yahui Chen, Hongtao Yan, Qinyan Yang, Ming Wang, Zhengwei Leng, Xiaolun Huang

Objective

This study aimed to explore factors associated with the likelihood of surgical resection after triple-combination conversion therapy in patients with initially unresectable hepatocellular carcinoma (uHCC) and to develop an exploratory predictive model.

Methods

A retrospective analysis was conducted using clinical data from 210 patients with uHCC who underwent triple-combination conversion therapy at Sichuan Cancer Hospital between January 2022 and January 2025. Patients were randomly assigned to a training cohort (n = 147) and a validation cohort (n = 63) in a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was applied to screen candidate predictors, followed by multivariate logistic regression to identify factors associated with surgical conversion. A nomogram was constructed based on these variables, and its discriminative ability, calibration, and potential clinical utility were internally assessed using receiver operating characteristic (ROC) analysis, calibration plots, the Hosmer–Lemeshow test, and decision curve analysis (DCA).

Results

Among the 210 patients, 47 (22.4%) successfully underwent conversion and radical resection. Multivariate logistic regression analysis suggested that lower tumor burden score (TBS; OR = 0.663), lower neutrophil-to-lymphocyte ratio (NLR; OR = 0.572), lower C-reactive protein-to-albumin ratio (CAR; OR = 0.057), and absence of cirrhosis (OR = 0.289) were associated with a higher likelihood of successful surgical conversion (P < 0.05). The nomogram showed moderate to good discriminative performance, with areas under the ROC curve (AUCs) of 0.850 (95% CI: 0.784–0.915) in the training cohort and 0.871 (95% CI: 0.783–0.962) in the validation cohort. Calibration plots and decision curve analysis provided descriptive information regarding model performance within the study cohort.

Conclusion

The proposed nomogram, incorporating TBS, NLR, CAR, and cirrhosis status, represents an exploratory tool for estimating the probability of surgical conversion following triple-combination therapy in patients with uHCC. While the model may provide supplementary information to support clinical assessment and patient stratification, further multicenter and prospective studies are required to externally validate and refine its performance before broader clinical application.

目的：本研究旨在探讨最初不可切除的肝细胞癌（uHCC）患者三联转化治疗后手术切除可能性的相关因素，并建立探索性预测模型。方法：回顾性分析2022年1月至2025年1月在四川省肿瘤医院接受三联转化治疗的210例uHCC患者的临床资料。患者按7:3的比例随机分配到训练队列（n = 147）和验证队列（n = 63）。最小绝对收缩和选择算子（LASSO）回归应用于筛选候选预测因子，然后进行多变量逻辑回归以确定与手术转换相关的因素。基于这些变量构建了一个nomogram，并使用受试者工作特征（ROC）分析、校准图、Hosmer-Lemeshow检验和决策曲线分析（DCA）对其判别能力、校准和潜在的临床应用进行了内部评估。结果：210例患者中，47例（22.4%）成功行根治性手术。多因素logistic回归分析显示，较低的肿瘤负荷评分（TBS, OR = 0.663）、较低的中性粒细胞与淋巴细胞比值（NLR, OR = 0.572）、较低的c反应蛋白与白蛋白比值(CAR；OR = 0.057)，无肝硬化（OR = 0.289）与手术转化成功的可能性较高相关(P结论：所提出的nomogram，结合TBS、NLR、CAR和肝硬化状态，是评估uHCC患者三联治疗后手术转化可能性的探索性工具。虽然该模型可以为临床评估和患者分层提供补充信息，但在更广泛的临床应用之前，需要进一步的多中心和前瞻性研究来外部验证和完善其性能。

{"title":"Development and validation of a nomogram to predict surgical resection after conversion therapy in unresectable hepatocellular carcinoma","authors":"Yuhao Su , Yuxin Liang , Deyuan Zhong, Yahui Chen, Hongtao Yan, Qinyan Yang, Ming Wang, Zhengwei Leng, Xiaolun Huang","doi":"10.1016/j.ejrad.2026.112724","DOIUrl":"10.1016/j.ejrad.2026.112724","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to explore factors associated with the likelihood of surgical resection after triple-combination conversion therapy in patients with initially unresectable hepatocellular carcinoma (uHCC) and to develop an exploratory predictive model.</div></div><div><h3>Methods</h3><div>A retrospective analysis was conducted using clinical data from 210 patients with uHCC who underwent triple-combination conversion therapy at Sichuan Cancer Hospital between January 2022 and January 2025. Patients were randomly assigned to a training cohort (n = 147) and a validation cohort (n = 63) in a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was applied to screen candidate predictors, followed by multivariate logistic regression to identify factors associated with surgical conversion. A nomogram was constructed based on these variables, and its discriminative ability, calibration, and potential clinical utility were internally assessed using receiver operating characteristic (ROC) analysis, calibration plots, the Hosmer–Lemeshow test, and decision curve analysis (DCA).</div></div><div><h3>Results</h3><div>Among the 210 patients, 47 (22.4%) successfully underwent conversion and radical resection. Multivariate logistic regression analysis suggested that lower tumor burden score (TBS; OR = 0.663), lower neutrophil-to-lymphocyte ratio (NLR; OR = 0.572), lower C-reactive protein-to-albumin ratio (CAR; OR = 0.057), and absence of cirrhosis (OR = 0.289) were associated with a higher likelihood of successful surgical conversion (P < 0.05). The nomogram showed moderate to good discriminative performance, with areas under the ROC curve (AUCs) of 0.850 (95% CI: 0.784–0.915) in the training cohort and 0.871 (95% CI: 0.783–0.962) in the validation cohort. Calibration plots and decision curve analysis provided descriptive information regarding model performance within the study cohort.</div></div><div><h3>Conclusion</h3><div>The proposed nomogram, incorporating TBS, NLR, CAR, and cirrhosis status, represents an exploratory tool for estimating the probability of surgical conversion following triple-combination therapy in patients with uHCC. While the model may provide supplementary information to support clinical assessment and patient stratification, further multicenter and prospective studies are required to externally validate and refine its performance before broader clinical application.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"197 ","pages":"Article 112724"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146219026","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}

引用次数: 0

Mri-based diagnostic model integrating clinical features for placenta accreta spectrum in non-previa placenta 结合非前置胎盘增生谱临床特征的mri诊断模型

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology

Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ejrad.2026.112717

Yoshiko Ueno , Takahiro Tsuboyama , Naoya Ebisu , Hitomi Imafuku , Toshiki Hyodo , Keitaro Sofue , Daigo Kobayashi , Izumi Imaoka , Kenji Tanimura , Takamichi Murakami

Objectives

To identify clinical and MRI features useful for diagnosing placenta accreta spectrum (PAS) in non-previa placenta and to develop diagnostic models integrating these features.

Methods

This retrospective study included 101 pregnant women with non-previa placenta who underwent MRI between January 2022 and June 2024. Nineteen were confirmed as PAS. Clinical variables and 11 MRI findings were evaluated using intraoperative or pathological results as the reference standard. Diagnostic performance was assessed using univariable analysis and repeated cross-validation of a random forest (RF) model, with ROC analysis used to assess discriminative performance.

Results

Hormone replacement cycle–frozen embryo transfer (HRC-FET) (sensitivity 0.89, specificity 0.63) and abnormal placental bed vascularization (sensitivity 0.63, specificity 0.90) showed the strongest univariable performance. The RF model using six variables with acceptable interobserver agreement achieved an AUC of 0.88, sensitivity 0.92, specificity 0.79, demonstrating higher discriminative performance than individual predictors. Feature importance analysis highlighted HRC-FET and abnormal placental bed vascularization as the most influential factors.

Conclusions

Integrating clinical and MRI features improves PAS diagnosis in non-previa placenta. The RF model demonstrated a more balanced diagnostic profile than individual predictors in this exploratory cohort and may aid preoperative risk assessment. HRC-FET and abnormal placental bed vascularization were key contributors, supporting their relevance for risk stratification.

目的探讨非前置胎盘增生谱（PAS）的临床和MRI特征，并建立综合这些特征的诊断模型。方法回顾性研究纳入101例非前置胎盘孕妇，于2022年1月至2024年6月接受MRI检查。其中19人被确认为PAS。以术中或病理结果为参考标准，评价临床变量及11项MRI表现。采用单变量分析和随机森林（RF）模型的重复交叉验证来评估诊断性能，使用ROC分析来评估判别性能。结果激素替代周期-冷冻胚胎移植（HRC-FET）（敏感性0.89，特异性0.63）和胎盘床血管形成异常（敏感性0.63，特异性0.90）表现出最强的单变量表现。使用具有可接受的观察者间一致性的6个变量的RF模型的AUC为0.88，灵敏度为0.92，特异性为0.79，显示出比单个预测因子更高的判别性能。特征重要性分析显示HRC-FET和胎盘床血管化异常是最重要的影响因素。结论综合临床和MRI特征可提高PAS对非前置胎盘的诊断。在这个探索性队列中，RF模型比单个预测因子显示出更平衡的诊断概况，可能有助于术前风险评估。HRC-FET和胎盘床血管化异常是关键因素，支持它们与风险分层的相关性。

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引用次数: 0