Literature DB >> 30106864

Prediction of Occult Lymph Node Metastasis Using Tumor-to-Blood Standardized Uptake Ratio and Metabolic Parameters in Clinical N0 Lung Adenocarcinoma.

Ming-Li Ouyang1, Kun Tang, Man-Man Xu, Jie Lin, Tian-Cheng Li, Xiang-Wu Zheng.   

Abstract

PURPOSE: We aimed to investigate whether the tumor-to-blood SUV ratio (SUR) and metabolic parameters of F-FDG uptake could predict occult lymph node metastasis (OLM) in clinically node-negative (cN0) lung adenocarcinoma.
MATERIALS AND METHODS: We retrospectively reviewed 157 patients with cN0 lung adenocarcinoma who underwent both preoperative F-FDG PET/CT and surgical resection with the systematic lymph node dissection. The SUVmax, SUVmean, MTV, and total lesion glycolysis (TLG) of the primary tumor was measured on the PET/CT workstation. SURmax, SURmean, and TLGsur were derived from each of them divided by descending aorta SUVmean. These PET parameters and clinicopathological variables were analyzed for OLM.
RESULTS: In our study, OLM was detected in 31 (19.7%) of 157 patients. Significantly higher values of tumor size, SUVmax, SUVmean, MTV, TLGsuv, SURmax, SURmean, and TLGsur were found in patients with OLM. In receiver operating characteristic curve analysis, the optimal cutoff values of the above parameters were 29.50, 4.38, 2.45, 6.37, 44.13, 5.30, 1.86, and 28.24, respectively. The multivariate analysis showed that TLGsur (odds ratio, 1.024; P = 0.002) was the most potent associated factor for the prediction of OLM in cN0 lung adenocarcinoma.
CONCLUSIONS: TLGsur showed the most powerful predictive performance than the other PET parameters for the prediction of OLM in cN0 lung adenocarcinoma. This normalized volumetric parameter would be helpful in selection of sublobar resection or aggressive tailored treatments in patients with cN0 lung adenocarcinoma.

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Year:  2018        PMID: 30106864     DOI: 10.1097/RLU.0000000000002229

Source DB:  PubMed          Journal:  Clin Nucl Med        ISSN: 0363-9762            Impact factor:   7.794


  10 in total

1.  Tumor glycolytic heterogeneity improves detection of regional nodal metastasis in patients with lung adenocarcinoma.

Authors:  Kun-Han Lue; Sung-Chao Chu; Ling-Yi Wang; Yen-Chang Chen; Ming-Hsun Li; Bee-Song Chang; Sheng-Chieh Chan; Yu-Hung Chen; Chih-Bin Lin; Shu-Hsin Liu
Journal:  Ann Nucl Med       Date:  2021-11-24       Impact factor: 2.668

2.  18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma.

Authors:  Lili Wang; Tiancheng Li; Junjie Hong; Mingyue Zhang; Mingli Ouyang; Xiangwu Zheng; Kun Tang
Journal:  Quant Imaging Med Surg       Date:  2021-01

3.  Development and Validation of a 18F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal-Hilar Lymph Nodes in Non-Small-Cell Lung Cancer.

Authors:  Ming-Li Ouyang; Yi-Ran Wang; Qing-Shan Deng; Ye-Fei Zhu; Zhen-Hua Zhao; Ling Wang; Liang-Xing Wang; Kun Tang
Journal:  Front Oncol       Date:  2021-09-08       Impact factor: 6.244

4.  First-in-human immunoPET imaging of HIV-1 infection using 89Zr-labeled VRC01 broadly neutralizing antibody.

Authors:  Denis R Beckford-Vera; Robert R Flavell; Youngho Seo; Enrique Martinez-Ortiz; Maya Aslam; Cassandra Thanh; Emily Fehrman; Marion Pardons; Shreya Kumar; Amelia N Deitchman; Vahid Ravanfar; Brailee Schulte; I-Wei Katherine Wu; Tony Pan; Jacqueline D Reeves; Christopher C Nixon; Nikita S Iyer; Leonel Torres; Sadie E Munter; Tony Hyunh; Christos J Petropoulos; Rebecca Hoh; Benjamin L Franc; Lucio Gama; Richard A Koup; John R Mascola; Nicolas Chomont; Steven G Deeks; Henry F VanBrocklin; Timothy J Henrich
Journal:  Nat Commun       Date:  2022-03-09       Impact factor: 17.694

5.  Prognostic value of node-to-primary tumor maximum standardized uptake value ratio in T1-4N1-3M0 non-small cell lung cancer patients treated with concurrent chemo-radiotherapy.

Authors:  Tian-Cheng Li; Xin Zhao; Yi-Nuo Liu; Guo-Lin Wang; Kai-Feng Liu; Kui Zhao
Journal:  Nucl Med Commun       Date:  2022-05-13       Impact factor: 1.698

6.  Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma.

Authors:  Ming-Li Ouyang; Rui-Xuan Zheng; Yi-Ran Wang; Zi-Yi Zuo; Liu-Dan Gu; Yu-Qian Tian; Yu-Guo Wei; Xiao-Ying Huang; Kun Tang; Liang-Xing Wang
Journal:  Front Oncol       Date:  2022-07-07       Impact factor: 5.738

Review 7.  PET/CT for Predicting Occult Lymph Node Metastasis in Gastric Cancer.

Authors:  Danyu Ma; Ying Zhang; Xiaoliang Shao; Chen Wu; Jun Wu
Journal:  Curr Oncol       Date:  2022-09-11       Impact factor: 3.109

8.  The predictive value of total-body PET/CT in non-small cell lung cancer for the PD-L1 high expression.

Authors:  Bingxin Hu; Huibin Jin; Xiali Li; Xinyu Wu; Junling Xu; Yongju Gao
Journal:  Front Oncol       Date:  2022-09-23       Impact factor: 5.738

9.  Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients.

Authors:  Kai Zheng; Xinrong Wang; Chengzhi Jiang; Yongxiang Tang; Zhihui Fang; Jiale Hou; Zehua Zhu; Shuo Hu
Journal:  Front Med (Lausanne)       Date:  2021-06-18

10.  Development and validation of tumor-to-blood based nomograms for preoperative prediction of lymph node metastasis in lung cancer.

Authors:  Yili Fu; Xiaoying Xi; Yanhua Tang; Xin Li; Xin Ye; Bin Hu; Yi Liu
Journal:  Thorac Cancer       Date:  2021-06-24       Impact factor: 3.500

  10 in total

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