scholarly journals Evaluation of spectral CT in differentiating metastatic from non-metastatic lymph nodes in non-small cell lung cancer(NSCLC)

2020 ◽  
Author(s):  
Funan Wang ◽  
Yanwei Wang ◽  
Gang Guo ◽  
Liuhong Zhu
Keyword(s):  
Lung Cancer ◽  
Lymph Nodes ◽  
Iodine Concentration ◽  
Arterial Phase ◽  
Spectral Ct ◽  
Small Cell Lung ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph ◽  
Venous Phase ◽  
The Difference

Abstract Background Lung cancer is the main cause of tumor-correlated deaths, analysis of lymph nodes is crucial to staging of lung cancer. The purpose of the study is to explore the importance of spectral CT imaging in the difference prognostic of metastatic and non-metastatic mediastinal lymph nodes in non-small cell lung cancer. Methods A retrospective examination of 76 patients with non-small cell lung tumor who underwent spectral CT was performed. Quantifiable GSI (Gemstone spectral imaging) parameters (eg, 40 keV, iodine concentration, water concentration) were calculated in non-contrast, arterial and venous phase in 110 mediastinal lymph nodes using AW4.6 (GE HEALTHCARE, USA). Results The CT values ​​of 40 kev, λHU (The slope of Hounsfield unit curve) and IC(values of iodine concentration values), WC(values of water concentration) measured at the arterial or venous phase were not significantly different from those of metastatic growth lymph nodes (P > 0.05). The net value of Arterial phase (nIAP,net value of iodine concentration in Arterial phase) and vein phase (nIVP, net value of iodine concentration in vein phase) were calculated. The value of nIAP was the difference between IAP (iodine concentration of arterial phase) and INCP (iondine concentration of non-contrast phase), while the value of nIVP was the difference between IVP (iodine concentration of venous phase) and INCP. There stood no noteworthy difference in nIAP amid metastatic lymph nodes and non-metastatic lymph nodes (P = 0.110). There was a substantial difference in nIVP amid metastatic lymph nodes and non-metastatic lymph nodes (P = 0.001). Conclusions Compared with qualitative assessment with conventional CT imaging features, quantitative GSI parameters (nIVP) showed higher accuracy for the preoperative diagnosis of mediastal lymph nodal metastases in patients with NSCLC.

scholarly journals 10P Impact of the number of metastatic lymph nodes on survival in pathological stage II-N1 non-small cell lung cancer

2021 ◽  
Vol 16 (4) ◽  
pp. S703
Author(s):  
J. de Ruiter ◽  
A. de Langen ◽  
K. Monkhorst ◽  
A. Veenhof ◽  
H. Klomp ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Nodes ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Stage Ii ◽  
Pathological Stage ◽  
Small Cell Lung ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph

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.

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.

Simultaneous Integrated Boost of intensity modulated radiation therapy to Stage II-III Non-Small Cell Lung Cancer with metastatic lymph nodes

2020 ◽  
Author(s):  
qingsong li ◽  
Na Liang ◽  
Wei-Wei Ouyang ◽  
Sheng-Fa Su ◽  
Zhu Ma ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Radiation Therapy ◽  
Lymph Nodes ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Stage Ii ◽  
Small Cell Lung ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph

Abstract Background: Local tumor failure remains a major problem after radiation-based nonsurgical treatment for unresectable locally advanced Non-Small Cell Lung Cancer （NSCLC）and inoperable stage II NSCLC .The aim of this study was to evaluate the feasibility of Simultaneous Integrated Boos of intensity modulated radiation therapy (SIB-IMRT) to Stage II-III NSCLC with metastatic lymph nodes. Methods: Patients were diagnosed by pathology or PET-CT. PTV was divide into two parts as follow, the PTV of primary tumor (PTVp) and the PTV of metastatic lymph nodes (PTVn) .The radiation doses were simultaneously prescripted 78Gy (BED = 101.48Gy) for PTVp and 60-65Gy (BED = 73.6-81.25Gy) for PTVn, 26f/ 5.2 weeks .Response was scored according to WHO criteria. Radiotherapy toxicity was scored according to RTOG criteria .Hematology and gastrointestinal toxicity were scored according to CTCAE1.0 criteria. Results: A total of 20 patients were enrolled. 17 patients were diagnosed by pathology and 3 patients were diagnosed by PET-CT . All patients were treated with SIB-IMRT. The objective response rate (ORR) was 90%, with CR 25%, PR 65%, NC 10% and PD 0%. Although Radiation toxicitiy was common, there were no grade≥3，with Radiation pneumonitis (10 cases), esophagitis (17 cases) and dermatitis (12 cases). The local control rates at 1, 3 and 5 years were 85%, 75% and 70%, respectively. The overall survival（OS）and local progression-free survival(LPFS) rates at 1, 3 and 5 years were 90%, 42.6%, 35.5% and 84.4%, 35.5% , 28.4%, respectively. The MST was 24 months. Conclusions: SIB-IMRT can significantly improve ORR and suivival for stage II-III NSCLC with metastatic lymph nodes , with high safety and satisfactory efficacy . Keywords: cancer/non-small-cell lung cancer; radiation therapy /SIB-IMRT; efficacy; safety Retrospective Trial Registration： (ChiCTR 2000029304)

LAG-3 in Non–Small-cell Lung Cancer: Expression in Primary Tumors and Metastatic Lymph Nodes Is Associated With Improved Survival

2018 ◽  
Vol 19 (3) ◽  
pp. 249-259.e2 ◽  
Author(s):  
Sigurd M. Hald ◽  
Mehrdad Rakaee ◽  
Inigo Martinez ◽  
Elin Richardsen ◽  
Samer Al-Saad ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Nodes ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Primary Tumors ◽  
Metastatic Lymph Nodes ◽  
Metastatic Lymph
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