Purpose: To investigate the magnetic resonance imaging (MRI) and clinicopathological features of primary hepatic angiosarcoma (PHA) and enhance preoperative diagnosis.
Methods: MRI and clinicopathological information of 12 cases proved PHA were reviewed. Summarize the MRI characteristics of PHA combined with literature reviews.
Results: Among 12 cases (6 males and 6 females; age range, 23-70 years; mean, 53.3 years), one presented as a large mass, two as a diffuse infiltrating tumor, and nine as a mixed pattern of large masses with multiple nodules, all involving both lobes of the liver and ranging from 0.1 cm to 11 cm in diameter. A total of 63 lesions were analyzed, including 21 masses and 42 nodules. 13 masses (61.9%) demonstrated intratumoral hemorrhage. 18 masses (85.7%) demonstrated heterogeneous patchy, ring-shaped, septate, or irregular shaped enhancing foci on late arterial phase (LAP). On dynamic contrast-enhanced MRI (DCE-MRI), 14 masses (66.7%) showed a centripetal or centrifugal pattern of incomplete progressive enhancement. 6 nodules (14.3%) appeared intratumoral hemorrhage. 31 nodules (73.8%) showed no enhancing foci on LAP images and 27 nodules (64.3%) showed enhancement pattern of complete filling, either centripetal or centrifugal pattern. Moreover, 12 cases (100%) exhibited prominent vessels within or adjacent to at least one lesion.
Conclusion: PHA exhibits diverse appearances on MRI. Typical MRI signs include multifoci with intratumoral hemorrhage, prominent vessels within or adjacent to the foci, as well as varied degrees of progressive enhancement with incomplete filling in dominant masses of PHA.
{"title":"Magnetic resonance imaging and clinicopathological findings of primary hepatic angiosarcoma.","authors":"Jingwen Zhang, Jianming Cai, Cheng Yan, Mingzi Gao, Jing Han, Mingxin Zhang, Hailong Yu, Mengmeng Zhang, Changchun Liu, Jinghui Dong, Liqin Zhao","doi":"10.1007/s00261-024-04592-2","DOIUrl":"https://doi.org/10.1007/s00261-024-04592-2","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the magnetic resonance imaging (MRI) and clinicopathological features of primary hepatic angiosarcoma (PHA) and enhance preoperative diagnosis.</p><p><strong>Methods: </strong>MRI and clinicopathological information of 12 cases proved PHA were reviewed. Summarize the MRI characteristics of PHA combined with literature reviews.</p><p><strong>Results: </strong>Among 12 cases (6 males and 6 females; age range, 23-70 years; mean, 53.3 years), one presented as a large mass, two as a diffuse infiltrating tumor, and nine as a mixed pattern of large masses with multiple nodules, all involving both lobes of the liver and ranging from 0.1 cm to 11 cm in diameter. A total of 63 lesions were analyzed, including 21 masses and 42 nodules. 13 masses (61.9%) demonstrated intratumoral hemorrhage. 18 masses (85.7%) demonstrated heterogeneous patchy, ring-shaped, septate, or irregular shaped enhancing foci on late arterial phase (LAP). On dynamic contrast-enhanced MRI (DCE-MRI), 14 masses (66.7%) showed a centripetal or centrifugal pattern of incomplete progressive enhancement. 6 nodules (14.3%) appeared intratumoral hemorrhage. 31 nodules (73.8%) showed no enhancing foci on LAP images and 27 nodules (64.3%) showed enhancement pattern of complete filling, either centripetal or centrifugal pattern. Moreover, 12 cases (100%) exhibited prominent vessels within or adjacent to at least one lesion.</p><p><strong>Conclusion: </strong>PHA exhibits diverse appearances on MRI. Typical MRI signs include multifoci with intratumoral hemorrhage, prominent vessels within or adjacent to the foci, as well as varied degrees of progressive enhancement with incomplete filling in dominant masses of PHA.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142339103","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 : 2024-09-21DOI: 10.1007/s00261-024-04585-1
Yu-Ping Wu, Lan Wu, Jing Ou, Sun Tang, Jin-Ming Cao, Mao-Yong Fu, Tian-Wu Chen
Purpose: To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC).
Methods: A total of 196 resectable patients with ESCC undergoing surgery were retrospectively enrolled, among whom 25% had sMLN. 146 out of 196 patients (from hospital 1) were randomly divided into the training (n = 116) and testing cohorts (n = 30) at an 8:2 ratio, while the remaining 50 patients from hospital 2 constituted the external validation cohort. Least absolute shrinkage and selection operator binary logistic regression was employed for radiomics feature dimensionality reduction and selection, and multivariable logistic regression analysis was used to construct the radiomics prediction model. The clinical features were statistically selected to develop the clinical model. And both the selected radiomics and clinical features were used to develop the combined model. The predictive value of models was assessed using the area under the receiver operating characteristic curves (AUC).
Results: The LN radiomics model was constructed with 9 radiomics features, the clinical model was developed with 3 clinical features, and the combined model was developed using both the LN radiomics and clinical features. However, no statistical radiomics features from ESCC were extracted in dimensionality reduction. Compared to the clinical model, the combined model exhibited superior predictive ability (AUC: 0.893 vs. 0.766, P = 0.003), and the LN radiomics model showed slightly better predictive ability (AUC: 0.860 vs. 0.766, P = 0.153). It was validated in the test and external validation cohorts.
Conclusion: The combined model could assist in preoperatively identifying sMLN in resectable ESCC. It is beneficial for more accurate N staging and clinical comprehensive staging of ESCC, thereby facilitating the clinical physician to make more personalized and standardized treatment strategies.
{"title":"Preoperative identification of small metastatic lymph nodes in esophageal squamous cell carcinoma using CT radiomics of lymph nodes.","authors":"Yu-Ping Wu, Lan Wu, Jing Ou, Sun Tang, Jin-Ming Cao, Mao-Yong Fu, Tian-Wu Chen","doi":"10.1007/s00261-024-04585-1","DOIUrl":"https://doi.org/10.1007/s00261-024-04585-1","url":null,"abstract":"<p><strong>Purpose: </strong>To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC).</p><p><strong>Methods: </strong>A total of 196 resectable patients with ESCC undergoing surgery were retrospectively enrolled, among whom 25% had sMLN. 146 out of 196 patients (from hospital 1) were randomly divided into the training (n = 116) and testing cohorts (n = 30) at an 8:2 ratio, while the remaining 50 patients from hospital 2 constituted the external validation cohort. Least absolute shrinkage and selection operator binary logistic regression was employed for radiomics feature dimensionality reduction and selection, and multivariable logistic regression analysis was used to construct the radiomics prediction model. The clinical features were statistically selected to develop the clinical model. And both the selected radiomics and clinical features were used to develop the combined model. The predictive value of models was assessed using the area under the receiver operating characteristic curves (AUC).</p><p><strong>Results: </strong>The LN radiomics model was constructed with 9 radiomics features, the clinical model was developed with 3 clinical features, and the combined model was developed using both the LN radiomics and clinical features. However, no statistical radiomics features from ESCC were extracted in dimensionality reduction. Compared to the clinical model, the combined model exhibited superior predictive ability (AUC: 0.893 vs. 0.766, P = 0.003), and the LN radiomics model showed slightly better predictive ability (AUC: 0.860 vs. 0.766, P = 0.153). It was validated in the test and external validation cohorts.</p><p><strong>Conclusion: </strong>The combined model could assist in preoperatively identifying sMLN in resectable ESCC. It is beneficial for more accurate N staging and clinical comprehensive staging of ESCC, thereby facilitating the clinical physician to make more personalized and standardized treatment strategies.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142278716","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 : 2024-09-20DOI: 10.1007/s00261-024-04579-z
Wenjie Yan, Haiyan Yu, Chuanfang Xu, Mengshu Zeng, Mingliang Wang
<p><strong>Objective: </strong>To construct a nomogram model based on multi-slice spiral CT imaging features to predict and differentiate between duodenal gastrointestinal stromal tumors (GISTs) and pancreatic head neuroendocrine tumors (NENs), providing imaging evidence for clinical treatment decisions.</p><p><strong>Methods: </strong>A retrospective collection of clinical information, pathological results, and imaging data was conducted on 115 cases of duodenal GISTs and 76 cases of pancreatic head NENs confirmed by surgical pathology at Zhongshan Hospital Fudan University from November 2013 to November 2022. Comparative analysis was performed on the tumor's maximum diameter, shortest diameter, long diameter/short diameter ratio, tumor morphology, tumor border, central position of the lesion, lesion long-axis direction, the relationship between tumor and common bile duct (CBD), duodenal side ulceration of the lesion, calcification, cystic and solid proportion within the tumor, thickened feeding arteries, tumor neovascularization, distant metastasis, and CT values during plain and enhanced scans in arterial and venous phases. Statistical analysis was conducted using t-tests, Mann-Whitney U tests, and χ<sup>2</sup> tests. Univariate and multivariate logistic regression analyses were used to identify independent predictors for differentiating duodenal GISTs from pancreatic head NENs. Based on these independent predictors, a nomogram model was constructed, and the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. The nomogram was validated using a calibration curve, and decision curve analysis was applied to assess the clinical application value of the nomogram.</p><p><strong>Results: </strong>There were significant differences in the duodenal GISTs group and the pancreatic head NENs group in terms of longest diameter (P < 0.001), shortest diameter (P < 0.001), plain CT value (P < 0.001), arterial phase CT value (P < 0.001), venous phase CT value (P = 0.002), lesion long-axis direction (P < 0.001), central position of the lesion (P < 0.001), the relationship between tumor and CBD(< 0.001), border (P = 0.004), calcification (P = 0.017), and distant metastasis (P = 0.018). Multivariate logistic regression analysis identified uncertain location (OR 0.040, 95% CI 0.003-0.549), near the duodenum (OR 0, 95% CI 0-0.009), with the lesion long-axis direction along the pancreas as a reference, along the duodenum (OR 0.106, 95% CI 0.010-1.156) or no significant difference (OR 4.946, 95% CI 0.453-54.017), and the relationship between tumor and CBD (OR 0.013, 95% CI 0.001-0.180), shortest diameter (OR 0.705, 95% CI 0.546-0.909), and calcification (OR 18.638, 95% CI 1.316-263.878) as independent risk factors for differentiating between duodenal GISTs and pancreatic head NENs (all P values < 0.05). The combined diagnostic model's AUC values based on central position of the lesion, calcification, lesion lon
目的构建基于多层螺旋CT成像特征的提名图模型，以预测和区分十二指肠胃肠道间质瘤（GIST）和胰头神经内分泌肿瘤（NEN），为临床治疗决策提供影像学证据：方法：回顾性收集2013年11月至2022年11月期间复旦大学附属中山医院经手术病理证实的115例十二指肠GIST和76例胰头NEN的临床信息、病理结果和影像学资料。对比分析了肿瘤的最大直径、最短直径、长短径比、肿瘤形态、肿瘤边界、病灶中心位置、病灶长轴方向、肿瘤与总胆管（CBD）的关系、病灶十二指肠侧溃疡、钙化、肿瘤内囊实性比例、进食动脉增粗、肿瘤新生血管、远处转移，以及动脉期和静脉期平扫和增强扫描的 CT 值。统计分析采用 t 检验、曼-惠特尼 U 检验和 χ2 检验。单变量和多变量逻辑回归分析用于确定十二指肠 GIST 与胰头 NEN 之间的独立预测因素。根据这些独立预测因子，构建了一个提名图模型，并使用接收者操作特征曲线（ROC）来评估该模型的诊断性能。利用校准曲线验证了提名图，并应用决策曲线分析评估了提名图的临床应用价值：结果：十二指肠 GISTs 组和胰头 NENs 组在最长直径方面存在明显差异（P 结论：十二指肠 GISTs 组和胰头 NENs 组在最长直径方面存在明显差异（P 结论）：基于CT成像特征的提名图模型能有效区分十二指肠GIST和胰头NENs，有助于做出更精确的临床治疗决策。
{"title":"The value of a nomogram model based on CT imaging features in differentiating duodenal gastrointestinal stromal tumors from pancreatic head neuroendocrine tumors.","authors":"Wenjie Yan, Haiyan Yu, Chuanfang Xu, Mengshu Zeng, Mingliang Wang","doi":"10.1007/s00261-024-04579-z","DOIUrl":"https://doi.org/10.1007/s00261-024-04579-z","url":null,"abstract":"<p><strong>Objective: </strong>To construct a nomogram model based on multi-slice spiral CT imaging features to predict and differentiate between duodenal gastrointestinal stromal tumors (GISTs) and pancreatic head neuroendocrine tumors (NENs), providing imaging evidence for clinical treatment decisions.</p><p><strong>Methods: </strong>A retrospective collection of clinical information, pathological results, and imaging data was conducted on 115 cases of duodenal GISTs and 76 cases of pancreatic head NENs confirmed by surgical pathology at Zhongshan Hospital Fudan University from November 2013 to November 2022. Comparative analysis was performed on the tumor's maximum diameter, shortest diameter, long diameter/short diameter ratio, tumor morphology, tumor border, central position of the lesion, lesion long-axis direction, the relationship between tumor and common bile duct (CBD), duodenal side ulceration of the lesion, calcification, cystic and solid proportion within the tumor, thickened feeding arteries, tumor neovascularization, distant metastasis, and CT values during plain and enhanced scans in arterial and venous phases. Statistical analysis was conducted using t-tests, Mann-Whitney U tests, and χ<sup>2</sup> tests. Univariate and multivariate logistic regression analyses were used to identify independent predictors for differentiating duodenal GISTs from pancreatic head NENs. Based on these independent predictors, a nomogram model was constructed, and the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. The nomogram was validated using a calibration curve, and decision curve analysis was applied to assess the clinical application value of the nomogram.</p><p><strong>Results: </strong>There were significant differences in the duodenal GISTs group and the pancreatic head NENs group in terms of longest diameter (P < 0.001), shortest diameter (P < 0.001), plain CT value (P < 0.001), arterial phase CT value (P < 0.001), venous phase CT value (P = 0.002), lesion long-axis direction (P < 0.001), central position of the lesion (P < 0.001), the relationship between tumor and CBD(< 0.001), border (P = 0.004), calcification (P = 0.017), and distant metastasis (P = 0.018). Multivariate logistic regression analysis identified uncertain location (OR 0.040, 95% CI 0.003-0.549), near the duodenum (OR 0, 95% CI 0-0.009), with the lesion long-axis direction along the pancreas as a reference, along the duodenum (OR 0.106, 95% CI 0.010-1.156) or no significant difference (OR 4.946, 95% CI 0.453-54.017), and the relationship between tumor and CBD (OR 0.013, 95% CI 0.001-0.180), shortest diameter (OR 0.705, 95% CI 0.546-0.909), and calcification (OR 18.638, 95% CI 1.316-263.878) as independent risk factors for differentiating between duodenal GISTs and pancreatic head NENs (all P values < 0.05). The combined diagnostic model's AUC values based on central position of the lesion, calcification, lesion lon","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142278718","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 : 2024-09-20DOI: 10.1007/s00261-024-04586-0
Long Zhao, Xin-Yu Tong, Zi-Han Ning, Guo-Qin Wang, Feng-Bo Xu, Jia-Yi Liu, Shuang Li, Nan Zhang, Zhong-Hua Sun, Xi-Hai Zhao, Lei Xu
Objective: To comprehensively evaluate the renal structure and function of patients with renal artery stenosis (RAS) using multiparametric magnetic resonance imaging (MRI), and analyze the correlation between magnetic resonance (MR) parameters and renal function.
Materials and methods: Renal multiparametric MRI was conducted on 62 patients with RAS utilizing a Philips Ingenia CX 3.0 T MRI system. The scanning protocols encompassed arterial spin labeling, phase contrast MRI, diffusion weighted imaging, T1 mapping, and blood oxygen level-dependent MRI. All patients underwent radionuclide renal dynamic imaging to calculate the glomerular filtration rate (GFR) for assessing renal function.
Results: Most MR parameters were correlated with GFR: renal parenchymal volume (R = 0.603), whole kidney renal blood flow (RBF) (R = 0.192), renal cortical RBF (R = 0.294), renal artery mean velocity (R = 0.593), stroke volume (R = 0.599), mean flux (R = 0.629), renal cortical apparent diffusion coefficient (ADC) (R = 0.466), medullary ADC (R = 0.332), cortical T1 value (R = - 0.206), corticomedullary T1 difference (R = 0.204), cortical T2* value (R = 0.448), and medullary T2* value (R = 0.272). The best prediction model for GFR using multiparametric MRI was obtained, including renal PV, whole kidney RBF, cortical RBF, mean velocity, mean flux, and CMD T1.
Conclusion: Multiparametric MRI is a novel noninvasive examination method that can effectively and comprehensively assess the renal structure and function of RAS.
{"title":"A preliminary study of renal function for renal artery stenosis using multiparametric magnetic resonance imaging.","authors":"Long Zhao, Xin-Yu Tong, Zi-Han Ning, Guo-Qin Wang, Feng-Bo Xu, Jia-Yi Liu, Shuang Li, Nan Zhang, Zhong-Hua Sun, Xi-Hai Zhao, Lei Xu","doi":"10.1007/s00261-024-04586-0","DOIUrl":"https://doi.org/10.1007/s00261-024-04586-0","url":null,"abstract":"<p><strong>Objective: </strong>To comprehensively evaluate the renal structure and function of patients with renal artery stenosis (RAS) using multiparametric magnetic resonance imaging (MRI), and analyze the correlation between magnetic resonance (MR) parameters and renal function.</p><p><strong>Materials and methods: </strong>Renal multiparametric MRI was conducted on 62 patients with RAS utilizing a Philips Ingenia CX 3.0 T MRI system. The scanning protocols encompassed arterial spin labeling, phase contrast MRI, diffusion weighted imaging, T1 mapping, and blood oxygen level-dependent MRI. All patients underwent radionuclide renal dynamic imaging to calculate the glomerular filtration rate (GFR) for assessing renal function.</p><p><strong>Results: </strong>Most MR parameters were correlated with GFR: renal parenchymal volume (R = 0.603), whole kidney renal blood flow (RBF) (R = 0.192), renal cortical RBF (R = 0.294), renal artery mean velocity (R = 0.593), stroke volume (R = 0.599), mean flux (R = 0.629), renal cortical apparent diffusion coefficient (ADC) (R = 0.466), medullary ADC (R = 0.332), cortical T1 value (R = - 0.206), corticomedullary T1 difference (R = 0.204), cortical T2<sup>*</sup> value (R = 0.448), and medullary T2<sup>*</sup> value (R = 0.272). The best prediction model for GFR using multiparametric MRI was obtained, including renal PV, whole kidney RBF, cortical RBF, mean velocity, mean flux, and CMD T1.</p><p><strong>Conclusion: </strong>Multiparametric MRI is a novel noninvasive examination method that can effectively and comprehensively assess the renal structure and function of RAS.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142278708","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 : 2024-09-19DOI: 10.1007/s00261-024-04581-5
Junghoan Park, Ijin Joo, Sun Kyung Jeon, Jong-Min Kim, Sang Joon Park, Soon Ho Yoon
Purpose: To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs.
Methods: Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland-Altman analysis and intraclass correlation coefficients (ICC).
Results: The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between - 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970-0.999 and 0.994-0.999, respectively), uniformity (ICCs, 0.985-0.998), entropy (ICCs, 0.931-0.993), elongation (ICCs, 0.978-0.992), and flatness (ICCs, 0.973-0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210-0.831), kurtosis (ICCs, 0.053-0.933), and sphericity (ICCs, 0.368-0.819) displayed relatively low and inconsistent agreement.
Conclusion: Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.
{"title":"Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis.","authors":"Junghoan Park, Ijin Joo, Sun Kyung Jeon, Jong-Min Kim, Sang Joon Park, Soon Ho Yoon","doi":"10.1007/s00261-024-04581-5","DOIUrl":"https://doi.org/10.1007/s00261-024-04581-5","url":null,"abstract":"<p><strong>Purpose: </strong>To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs.</p><p><strong>Methods: </strong>Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland-Altman analysis and intraclass correlation coefficients (ICC).</p><p><strong>Results: </strong>The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between - 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970-0.999 and 0.994-0.999, respectively), uniformity (ICCs, 0.985-0.998), entropy (ICCs, 0.931-0.993), elongation (ICCs, 0.978-0.992), and flatness (ICCs, 0.973-0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210-0.831), kurtosis (ICCs, 0.053-0.933), and sphericity (ICCs, 0.368-0.819) displayed relatively low and inconsistent agreement.</p><p><strong>Conclusion: </strong>Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142278709","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 : 2024-09-19DOI: 10.1007/s00261-024-04587-z
Paul S. Sidhu, Gibran T. Yusuf, Maria E. Sellars, Annamaria Deganello, Cheng Fang, Dean Y. H. Huang
The innovative techniques in ultrasound have added a new dimension to investigating superficially located areas such as the contents of the scrotal sac. High frequency transducers, improved technology with the addition of elastography, contrast enhanced ultrasound and microvascular imaging has resulted in a further improvement in diagnostic capabilities. The ability to clearly demonstrate the presence or absence of vascularity within the area under investigation adds an additional dimension to operator confidence in establishing the presence of infarction, global or segmental, or the walls and cavity of an abscess in the testis or epididymis. Increased vascularity of a tumor aids the differential diagnosis based on the flow dynamics of the microbubble contrast, benign lesions likely to retain contrast. Elastography has the ability to ascertain the stiffness of tissue, and when used in conjunction with other ultrasound methods adds to the understanding of the likelihood of a malignant abnormality being present. All the different techniques come under the umbrella term ‘multiparametric ultrasound’, with the application in the scrotal sac detailed in this article.
{"title":"A review of multiparametric ultrasound imaging in the clinical setting: scrotal contents","authors":"Paul S. Sidhu, Gibran T. Yusuf, Maria E. Sellars, Annamaria Deganello, Cheng Fang, Dean Y. H. Huang","doi":"10.1007/s00261-024-04587-z","DOIUrl":"https://doi.org/10.1007/s00261-024-04587-z","url":null,"abstract":"<p>The innovative techniques in ultrasound have added a new dimension to investigating superficially located areas such as the contents of the scrotal sac. High frequency transducers, improved technology with the addition of elastography, contrast enhanced ultrasound and microvascular imaging has resulted in a further improvement in diagnostic capabilities. The ability to clearly demonstrate the presence or absence of vascularity within the area under investigation adds an additional dimension to operator confidence in establishing the presence of infarction, global or segmental, or the walls and cavity of an abscess in the testis or epididymis. Increased vascularity of a tumor aids the differential diagnosis based on the flow dynamics of the microbubble contrast, benign lesions likely to retain contrast. Elastography has the ability to ascertain the stiffness of tissue, and when used in conjunction with other ultrasound methods adds to the understanding of the likelihood of a malignant abnormality being present. All the different techniques come under the umbrella term ‘multiparametric ultrasound’, with the application in the scrotal sac detailed in this article.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257370","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 : 2024-09-19DOI: 10.1007/s00261-024-04571-7
Tian Shu Yang, Xu Hua Gong, Li Wang, Shan Zhang, Yao Ping Shi, Hai Nan Ren, Yun Qi Yan, Li Zhu, Lei Lv, Yong Ming Dai, Li Jun Qian, Jian Rong Xu, Yan Zhou
{"title":"Comparison of automated with manual 3D qEASL assessment based on MR imaging in hepatocellular carcinoma treated with conventional TACE","authors":"Tian Shu Yang, Xu Hua Gong, Li Wang, Shan Zhang, Yao Ping Shi, Hai Nan Ren, Yun Qi Yan, Li Zhu, Lei Lv, Yong Ming Dai, Li Jun Qian, Jian Rong Xu, Yan Zhou","doi":"10.1007/s00261-024-04571-7","DOIUrl":"https://doi.org/10.1007/s00261-024-04571-7","url":null,"abstract":"","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257371","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 : 2024-09-19DOI: 10.1007/s00261-024-04584-2
Kai Lu, Furui Zhong, Juan Miao, Chong Sun, Kaibo Zhou, Wei Wang, Faqiang Zhang, Hua Yang, Ke Lan
Purpose
Ultrasound and multi-slice spiral computed tomography (CT) are frequently used to assist the diagnosis of acute appendicitis (AA), and the examination results may vary among different demographics. This study aimed to compare the diagnostic accuracy of ultrasound and CT for AA.
Methods
We performed a retrospective analysis of patients diagnosed with AA who underwent emergency surgery at our hospital from March 2021 to August 2023, with postoperative pathological results as the gold standard. Differences in the diagnostic accuracy of ultrasound and CT for different types of AA, age groups, and body mass index (BMI) values were then analyzed.
Results
The overall sample comprised 279 confirmed cases of AA, with 64 cases of simple appendicitis, 127 cases of suppurative appendicitis, and 88 cases of gangrenous appendicitis. For these three pathological classifications, the diagnostic accuracy of ultrasound was 68.75% (44/64), 73.22% (93/127), and 81.81% (72/88), respectively, while the diagnostic accuracy of CT was 71.87% (46/64), 82.67% (105/127), and 90.90% (80/88), respectively. There was no statistically significant difference in the overall diagnostic accuracy between the two methods (P > 0.05). Subgroup analysis showed no difference in diagnostic accuracy between the two methods for patients with normal BMI (P > 0.05). However, for overweight, obese, and elderly patients, CT provided significantly better diagnostic accuracy than ultrasound (P < 0.05).
Conclusion
While ultrasound and CT have similar diagnostic accuracy for different pathological types of AA, CT is more accurate for overweight, obese, and elderly patients.
{"title":"Assessment of diagnostic value of ultrasound and multi-slice spiral computed tomography in acute appendicitis: a retrospective study","authors":"Kai Lu, Furui Zhong, Juan Miao, Chong Sun, Kaibo Zhou, Wei Wang, Faqiang Zhang, Hua Yang, Ke Lan","doi":"10.1007/s00261-024-04584-2","DOIUrl":"https://doi.org/10.1007/s00261-024-04584-2","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Ultrasound and multi-slice spiral computed tomography (CT) are frequently used to assist the diagnosis of acute appendicitis (AA), and the examination results may vary among different demographics. This study aimed to compare the diagnostic accuracy of ultrasound and CT for AA.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We performed a retrospective analysis of patients diagnosed with AA who underwent emergency surgery at our hospital from March 2021 to August 2023, with postoperative pathological results as the gold standard. Differences in the diagnostic accuracy of ultrasound and CT for different types of AA, age groups, and body mass index (BMI) values were then analyzed.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The overall sample comprised 279 confirmed cases of AA, with 64 cases of simple appendicitis, 127 cases of suppurative appendicitis, and 88 cases of gangrenous appendicitis. For these three pathological classifications, the diagnostic accuracy of ultrasound was 68.75% (44/64), 73.22% (93/127), and 81.81% (72/88), respectively, while the diagnostic accuracy of CT was 71.87% (46/64), 82.67% (105/127), and 90.90% (80/88), respectively. There was no statistically significant difference in the overall diagnostic accuracy between the two methods (P > 0.05). Subgroup analysis showed no difference in diagnostic accuracy between the two methods for patients with normal BMI (P > 0.05). However, for overweight, obese, and elderly patients, CT provided significantly better diagnostic accuracy than ultrasound (P < 0.05).</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>While ultrasound and CT have similar diagnostic accuracy for different pathological types of AA, CT is more accurate for overweight, obese, and elderly patients.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269484","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 : 2024-09-18DOI: 10.1007/s00261-024-04565-5
Esther Ro, Gary R. Schooler, Cara E. Morin, Geetika Khanna, Alexander J. Towbin
{"title":"Update on the imaging evaluation of pediatric liver tumors from the ACR Pediatric LI-RADS Working Group","authors":"Esther Ro, Gary R. Schooler, Cara E. Morin, Geetika Khanna, Alexander J. Towbin","doi":"10.1007/s00261-024-04565-5","DOIUrl":"https://doi.org/10.1007/s00261-024-04565-5","url":null,"abstract":"","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257400","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 : 2024-09-18DOI: 10.1007/s00261-024-04578-0
Daniel Freedman, Barun Bagga, Kira Melamud, Thomas O’Donnell, Emilio Vega, Malte Westerhoff, Bari Dane
Purpose
Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatically by thin-client artificial intelligence (AI) mechanisms.
Methods
A retrospective PACS search identified adults who underwent an emergency department contrast-enhanced abdominopelvic CT in 07/2022 (Console Cohort) and 07/2023 (Server Cohort). Coronal and sagittal multiplanar reformatted images (MPR) were created by AI software in the Server cohort. Time to completion of MPR images was compared using 2-sample t-tests for all patients in both cohorts. Two radiologists qualitatively assessed image quality and diagnostic confidence on 5-point Likert scales for 50 consecutive examinations from each cohort. Additionally, they assessed for acute abdominopelvic findings. Continuous variables and qualitative scores were compared with the Mann-Whitney U test. A p < .05 indicated statistical significance.
Results
Mean[SD] time to exam completion in PACS was 8.7[11.1] minutes in the Console cohort (n = 728) and 4.6[6.6] minutes in the Server cohort (n = 892), p < .001. 50 examinations in the Console Cohort (28 women 22 men, 51[19] years) and Server cohort (27 women 23 men, 57[19] years) were included for radiologist review. Age, sex, CTDlvol, and DLP were not statistically different between the cohorts (all p > .05). There was no significant difference in image quality or diagnostic confidence for either reader when comparing the Console and Server cohorts (all p > .05).
Conclusion
Examinations utilizing AI generated MPRs on a thin-client architecture were completed approximately 50% faster than those utilizing reconstructions generated at the console with no statistical difference in diagnostic confidence or image quality.
{"title":"Quality assessment of expedited AI generated reformatted images for ED acquired CT abdomen and pelvis imaging","authors":"Daniel Freedman, Barun Bagga, Kira Melamud, Thomas O’Donnell, Emilio Vega, Malte Westerhoff, Bari Dane","doi":"10.1007/s00261-024-04578-0","DOIUrl":"https://doi.org/10.1007/s00261-024-04578-0","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatically by thin-client artificial intelligence (AI) mechanisms.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>A retrospective PACS search identified adults who underwent an emergency department contrast-enhanced abdominopelvic CT in 07/2022 (Console Cohort) and 07/2023 (Server Cohort). Coronal and sagittal multiplanar reformatted images (MPR) were created by AI software in the Server cohort. Time to completion of MPR images was compared using 2-sample t-tests for all patients in both cohorts. Two radiologists qualitatively assessed image quality and diagnostic confidence on 5-point Likert scales for 50 consecutive examinations from each cohort. Additionally, they assessed for acute abdominopelvic findings. Continuous variables and qualitative scores were compared with the Mann-Whitney U test. A <i>p</i> < .05 indicated statistical significance.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Mean[SD] time to exam completion in PACS was 8.7[11.1] minutes in the Console cohort (<i>n</i> = 728) and 4.6[6.6] minutes in the Server cohort (<i>n</i> = 892), <i>p</i> < .001. 50 examinations in the Console Cohort (28 women 22 men, 51[19] years) and Server cohort (27 women 23 men, 57[19] years) were included for radiologist review. Age, sex, CTDlvol, and DLP were not statistically different between the cohorts (all <i>p</i> > .05). There was no significant difference in image quality or diagnostic confidence for either reader when comparing the Console and Server cohorts (all <i>p</i> > .05).</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Examinations utilizing AI generated MPRs on a thin-client architecture were completed approximately 50% faster than those utilizing reconstructions generated at the console with no statistical difference in diagnostic confidence or image quality.</p>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257372","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}
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