Characterizing perfusion defects in metastatic lymph nodes at an early stage using high-frequency ultrasound and micro-CT imaging

2021 ◽  
Author(s):  
Teppei Yamaki ◽  
Ariunbuyan Sukhbaatar ◽  
Radhika Mishra ◽  
Ryoichi Kikuchi ◽  
Maya Sakamoto ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
High Frequency ◽  
Early Stage ◽  
Ct Imaging ◽  
Micro Ct ◽  
High Frequency Ultrasound ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph ◽  
Perfusion Defects ◽  
Frequency Ultrasound

scholarly journals Morphologic Features of Cutaneous T-Cell Lymphomas Using Dermoscopy and High Frequency Ultrasound

2020 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Iris Wohlmuth-Wieser ◽  
Joel M. Ramjist ◽  
Neil Shear ◽  
Raed Alhusayen
Keyword(s):  
T Cell ◽  
High Frequency ◽  
Early Stage ◽  
Skin Lesions ◽  
Diagnostic Tools ◽  
Skin Thickness ◽  
High Frequency Ultrasound ◽  
T Cell Lymphomas ◽  
Standardized Parameters ◽  
Frequency Ultrasound

The diagnosis of cutaneous T-cell lymphomas (CTCL) is frequently delayed by a median of three years and requires the clinical evaluation of an experienced dermatologist and a confirmatory skin biopsy. Dermoscopy and high-frequency ultrasound (HFUS) represent two non-invasive diagnostic tools. While dermoscopy is inexpensive and widely used for the diagnosis of melanoma and non-melanoma skin cancers, HFUS of skin lymphomas represents a novel diagnostic approach that is not yet implemented in the routine dermatologic practice. The aim of our study was to prospectively assess skin lesions of patients with either CTCL patches or plaques with dermoscopy and HFUS and to compare the findings with atopic dermatitis (AD) and psoriasis. Thirteen patients with an established diagnosis of CTCL, psoriasis, or AD were studied: Dermoscopy features including spermatozoa-like structures and the presence of white scales could assist in differentiating between early-stage CTCL and AD. HFUS measurements of the skin thickness indicated increased epidermal-, thickness in CTCL, and psoriasis compared with AD. Our results support the use of dermoscopy as a useful tool to diagnose CTCL. HFUS could augment the dermatologic assessment, but further studies will be needed to define standardized parameters.

scholarly journals Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging

2019 ◽  
Author(s):  
Xue Sha ◽  
Guan Zhong Gong ◽  
Qing Tao Qiu ◽  
Jing Hao Duan ◽  
Deng Wang Li ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Ct Images ◽  
Ct Imaging ◽  
Arterial Phase ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph ◽  
Venous Phase

Abstract Background: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). Methods: We selected 231 mediastinal LNs confirmed by pathology results as the subjects, which were divided into training (n=163) and validation cohorts (n=68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). Results: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. Conclusions: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

A Retrospective Analysis of the Diameter of Metastatic Lymph Nodes in Apparently Early Stage Endometrial Cancer

2008 ◽  
Vol 94 (5) ◽  
pp. 681-685 ◽  
Author(s):  
Nurettin Boran ◽  
Derya Akdag ◽  
Filiz Halici ◽  
Gokhan Tulunay ◽  
Taner Turan ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lymph Nodes ◽  
Retrospective Analysis ◽  
Early Stage ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph ◽  
Early Stage Endometrial Cancer

scholarly journals Differentiation of mediastinal metastatic lymph nodes in NSCLC based on radiomic features of different phase CT imaging

2019 ◽  
Author(s):  
Xue Sha ◽  
Guan Zhong Gong ◽  
Qing Tao Qiu ◽  
Jing Hao Duan ◽  
Deng Wang Li ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Ct Images ◽  
Ct Imaging ◽  
Arterial Phase ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph ◽  
Venous Phase

Abstract Background To develop radiomic models based on different phases of computed tomography (CT) imaging and investigate the efficacy of models to diagnose mediastinal metastatic lymph nodes in non-small cell lung cancer (NSCLC).Methods We selected 231 mediastinal lymph nodes confirmed by pathology results as the subjects, which were divided into training (n=163) and validation cohorts (n=68). The regions of interest (ROIs) were delineated on CT scans of the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images of each phase. Least absolute shrinkage and selection operator (LASSO) was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders of 1-6) based on radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV).Results A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6. All of the models showed superior differentiation, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 VS 0.925, 0.860 VS 0.769, 0.871 VS 0.882 and 0.906 VS 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879, 0.919 to 0.949, 0979 and the NPV increased from 0.821, 0.789 to 0.878, 0.900 in the training group, respectively.Conclusion CT radiomic models based on different phases all showed high accuracy and precision in the diagnosis of LNM in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model can be further improved.

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