Pub Date : 2024-10-18DOI: 10.1016/j.ejrad.2024.111795
Ludovica Rita La Rocca , Martina Caruso , Arnaldo Stanzione , Nicola Rocco , Tommaso Pellegrino , Daniela Russo , Maria Salatiello , Andrea de Giorgio , Roberta Pastore , Simone Maurea , Arturo Brunetti , Renato Cuocolo , Valeria Romeo
Purpose
To build and validate a combined radiomics and machine learning (ML) approach using B-mode US and SWE images to differentiate benign from malignant solid breast lesions (BLs) and compare its performance with that of an expert radiologist.
Methods
Patients with at least one BI-RADS 2–6 BL who performed breast US integrated with SWE were retrospectively included. B-mode US and SWE images were manually segmented to extract radiomics features. A multi-step feature selection process was performed and a predictive model built using the Logistic Regression algorithm. The diagnostic accuracy was evaluated with the AUC and Matthews Correlation Coefficient (MCC) metrics. The performance of the ML classifier was compared to that of an expert radiologist.
Results
427 Bls were included and divided into a training (286 BLs, of which 127 benign and 159 malignant) and a test set (141 BLs, of which 59 benign and 82 malignant). Of 1098 features extracted from B-mode US and SWE images, 13 were finally selected. The ML classifier showed an AUC of 0.768 and 0.746, and an MCC of 0.403 and 0.423 in the training and test sets, respectively. The performance was higher than that of the expert radiologist assessing only B-mode US images, but significantly lower when SWE images were also provided.
Conclusion
A ML approach based on B-mode US and SWE images may represent a potential tool in the characterization of BLs. SWE still gives its most relevant contribution in the clinical setting rather than included in a radiomics pipeline.
目的：利用 B 型 US 和 SWE 图像建立并验证一种结合放射组学和机器学习（ML）的方法，以区分乳腺实体病变（BL）的良恶性，并将其性能与放射科专家的性能进行比较：回顾性纳入至少有一个 BI-RADS 2-6 BL 的患者，这些患者的乳腺 US 与 SWE 相结合。手动分割 B 型 US 和 SWE 图像以提取放射组学特征。进行多步骤特征选择，并使用逻辑回归算法建立预测模型。诊断准确性通过 AUC 和马修斯相关系数 (Matthews Correlation Coefficient, MCC) 指标进行评估。将 ML 分类器的性能与放射科专家的性能进行了比较：共纳入 427 个BL，分为训练集（286 个BL，其中 127 个良性，159 个恶性）和测试集（141 个BL，其中 59 个良性，82 个恶性）。从 B 型 US 和 SWE 图像中提取的 1098 个特征中，最终选出 13 个。在训练集和测试集中，ML 分类器的 AUC 分别为 0.768 和 0.746，MCC 分别为 0.403 和 0.423。其性能高于仅评估 B 型 US 图像的放射科专家，但在同时提供 SWE 图像时，其性能明显降低：结论：基于 B 型 US 和 SWE 图像的 ML 方法可能是表征 BL 的一种潜在工具。在临床环境中，SWE 仍能做出最大贡献，而不是将其纳入放射组学管道中。
{"title":"Machine learning-based discrimination of benign and malignant breast lesions on US: The contribution of shear-wave elastography","authors":"Ludovica Rita La Rocca , Martina Caruso , Arnaldo Stanzione , Nicola Rocco , Tommaso Pellegrino , Daniela Russo , Maria Salatiello , Andrea de Giorgio , Roberta Pastore , Simone Maurea , Arturo Brunetti , Renato Cuocolo , Valeria Romeo","doi":"10.1016/j.ejrad.2024.111795","DOIUrl":"10.1016/j.ejrad.2024.111795","url":null,"abstract":"<div><h3>Purpose</h3><div>To build and validate a combined radiomics and machine learning (ML) approach using B-mode US and SWE images to differentiate benign from malignant solid breast lesions (BLs) and compare its performance with that of an expert radiologist.</div></div><div><h3>Methods</h3><div>Patients with at least one BI-RADS 2–6 BL who performed breast US integrated with SWE were retrospectively included. B-mode US and SWE images were manually segmented to extract radiomics features. A multi-step feature selection process was performed and a predictive model built using the Logistic Regression algorithm. The diagnostic accuracy was evaluated with the AUC and Matthews Correlation Coefficient (MCC) metrics. The performance of the ML classifier was compared to that of an expert radiologist.</div></div><div><h3>Results</h3><div>427 Bls were included and divided into a training (286 BLs, of which 127 benign and 159 malignant) and a test set (141 BLs, of which 59 benign and 82 malignant). Of 1098 features extracted from B-mode US and SWE images, 13 were finally selected. The ML classifier showed an AUC of 0.768 and 0.746, and an MCC of 0.403 and 0.423 in the training and test sets, respectively. The performance was higher than that of the expert radiologist assessing only B-mode US images, but significantly lower when SWE images were also provided.</div></div><div><h3>Conclusion</h3><div>A ML approach based on B-mode US and SWE images may represent a potential tool in the characterization of BLs. SWE still gives its most relevant contribution in the clinical setting rather than included in a radiomics pipeline.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111795"},"PeriodicalIF":3.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142497614","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}
Pub Date : 2024-10-18DOI: 10.1016/j.ejrad.2024.111794
A. Vidaud , F. Deleau , C. Cantarel , G. MacGrogan , M. Renaud , R. Dourmap , M.P. Depetiteville , P. Taourel , F. Chamming’s
Purpose
To evaluate the positive predictive value and factors predictive of malignancy of additional calcifications in the pre-therapeutic work-up of a synchronous breast cancer.
Materials and methods
Institutional review board approval was obtained for this retrospective study and informed consent was waved. Consecutive patients referred to our center between January 1st 2018 and December 31st 2022 for a breast cancer and who presented additional calcifications detected during the pretreatment work-up were eligible for inclusion in this study. Morphology, distribution and BI-RADS category of the calcifications were assessed in consensus by 3 radiologists specialized in breast imaging. Side and distance from the cancer were collected. The predictive value of malignancy of the calcifications was calculated for each BI-RADS category. Factors associated with malignancy were evaluated by logistic non-conditional regression on univariate and multivariate analysis.
Results
One hundred and thirteen clusters of calcifications in 103 patients were included. Among the groups of calcifications 41 % were malignant, 31 % benign and 28 % were atypia on biopsy. After exclusion of the non-operated atypia, 50.5 % of additional calcifications were ultimately malignant and 49.5 % were benign. The predictive value of malignancy was 20.7 %; 40.7 %; 63 %; 85.7 % and 100 % for category BI-RADS 3, 4a, 4B, 4c and 5 respectively. On multivariate analysis, multifocality or centricity of the index tumour (P = 0.01), BI-RADS classifications (P = 0.0001) and location ipsilateral less than 35 mm to the index cancer (P = 0.008) of the additional calcifications were found to be independent predictors of malignancy. Sixty percent of calcifications were not described on the initial out-center diagnostic work-up.
Conclusion
Additional calcifications detected during the pretreatment work-up of a breast cancer are associated with a higher probability of malignancy than in a screening population and require biopsy even when demonstrating probably benign (BI-RADS 3) features.
{"title":"Positive predictive value of malignancy for additional calcifications found during evaluation of a synchronous breast cancer","authors":"A. Vidaud , F. Deleau , C. Cantarel , G. MacGrogan , M. Renaud , R. Dourmap , M.P. Depetiteville , P. Taourel , F. Chamming’s","doi":"10.1016/j.ejrad.2024.111794","DOIUrl":"10.1016/j.ejrad.2024.111794","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the positive predictive value and factors predictive of malignancy of additional calcifications in the pre-therapeutic work-up of a synchronous breast cancer.</div></div><div><h3>Materials and methods</h3><div>Institutional review board approval was obtained for this retrospective study and informed consent was waved. Consecutive patients referred to our center between January 1st 2018 and December 31st 2022 for a breast cancer and who presented additional calcifications detected during the pretreatment work-up were eligible for inclusion in this study. Morphology, distribution and BI-RADS category of the calcifications were assessed in consensus by 3 radiologists specialized in breast imaging. Side and distance from the cancer were collected. The predictive value of malignancy of the calcifications was calculated for each BI-RADS category. Factors associated with malignancy were evaluated by logistic non-conditional regression on univariate and multivariate analysis.</div></div><div><h3>Results</h3><div>One hundred and thirteen clusters of calcifications in 103 patients were included. Among the groups of calcifications 41 % were malignant, 31 % benign and 28 % were atypia on biopsy. After exclusion of the non-operated atypia, 50.5 % of additional calcifications were ultimately malignant and 49.5 % were benign. The predictive value of malignancy was 20.7 %; 40.7 %; 63 %; 85.7 % and 100 % for category BI-RADS 3, 4a, 4B, 4c and 5 respectively. On multivariate analysis, multifocality or centricity of the index tumour (<em>P</em> = 0.01), BI-RADS classifications (<em>P</em> = 0.0001) and location ipsilateral less than 35 mm to the index cancer (<em>P</em> = 0.008) of the additional calcifications were found to be independent predictors of malignancy. Sixty percent of calcifications were not described on the initial out-center diagnostic work-up.</div></div><div><h3>Conclusion</h3><div>Additional calcifications detected during the pretreatment work-up of a breast cancer are associated with a higher probability of malignancy than in a screening population and require biopsy even when demonstrating probably benign (BI-RADS 3) features<strong>.</strong></div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111794"},"PeriodicalIF":3.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142497617","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}
Pub Date : 2024-10-18DOI: 10.1016/j.ejrad.2024.111766
Luca Russo , Luca D’Erme , Silvia Bottazzi , Andrea Amerighi , Miriam Dolciami , Federica Bernardini , Rosa De Vincenzo , Francesco Fanfani , Giovanni Scambia , Evis Sala , Benedetta Gui
Purpose
This study explores the performance of MRI in detecting residual disease after platinum-based neoadjuvant chemotherapy (NACT) in pregnant cervical cancer (CC) patients, which would serve as a surrogate of treatment response.
Methods
In this retrospective single-centre study, consecutive pregnant cervical cancer patients treated with NACT and undergoing MRI examination before and at the end of the therapeutic protocol between 2010 and 2021 were included. Tumour maximum diameter and nodal status were evaluated in MRI at staging and after NACT. Patients exhibiting increased tumor burden post-NACT were excluded. On post-NACT the presence or absence of residual disease was recorded and the MRI diagnostic performance for assessing residual disease was calculated using histopathology at radical hysterectomy as the reference standard.
Results
The study included 12 pregnant patients (median age 36 years, 27–42). At post-NACT MRI, residual disease was absent in 2/12 patients (16.7 %) while present in 10/12 (83.3 %). Histopathology was concordant in all patients without MRI residual disease and in 9/10 patients with MRI residual disease, while discordant in 1/10. MRI sensitivity, specificity, positive predictive value, negative predictive and accuracy for detecting residual disease were 100.0 % (95 %CI: 1.00, 1.00), 66.7 % (95 %CI: 0.13, 1.00), 90 % (95 %CI: 0.71, 1.00), 100 % (95 %CI: 1.00, 1.00), and 91.7 % (95 %CI: 0.76, 1.00) respectively (p = 0.045).
Conclusions
This study suggests that MRI has good diagnostic performance to detect residual disease after NACT in pregnant CC patients, and potentially assess response to treatment in this setting.
{"title":"MRI-based assessment of residual disease after neoadjuvant chemotherapy in pregnant women with cervical cancer","authors":"Luca Russo , Luca D’Erme , Silvia Bottazzi , Andrea Amerighi , Miriam Dolciami , Federica Bernardini , Rosa De Vincenzo , Francesco Fanfani , Giovanni Scambia , Evis Sala , Benedetta Gui","doi":"10.1016/j.ejrad.2024.111766","DOIUrl":"10.1016/j.ejrad.2024.111766","url":null,"abstract":"<div><h3>Purpose</h3><div>This study explores the performance of MRI in detecting residual disease after platinum-based neoadjuvant chemotherapy (NACT) in pregnant cervical cancer (CC) patients, which would serve as a surrogate of treatment response.</div></div><div><h3>Methods</h3><div>In this retrospective single-centre study, consecutive pregnant cervical cancer patients treated with NACT and undergoing MRI examination before and at the end of the therapeutic protocol between 2010 and 2021 were included. Tumour maximum diameter and nodal status were evaluated in MRI at staging and after NACT. Patients exhibiting increased tumor burden post-NACT were excluded. On post-NACT the presence or absence of residual disease was recorded and the MRI diagnostic performance for assessing residual disease was calculated using histopathology at radical hysterectomy as the reference standard.</div></div><div><h3>Results</h3><div>The study included 12 pregnant patients (median age 36 years, 27–42). At post-NACT MRI, residual disease was absent in 2/12 patients (16.7 %) while present in 10/12 (83.3 %). Histopathology was concordant in all patients without MRI residual disease and in 9/10 patients with MRI residual disease, while discordant in 1/10. MRI sensitivity, specificity, positive predictive value, negative predictive and accuracy for detecting residual disease were 100.0 % (95 %CI: 1.00, 1.00), 66.7 % (95 %CI: 0.13, 1.00), 90 % (95 %CI: 0.71, 1.00), 100 % (95 %CI: 1.00, 1.00), and 91.7 % (95 %CI: 0.76, 1.00) respectively (<em>p</em> = 0.045).</div></div><div><h3>Conclusions</h3><div>This study suggests that MRI has good diagnostic performance to detect residual disease after NACT in pregnant CC patients, and potentially assess response to treatment in this setting.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111766"},"PeriodicalIF":3.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593639","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}
Pub Date : 2024-10-17DOI: 10.1016/j.ejrad.2024.111782
Yixin Liu , Zhihui Wang , Liping Yang , Meng Zhang , Mengyue Li , Juxuan Zhang , Lefan Tang , Zhiyun Jiang , Xin Li , Jiaxing Deng , Qingwei Meng , Shilong Liu , Kezheng Wang , Lishuang Qi
Objectives
Radiomics provides an opportunity to evaluate cancer prognosis noninvasively. However, the susceptibility of the radiomic quantitative features to multicenter effects, leads to the clinical dilemma of the radiomic signatures. This study aimed to develop a radiomic signature to circumvent multicenter effects, achieving the individualized prognostic assessment of lung adenocarcinoma (LUAD).
Methods
Using computed tomography (CT) imaging of 234 stage I–IIIA LUAD patients derived from three public multicenter cohorts, we proposed a rank-based method that utilized the relative rank patterns of quantitative values between radiomic feature pairs within individual patients and established a feature pair signature for LUAD prognosis. We collected a new clinical cohort with 162 LUAD patients for independent validation.
Results
A rank-based radiomic signature, consisting of 12 feature pairs, was developed, and it could determine the mortality risk for an individual according to the rank patterns of 12 feature pairs within the patient’s CT imaging. The prognostic performance of the rank-based signature was effectively validated in the new clinical cohort (log-rank P = 0.0051, C-index = 0.73), whereas other signatures lost their prognostic ability across centers. The novel proposed radiomic nomogram significantly improved the prognostic performance of clinicopathological factors. The further radiogenomic analyses revealed the underlying biological characteristics (e.g., Stemness, Ferroptosis, ’ECM’) reflected by the rank-based radiomic signature.
Conclusions
This multicenter study illustrates the accuracy and stability of the rank-based radiomic signature for LUAD prognosis, and demonstrates a unique advantage of clinical individualized application. The biological characteristics underlying the rank-based radiomic signature would accelerate its clinical application.
{"title":"Identification of a rank-based radiomic signature with individualized prognostic value for lung adenocarcinoma in a multi-cohort study","authors":"Yixin Liu , Zhihui Wang , Liping Yang , Meng Zhang , Mengyue Li , Juxuan Zhang , Lefan Tang , Zhiyun Jiang , Xin Li , Jiaxing Deng , Qingwei Meng , Shilong Liu , Kezheng Wang , Lishuang Qi","doi":"10.1016/j.ejrad.2024.111782","DOIUrl":"10.1016/j.ejrad.2024.111782","url":null,"abstract":"<div><h3>Objectives</h3><div>Radiomics provides an opportunity to evaluate cancer prognosis noninvasively. However, the susceptibility of the radiomic quantitative features to multicenter effects, leads to the clinical dilemma of the radiomic signatures. This study aimed to develop a radiomic signature to circumvent multicenter effects, achieving the individualized prognostic assessment of lung adenocarcinoma (LUAD).</div></div><div><h3>Methods</h3><div>Using computed tomography (CT) imaging of 234 stage I–IIIA LUAD patients derived from three public multicenter cohorts, we proposed a rank-based method that utilized the relative rank patterns of quantitative values between radiomic feature pairs within individual patients and established a feature pair signature for LUAD prognosis. We collected a new clinical cohort with 162 LUAD patients for independent validation.</div></div><div><h3>Results</h3><div>A rank-based radiomic signature, consisting of 12 feature pairs, was developed, and it could determine the mortality risk for an individual according to the rank patterns of 12 feature pairs within the patient’s CT imaging. The prognostic performance of the rank-based signature was effectively validated in the new clinical cohort (log-rank <em>P</em> = 0.0051, C-index = 0.73), whereas other signatures lost their prognostic ability across centers. The novel proposed radiomic nomogram significantly improved the prognostic performance of clinicopathological factors. The further radiogenomic analyses revealed the underlying biological characteristics (e.g., Stemness, Ferroptosis, ’ECM’) reflected by the rank-based radiomic signature.</div></div><div><h3>Conclusions</h3><div>This multicenter study illustrates the accuracy and stability of the rank-based radiomic signature for LUAD prognosis, and demonstrates a unique advantage of clinical individualized application. The biological characteristics underlying the rank-based radiomic signature would accelerate its clinical application.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111782"},"PeriodicalIF":3.2,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461235","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}
Pub Date : 2024-10-15DOI: 10.1016/j.ejrad.2024.111791
Jana Ivanidze , Arman Sharbatdaran , Aliah McCalla , Andrew Brandmaier , Eleni Andreopoulou , Massimo Cristofanilli , Tessa Cigler , Onyinye D. Balogun , Rajiv S. Magge , Benjamin Liechty , Nicolas A. Karakatsanis , Sadek A. Nehmeh , Matthew A. Agee , Jolie Jean , Joseph R. Osborne , Kathryn Beal , Theodore H. Schwartz , Susan C. Pannullo , Jonathan P.S. Knisely , Rohan Ramakrishna
Objective
Our purpose was to describe our initial institutional experience using dedicated brain [18F]-Fluoroestradiol (FES) PET/CT or PET/MRI in the management of patients with estrogen-receptor-positive (ER+) breast cancer brain metastases (BCBM), and compare to [18F]-Fluorodeoxyglucose (FDG) PET and MRI.
Materials & Methods
Patients with biopsy-proven ER+ disease and MRI findings of suspected new, progressive, or recurrent BCBM were included in this retrospective study. Clinical and demographic data were collected. Dedicated brain FES PET/CT or PET/MRI was performed for clinical purposes. Maximum standardized uptake value (SUV) in MRI-defined target lesions and SUV ratio (SUVR, referencing normal-appearing parenchyma) were obtained. Pathology and/or clinical and MRI follow-up data were used as gold standard to classify viable neoplasm versus post-radiotherapy (RT) sequelae. Mann-Whitney tests were performed to compare subgroups.
Results
Seven patients met inclusion criteria. 15/16 (94 %) lesions classified as neoplasm were FES-positive. 4/4 (100 %) lesions classified as RT sequelae were FES-negative. Median tumor FES-SUVR were higher than median RT-sequelae FES-SUVR (6.0 (2.8–9.1) versus 0.5 (0.3–0.7), p < 0.01), and similarly, median tumor FES-SUV were higher than median RT-sequelae FES-SUV (4.8 (2.8–9.1) versus 0.6 (0.3–0.8), p < 0.01). Lesion-based analysis of FDG-SUV and −SUVR demonstrated a trend for higher FDG avidity in lesions characterized as neoplasm; however, this did not reach statistical significance.
Conclusion
Dedicated FES brain PET represents a promising adjunct modality, noting limitations of small sample size, retrospective nature of our study, and the possibility of ER expression heterogeneity. Our findings merit future prospective clinical trials incorporating dedicated brain FES PET/CT and PET/MRI in the management of patients with ER-positive disease and BCBM.
{"title":"[18F]-Fluoroestradiol (FES) brain PET in the evaluation of patients with estrogen receptor positive breast cancer and known or suspected brain metastases","authors":"Jana Ivanidze , Arman Sharbatdaran , Aliah McCalla , Andrew Brandmaier , Eleni Andreopoulou , Massimo Cristofanilli , Tessa Cigler , Onyinye D. Balogun , Rajiv S. Magge , Benjamin Liechty , Nicolas A. Karakatsanis , Sadek A. Nehmeh , Matthew A. Agee , Jolie Jean , Joseph R. Osborne , Kathryn Beal , Theodore H. Schwartz , Susan C. Pannullo , Jonathan P.S. Knisely , Rohan Ramakrishna","doi":"10.1016/j.ejrad.2024.111791","DOIUrl":"10.1016/j.ejrad.2024.111791","url":null,"abstract":"<div><h3>Objective</h3><div>Our purpose was to describe our initial institutional experience using dedicated brain [18F]-Fluoroestradiol (FES) PET/CT or PET/MRI in the management of patients with estrogen-receptor-positive (ER+) breast cancer brain metastases (BCBM), and compare to [18F]-Fluorodeoxyglucose (FDG) PET and MRI.</div></div><div><h3>Materials & Methods</h3><div>Patients with biopsy-proven ER+ disease and MRI findings of suspected new, progressive, or recurrent BCBM were included in this retrospective study. Clinical and demographic data were collected. Dedicated brain FES PET/CT or PET/MRI was performed for clinical purposes. Maximum standardized uptake value (SUV) in MRI-defined target lesions and SUV ratio (SUVR, referencing normal-appearing parenchyma) were obtained. Pathology and/or clinical and MRI follow-up data were used as gold standard to classify viable neoplasm versus post-radiotherapy (RT) sequelae. Mann-Whitney tests were performed to compare subgroups.</div></div><div><h3>Results</h3><div>Seven patients met inclusion criteria. 15/16 (94 %) lesions classified as neoplasm were FES-positive. 4/4 (100 %) lesions classified as RT sequelae were FES-negative. Median tumor FES-SUVR were higher than median RT-sequelae FES-SUVR (6.0 (2.8–9.1) versus 0.5 (0.3–0.7), p < 0.01), and similarly, median tumor FES-SUV were higher than median RT-sequelae FES-SUV (4.8 (2.8–9.1) versus 0.6 (0.3–0.8), p < 0.01). Lesion-based analysis of FDG-SUV and −SUVR demonstrated a trend for higher FDG avidity in lesions characterized as neoplasm; however, this did not reach statistical significance.</div></div><div><h3>Conclusion</h3><div>Dedicated FES brain PET represents a promising adjunct modality, noting limitations of small sample size, retrospective nature of our study, and the possibility of ER expression heterogeneity. Our findings merit future prospective clinical trials incorporating dedicated brain FES PET/CT and PET/MRI in the management of patients with ER-positive disease and BCBM.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111791"},"PeriodicalIF":3.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461233","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}
Pub Date : 2024-10-15DOI: 10.1016/j.ejrad.2024.111789
M.M.Q Robbe , F.M.E Pinckaers , B.A.J.M. Wagemans , R.J van Oostenbrugge , W.H van Zwam , J. Staals , A.A. Postma
Background
CT perfusion angiographic reconstructions (CTP-AR), derived at the peak arterial inflow, have demonstrated similar diagnostic accuracy to CTA for occlusion detection in the anterior circulation. Colour-coded CTP maps may aid in localizing an occlusion. This study aim to assess the additional value of CTP maps for occlusion detection in ischemic stroke patients assessed with CTA or CTP-AR.
Methods
In this single center retrospective study, ischemic stroke patients who underwent both CTA and CTP from September 2020 up to and including September 2021 were included. The 1 mm CTP series at peak arterial inflow corresponds to the CTP-AR. Following the initial assessment of CTA and CTP-AR for occlusion detection, three readers with varying levels of experienced, reassessed the CTA and CTP-AR with access to the color-coded CTP maps. Each CTA and CTP-AR was reviewed by two readers, and certainty of assessment was recorded. The reference standard was determined through a consensus meeting. Differences in certainty were assessed using a t-test for paired samples.
Results
Out of 210 patients, 107 patients had an occlusion based on our reference standard. The addition of CTP maps resulted in the finding of 11 (2.6%) additional occlusions out of 420 CTA readings and 9 (2.1%) out of 420 CTP-AR readings, primarily involving distal occlusions. After addition of CTP maps, 7 (1.6%) initial assessments on CTA and 3 (0.7%) initial assessments on CTP-AR were deemed false positive. The overall certainty of all readers significantly (P < 0.001) increased after addition of CTP maps.
Conclusion
The usage of CTP maps improved occlusion detection on both CTA and CTP-AR, especially for distal occlusions, and resulted in the increase of the overall level of certainty among readers with varying levels of experience.
{"title":"Additional value of CTP maps in occlusion detection on CTP angiographic reconstructions for ischemic stroke","authors":"M.M.Q Robbe , F.M.E Pinckaers , B.A.J.M. Wagemans , R.J van Oostenbrugge , W.H van Zwam , J. Staals , A.A. Postma","doi":"10.1016/j.ejrad.2024.111789","DOIUrl":"10.1016/j.ejrad.2024.111789","url":null,"abstract":"<div><h3>Background</h3><div>CT perfusion angiographic reconstructions (CTP-AR), derived at the peak arterial inflow, have demonstrated similar diagnostic accuracy to CTA for occlusion detection in the anterior circulation. Colour-coded CTP maps may aid in localizing an occlusion. This study aim to assess the additional value of CTP maps for occlusion detection in ischemic stroke patients assessed with CTA or CTP-AR.</div></div><div><h3>Methods</h3><div>In this single center retrospective study, ischemic stroke patients who underwent both CTA and CTP from September 2020 up to and including September 2021 were included. The 1 mm CTP series at peak arterial inflow corresponds to the CTP-AR. Following the initial assessment of CTA and CTP-AR for occlusion detection, three readers with varying levels of experienced, reassessed the CTA and CTP-AR with access to the color-coded CTP maps. Each CTA and CTP-AR was reviewed by two readers, and certainty of assessment was recorded. The reference standard was determined through a consensus meeting. Differences in certainty were assessed using a <em>t</em>-test for paired samples.</div></div><div><h3>Results</h3><div>Out of 210 patients, 107 patients had an occlusion based on our reference standard. The addition of CTP maps resulted in the finding of 11 (2.6%) additional occlusions out of 420 CTA readings and 9 (2.1%) out of 420 CTP-AR readings, primarily involving distal occlusions. After addition of CTP maps, 7 (1.6%) initial assessments on CTA and 3 (0.7%) initial assessments on CTP-AR were deemed false positive. The overall certainty of all readers significantly (<em>P</em> < 0.001) increased after addition of CTP maps.</div></div><div><h3>Conclusion</h3><div>The usage of CTP maps improved occlusion detection on both CTA and CTP-AR, especially for distal occlusions, and resulted in the increase of the overall level of certainty among readers with varying levels of experience.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111789"},"PeriodicalIF":3.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461234","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}
Pub Date : 2024-10-15DOI: 10.1016/j.ejrad.2024.111790
Nadine Bayerl, Lisa C Adams, Alexander Cavallaro, Tobias Bäuerle, Michael Schlicht, Bernd Wullich, Arndt Hartmann, Michael Uder, Stephan Ellmann
Objective: This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the reference standard, while contextualizing its performance within the framework of PI-RADS v2.1 criteria.
Material and methods: This retrospective study analyzed 123 patients who underwent multiparametric prostate MRI followed by systematic and targeted biopsies. MRI protocols adhered to international guidelines and included T2-weighted, diffusion-weighted, T1-weighted, and dynamic contrast-enhanced imaging. The AI software tool mdprostate was integrated into the Picture Archiving and Communication System to automatically segment the prostate, calculate prostate volume, and classify lesions according to PI-RADS scores using biparametric T2-weighted and diffusion-weighted imaging. Histopathological analysis of biopsy cores served as the reference standard. Diagnostic performance metrics including sensitivity, specificity, positive and negative predictive value (PPV, NPV), and area under the ROC curve (AUC) were calculated.
Results: mdprostate demonstrated 100 % sensitivity at a PI-RADS ≥ 2 cutoff, effectively ruling out both clinically significant and non-significant prostate cancers for lesions remaining below this threshold. For detecting clinically significant prostate cancer (csPCa) using a PI-RADS ≥ 4 cutoff, mdprostate achieved a sensitivity of 85.5 % and a specificity of 63.2 %. The AUC for detecting cancers of any grade was 0.803. The performance metrics of mdprostate were comparable to those reported in two meta-analyses of PI-RADS v2.1, with no significant differences in sensitivity and specificity (p > 0.05).
Conclusion: The evaluated AI tool demonstrated high diagnostic performance in identifying and grading prostate lesions, with results comparable to those reported in meta-analyses of expert readers using PI-RADS v2.1. Its ability to standardize evaluations and potentially reduce variability underscores its potential as a valuable adjunct in the prostate cancer diagnostic pathway. The high accuracy of mdprostate, particularly in ruling out prostate cancers, highlights its clinical utility by reducing workload and enhancing patient outcomes.
{"title":"Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation.","authors":"Nadine Bayerl, Lisa C Adams, Alexander Cavallaro, Tobias Bäuerle, Michael Schlicht, Bernd Wullich, Arndt Hartmann, Michael Uder, Stephan Ellmann","doi":"10.1016/j.ejrad.2024.111790","DOIUrl":"https://doi.org/10.1016/j.ejrad.2024.111790","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the reference standard, while contextualizing its performance within the framework of PI-RADS v2.1 criteria.</p><p><strong>Material and methods: </strong>This retrospective study analyzed 123 patients who underwent multiparametric prostate MRI followed by systematic and targeted biopsies. MRI protocols adhered to international guidelines and included T2-weighted, diffusion-weighted, T1-weighted, and dynamic contrast-enhanced imaging. The AI software tool mdprostate was integrated into the Picture Archiving and Communication System to automatically segment the prostate, calculate prostate volume, and classify lesions according to PI-RADS scores using biparametric T2-weighted and diffusion-weighted imaging. Histopathological analysis of biopsy cores served as the reference standard. Diagnostic performance metrics including sensitivity, specificity, positive and negative predictive value (PPV, NPV), and area under the ROC curve (AUC) were calculated.</p><p><strong>Results: </strong>mdprostate demonstrated 100 % sensitivity at a PI-RADS ≥ 2 cutoff, effectively ruling out both clinically significant and non-significant prostate cancers for lesions remaining below this threshold. For detecting clinically significant prostate cancer (csPCa) using a PI-RADS ≥ 4 cutoff, mdprostate achieved a sensitivity of 85.5 % and a specificity of 63.2 %. The AUC for detecting cancers of any grade was 0.803. The performance metrics of mdprostate were comparable to those reported in two meta-analyses of PI-RADS v2.1, with no significant differences in sensitivity and specificity (p > 0.05).</p><p><strong>Conclusion: </strong>The evaluated AI tool demonstrated high diagnostic performance in identifying and grading prostate lesions, with results comparable to those reported in meta-analyses of expert readers using PI-RADS v2.1. Its ability to standardize evaluations and potentially reduce variability underscores its potential as a valuable adjunct in the prostate cancer diagnostic pathway. The high accuracy of mdprostate, particularly in ruling out prostate cancers, highlights its clinical utility by reducing workload and enhancing patient outcomes.</p>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"111790"},"PeriodicalIF":3.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142617465","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}
National Comprehensive Cancer Network guidelines suggest chest CT when gastrointestinal stromal tumors are larger than 2 cm. We evaluate the value of screening the chest region during initial and follow-up CT.
Method
Single institution retrospective analysis of GIST cancer registry for patients diagnosed between May 2010 and November 2019 with tumor > 2 cm. We collected the patient demographics and clinical data; reviewed all CT scans of the chest region and recorded the lung nodules. Patients were grouped into lung nodule group and non-nodule group. Categorical variables were compared with the Chi square test and continuous variables with the Mann-Whitney U test. The survival probability was determined from Kaplan-Meier survival analysis and log-rank test for comparing differences.
Results
The final cohort included 382 patients (median age 61 years-old [interquartile range: 52–71]) and grouped into non-nodule group (n = 284) and lung nodule group (n = 98). The frequency of the CT scan was more in lung nodule group (8 [5–12]) than in non-nodule group (4 [2–9], p < 0.001). The lung nodule group had more CT including the chest region (6 [3–10] vs 3 [1–7], p < 0.001). In progressive lung nodules (8/98 [8 %]), only one patient had confirmed lung metastasis from GIST (1/382 [3 %]). There was no difference in overall survival between nodule groups (p = 0.12).
Conclusions
GIST patients with tumors larger than 2 cm have extremely low risk for lung metastasis. Routine inclusion of chest CT scan in staging and follow up is unnecessary.
{"title":"Is there value in the routine inclusion of chest computed tomography for patients with gastrointestinal stromal tumor?","authors":"Min-Ru Shen , Wen-Hui Chan , Ying-Chieh Lai , Chien-Ming Chen","doi":"10.1016/j.ejrad.2024.111787","DOIUrl":"10.1016/j.ejrad.2024.111787","url":null,"abstract":"<div><h3>Purpose</h3><div>National Comprehensive Cancer Network guidelines suggest chest CT when gastrointestinal stromal tumors are larger than 2 cm. We evaluate the value of screening the chest region during initial and follow-up CT.</div></div><div><h3>Method</h3><div>Single institution retrospective analysis of GIST cancer registry for patients diagnosed between May 2010 and November 2019 with tumor > 2 cm. We collected the patient demographics and clinical data; reviewed all CT scans of the chest region and recorded the lung nodules. Patients were grouped into lung nodule group and non-nodule group. Categorical variables were compared with the Chi square test and continuous variables with the Mann-Whitney <em>U</em> test. The survival probability was determined from Kaplan-Meier survival analysis and log-rank test for comparing differences.</div></div><div><h3>Results</h3><div>The final cohort included 382 patients (median age 61 years-old [interquartile range: 52–71]) and grouped into non-nodule group (n = 284) and lung nodule group (n = 98). The frequency of the CT scan was more in lung nodule group (8 [5–12]) than in non-nodule group (4 [2–9], p < 0.001). The lung nodule group had more CT including the chest region (6 [3–10] vs 3 [1–7], p < 0.001). In progressive lung nodules (8/98 [8 %]), only one patient had confirmed lung metastasis from GIST (1/382 [3 %]). There was no difference in overall survival between nodule groups (p = 0.12).</div></div><div><h3>Conclusions</h3><div>GIST patients with tumors larger than 2 cm have extremely low risk for lung metastasis. Routine inclusion of chest CT scan in staging and follow up is unnecessary.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111787"},"PeriodicalIF":3.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461236","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}
Pub Date : 2024-10-14DOI: 10.1016/j.ejrad.2024.111788
Burak Kocak , Andrea Ponsiglione , Arnaldo Stanzione , Lorenzo Ugga , Michail E. Klontzas , Roberto Cannella , Renato Cuocolo
Purpose
This study aims to evaluate current reporting practices in radiomics research, with a focus on CheckList for EvaluAtion of Radiomics research (CLEAR).
Methods
We conducted a citation search using Google Scholar to collect original research articles on radiomics citing the CLEAR guideline up to June 17, 2024. We examined the adoption of the guideline, adherence scores per publication, item-wise adherence rates, and self-reporting practices. An expert panel from the European Society of Medical Imaging Informatics Radiomics Auditing Group conducted a detailed item-by-item confirmation analysis of the self-reported CLEAR checklists.
Results
Out of 100 unique citations from 104 records, 48 original research papers on radiomics were included. The overall adoption rate in the literature was 2 %. Among the citing articles, 94 % (45/48) adopted CLEAR for reporting purposes, applying it to both hand-crafted radiomics (89 %) and deep learning (24 %). Self-reported checklists were included in 58 % (26/45) of these papers. Median study-wise adherence score for self-reported data was 91 % (interquartile range = 18 %). Mean confirmed adherence score was 66 % (standard deviation = 14 %). Difference between these scores was statistically significant, (mean = 21 %; standard deviation = 11 %), p < 0.001. Using an arbitrary 50 % adherence cut-off, the number of items with poor adherence increased from 3 to 15 after confirmation analysis, mostly comprised of open science-related items. In addition, several items were frequently misreported.
Conclusion
This study revealed significant discrepancies between self-reported and confirmed adherence to the CLEAR guideline in radiomics research, indicating a need for improved reporting accuracy and verification practices.
{"title":"CLEAR guideline for radiomics: Early insights into current reporting practices endorsed by EuSoMII","authors":"Burak Kocak , Andrea Ponsiglione , Arnaldo Stanzione , Lorenzo Ugga , Michail E. Klontzas , Roberto Cannella , Renato Cuocolo","doi":"10.1016/j.ejrad.2024.111788","DOIUrl":"10.1016/j.ejrad.2024.111788","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aims to evaluate current reporting practices in radiomics research, with a focus on CheckList for EvaluAtion of Radiomics research (CLEAR).</div></div><div><h3>Methods</h3><div>We conducted a citation search using Google Scholar to collect original research articles on radiomics citing the CLEAR guideline up to June 17, 2024. We examined the adoption of the guideline, adherence scores per publication, item-wise adherence rates, and self-reporting practices. An expert panel from the European Society of Medical Imaging Informatics Radiomics Auditing Group conducted a detailed item-by-item confirmation analysis of the self-reported CLEAR checklists.</div></div><div><h3>Results</h3><div>Out of 100 unique citations from 104 records, 48 original research papers on radiomics were included. The overall adoption rate in the literature was 2 %. Among the citing articles, 94 % (45/48) adopted CLEAR for reporting purposes, applying it to both hand-crafted radiomics (89 %) and deep learning (24 %). Self-reported checklists were included in 58 % (26/45) of these papers. Median study-wise adherence score for self-reported data was 91 % (interquartile range = 18 %). Mean confirmed adherence score was 66 % (standard deviation = 14 %). Difference between these scores was statistically significant, (mean = 21 %; standard deviation = 11 %), p < 0.001. Using an arbitrary 50 % adherence cut-off, the number of items with poor adherence increased from 3 to 15 after confirmation analysis, mostly comprised of open science-related items. In addition, several items were frequently misreported.</div></div><div><h3>Conclusion</h3><div>This study revealed significant discrepancies between self-reported and confirmed adherence to the CLEAR guideline in radiomics research, indicating a need for improved reporting accuracy and verification practices.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111788"},"PeriodicalIF":3.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142497611","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}
Pub Date : 2024-10-13DOI: 10.1016/j.ejrad.2024.111785
Eva Klintström , Adam Ly , Michael Sandborg , Mischa Woisetschläger , Erik Tesselaar
Background
Accurate diagnostic imaging is crucial for managing facial fractures, which are a common global occurrence. This study aimed to compare the image quality of Photon Counting Detector CT (PCD-CT) with state-of-the-art Energy Integrating Detector CT (EID-CT) and intraoperative C-arm CBCT (CBCT) in visualizing maxillofacial anatomy using a cadaveric sheep head model.
Methods
Three fresh sheep heads were used, with surgical interventions simulating metal implants in two of them. The specimens were imaged using PCD-CT, EID-CT, and CBCT, following which quantitative assessments of signal-to-noise ratio, sharpness, and artifacts were conducted. A visual grading study was performed by six observers, using criteria focusing on the mandible, orbit, and soft tissues. Statistical analyses included Friedman tests for comparing modalities and Kendall’s W and Gwet’s AC1 for assessing inter- and intrarater agreement.
Results
PCD-CT demonstrated a significantly higher signal-to-noise ratio (p = 0.03) and bone sharpness (p < 0.001) compared to CBCT. In visual grading, PCD-CT outperformed CBCT, but not EID-CT, particularly in delineating mandibular and orbital structures. EID-CT and PCD-CT showed slightly more severe hypodense artifacts (p = 0.01) but were comparable in streak artifact presentation. The interrater and intrarater agreements indicated consistent evaluations across and within observers.
Conclusion
PCD-CT exhibits superior image quality over CBCT in key parameters essential for maxillofacial imaging, while no apparent improvement was shown compared to state-of-the-art EID-CT. PCD-CT offers enhanced visualization of critical anatomical structures, suggesting its potential as a preferred modality in managing maxillofacial trauma. The findings in this study align with limited existing research on PCD-CT, underscoring its promise for advanced diagnostic imaging in maxillofacial applications.
{"title":"Image quality of photon-counting detector CT for visualization of maxillofacial anatomy in comparison with energy-integrating detector CT and intraoperative C-arm CBCT","authors":"Eva Klintström , Adam Ly , Michael Sandborg , Mischa Woisetschläger , Erik Tesselaar","doi":"10.1016/j.ejrad.2024.111785","DOIUrl":"10.1016/j.ejrad.2024.111785","url":null,"abstract":"<div><h3>Background</h3><div>Accurate diagnostic imaging is crucial for managing facial fractures, which are a common global occurrence. This study aimed to compare the image quality of Photon Counting Detector CT (PCD-CT) with state-of-the-art Energy Integrating Detector CT (EID-CT) and intraoperative C-arm CBCT (CBCT) in visualizing maxillofacial anatomy using a cadaveric sheep head model.</div></div><div><h3>Methods</h3><div>Three fresh sheep heads were used, with surgical interventions simulating metal implants in two of them. The specimens were imaged using PCD-CT, EID-CT, and CBCT, following which quantitative assessments of signal-to-noise ratio, sharpness, and artifacts were conducted. A visual grading study was performed by six observers, using criteria focusing on the mandible, orbit, and soft tissues. Statistical analyses included Friedman tests for comparing modalities and Kendall’s W and Gwet’s AC1 for assessing inter- and intrarater agreement.</div></div><div><h3>Results</h3><div>PCD-CT demonstrated a significantly higher signal-to-noise ratio (p = 0.03) and bone sharpness (p < 0.001) compared to CBCT. In visual grading, PCD-CT outperformed CBCT, but not EID-CT, particularly in delineating mandibular and orbital structures. EID-CT and PCD-CT showed slightly more severe hypodense artifacts (p = 0.01) but were comparable in streak artifact presentation. The interrater and intrarater agreements indicated consistent evaluations across and within observers.</div></div><div><h3>Conclusion</h3><div>PCD-CT exhibits superior image quality over CBCT in key parameters essential for maxillofacial imaging, while no apparent improvement was shown compared to state-of-the-art EID-CT. PCD-CT offers enhanced visualization of critical anatomical structures, suggesting its potential as a preferred modality in managing maxillofacial trauma. The findings in this study align with limited existing research on PCD-CT, underscoring its promise for advanced diagnostic imaging in maxillofacial applications.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"181 ","pages":"Article 111785"},"PeriodicalIF":3.2,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441860","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号