Literature DB >> 22968069

Volume-based parameters of (18)F-fluorodeoxyglucose positron emission tomography/computed tomography improve outcome prediction in early-stage non-small cell lung cancer after surgical resection.

Seung Hyup Hyun1, Joon Young Choi, Kwhanmien Kim, Jhingook Kim, Young Mog Shim, Sang-Won Um, Hojoong Kim, Kyung-Han Lee, Byung-Tae Kim.   

Abstract

OBJECTIVE: To evaluate the prognostic significance and predictive performance of volume-based parameters of F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) in early-stage non-small cell lung cancer (NSCLC).
BACKGROUND: Although surgical resection remains the optimal treatment for early-stage NSCLC, approximately 40% of patients with stage I and 60% of patients with stage II NSCLC relapse and die within 5 years after curative resection. Therefore, identification of additional prognostic biomarkers is needed to develop risk-adapted treatment strategies.
METHODS: We retrospectively reviewed 529 consecutive patients with pathologically proven early-stage NSCLC who underwent preoperative F-FDG PET/CT. Maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for the primary tumors were measured. Overall survival (OS) and disease-free survival (DFS) were assessed by the Kaplan-Meier method. The prognostic significance of PET parameters and other clinicopathological variables was assessed by Cox proportional hazards regression analysis. To evaluate and compare the predictive performance of PET parameters, time-dependent receiver operating characteristic (ROC) curve analysis was used.
RESULTS: In the multivariate analyses, volume-based parameters of PET (MTV and TLG) that were analyzed as continuous variables were significantly associated with an increased risk of recurrence (P = 0.001 for MTV, P < 0.001 for TLG) and death (P = 0.009 for MTV, P = 0.007 for TLG), after adjusting for age, sex, histology, tumor stage, and type of surgery. SUVmax analyzed as a continuous variable was not a significant prognostic factor for both DFS (P = 0.056) and OS (P = 0.525). In the time-dependent ROC curve analysis, the volume-based parameter of PET showed better predictive performance than SUVmax (P < 0.001).
CONCLUSIONS: The volume-based parameter of PET is an independent prognostic factor for survival in addition to pathological tumor-node-metastasis stage and a promising tool for better prediction of outcome in patients with early-stage NSCLC.

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Year:  2013        PMID: 22968069     DOI: 10.1097/SLA.0b013e318262a6ec

Source DB:  PubMed          Journal:  Ann Surg        ISSN: 0003-4932            Impact factor:   12.969


  47 in total

1.  A Pilot Study of Texture Analysis of Primary Tumor [18F]FDG Uptake to Predict Recurrence in Surgically Treated Patients with Non-small Cell Lung Cancer.

Authors:  Masatoyo Nakajo; Megumi Jinguji; Tetsuya Shinaji; Masaya Aoki; Atsushi Tani; Yoshiaki Nakabeppu; Masayuki Nakajo; Masami Sato; Takashi Yoshiura
Journal:  Mol Imaging Biol       Date:  2019-08       Impact factor: 3.488

2.  The role of metabolic tumor volume and total lesion glycolysis on ¹⁸F-FDG PET/CT in the prognosis of epithelial ovarian cancer.

Authors:  Jeong Won Lee; Arthur Cho; Jae-Hoon Lee; Mijin Yun; Jong Doo Lee; Young Tae Kim; Won Jun Kang
Journal:  Eur J Nucl Med Mol Imaging       Date:  2014-05-23       Impact factor: 9.236

3.  A new PET/CT volumetric prognostic index for non-small cell lung cancer.

Authors:  Hao Zhang; Kristen Wroblewski; Yulei Jiang; Bill C Penney; Daniel Appelbaum; Cassie A Simon; Ravi Salgia; Yonglin Pu
Journal:  Lung Cancer       Date:  2015-04-09       Impact factor: 5.705

4.  Developing and validating a novel metabolic tumor volume risk stratification system for supplementing non-small cell lung cancer staging.

Authors:  Yonglin Pu; James X Zhang; Haiyan Liu; Daniel Appelbaum; Jianfeng Meng; Bill C Penney
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-06-07       Impact factor: 9.236

5.  Relationship between Overall Survival of Patients with Non-Small Cell Lung Cancer and Whole-Body Metabolic Tumor Burden Seen on Postsurgical Fluorodeoxyglucose PET Images.

Authors:  Chenpeng Zhang; Chuanhong Liao; Bill C Penney; Daniel E Appelbaum; Cassie A Simon; Yonglin Pu
Journal:  Radiology       Date:  2015-01-14       Impact factor: 11.105

6.  Volume-based predictive biomarkers of sequential FDG-PET/CT for sunitinib in cancer of unknown primary: identification of the best benefited patients.

Authors:  Yifei Ma; Wei Xu; Ruojing Bai; Yiming Li; Hongyu Yu; Chunshan Yang; Huazheng Shi; Jian Zhang; Jidong Li; Chenguang Wang; Jianru Xiao
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-09-07       Impact factor: 9.236

Review 7.  Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better?

Authors:  Hyung-Jun Im; Tyler Bradshaw; Meiyappan Solaiyappan; Steve Y Cho
Journal:  Nucl Med Mol Imaging       Date:  2017-09-19

8.  Metabolic tumour burden assessed by ¹⁸F-FDG PET/CT associated with serum CA19-9 predicts pancreatic cancer outcome after resection.

Authors:  Hua-Xiang Xu; Tao Chen; Wen-Quan Wang; Chun-Tao Wu; Chen Liu; Jiang Long; Jin Xu; Ying-Jian Zhang; Run-Hao Chen; Liang Liu; Xian-Jun Yu
Journal:  Eur J Nucl Med Mol Imaging       Date:  2014-02-13       Impact factor: 9.236

9.  Baseline metabolic tumour volume is an independent prognostic factor in Hodgkin lymphoma.

Authors:  Salim Kanoun; Cédric Rossi; Alina Berriolo-Riedinger; Inna Dygai-Cochet; Alexandre Cochet; Olivier Humbert; Michel Toubeau; Emmanuelle Ferrant; François Brunotte; René-Olivier Casasnovas
Journal:  Eur J Nucl Med Mol Imaging       Date:  2014-05-09       Impact factor: 9.236

10.  Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer.

Authors:  Sara Carvalho; Ralph T H Leijenaar; Emmanuel Rios Velazquez; Cary Oberije; Chintan Parmar; Wouter van Elmpt; Bart Reymen; Esther G C Troost; Michel Oellers; Andre Dekker; Robert Gillies; Hugo J W L Aerts; Philippe Lambin
Journal:  Acta Oncol       Date:  2013-09-09       Impact factor: 4.089

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