Literature DB >> 29656743

Prognostic stratification model for patients with stage I non-small cell lung cancer adenocarcinoma treated with surgical resection without adjuvant therapies using metabolic features measured on F-18 FDG PET and postoperative pathologic factors.

Yeon-Koo Kang1, Yoo Sung Song2, Sukki Cho3, Sanghoon Jheon4, Won Woo Lee5, Kwhanmien Kim6, Sang Eun Kim7.   

Abstract

PURPOSE: In the management of non-small cell lung cancer (NSCLC), the prognostic stratification of stage I tumors without indication of adjuvant therapy, remains to be elucidated in order to better select patients who can benefit from additional therapies. We aimed to stratify the prognosis of patients with stage I NSCLC adenocarcinoma using clinicopathologic factors and F-18 FDG PET.
MATERIALS AND METHODS: We retrospectively enrolled 128 patients with stage I NSCLC without any high-risk factors, who underwent curative surgical resection without adjuvant therapies. Preoperative clinical and postoperative pathologic factors were evaluated by medical record review. Standardized uptake value corrected with lean body mass (SULmax) was measured on F-18 FDG PET. Among the factors, independent predictors for recurrence-free survival (RFS) were selected using univariate and stepwise multivariate survival analyses. A prognostic stratification model for RFS was designed using the selected factors.
RESULTS: Tumors recurred in nineteen patients (14.8%). Among the investigated clinicopathologic and FDG PET factors, SULmax on PET and spread through air spaces (STAS) on pathologic review were determined to be independent prognostic factors for RFS. A prognostic model was designed using these two factors in the following manner: (1) Low-risk: SULmax ≤ 1.9 and no STAS, (2) intermediate-risk: neither low-risk nor high-risk, (3) high-risk: SULmax>1.9 and observed STAS. This model exhibited significant predictive power for RFS.
CONCLUSION: We showed that FDG uptake and STAS are significant prognostic markers in stage I NSCLC adenocarcinoma treated with surgical resection without adjuvant therapies.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  FDG PET; Non-small cell lung cancer; Prognostic model; STAS

Mesh:

Substances:

Year:  2018        PMID: 29656743     DOI: 10.1016/j.lungcan.2018.02.013

Source DB:  PubMed          Journal:  Lung Cancer        ISSN: 0169-5002            Impact factor:   5.705


  4 in total

1.  Spread Through Air Spaces (STAS) Is Prognostic in Atypical Carcinoid, Large Cell Neuroendocrine Carcinoma, and Small Cell Carcinoma of the Lung.

Authors:  Rania G Aly; Natasha Rekhtman; Xiaoyu Li; Yusuke Takahashi; Takashi Eguchi; Kay See Tan; Charles M Rudin; Prasad S Adusumilli; William D Travis
Journal:  J Thorac Oncol       Date:  2019-05-20       Impact factor: 15.609

2.  A novel analytical approach for outcome prediction in newly diagnosed NSCLC based on [18F]FDG PET/CT metabolic parameters, inflammatory markers, and clinical variables.

Authors:  Lixia Zhang; Caiyun Xu; Xiaohui Zhang; Jing Wang; Han Jiang; Jinyan Chen; Hong Zhang
Journal:  Eur Radiol       Date:  2022-10-12       Impact factor: 7.034

3.  A prediction model integrated genomic alterations and immune signatures of tumor immune microenvironment for early recurrence of stage I NSCLC after curative resection.

Authors:  Chunhong Hu; Long Shu; Chen Chen; Songqing Fan; Qingchun Liang; Hongmei Zheng; Yue Pan; Lishu Zhao; Fangwen Zou; Chaoyuan Liu; Wenliang Liu; Feng-Lei Yu; Xianling Liu; Lijuan Liu; Lingling Yang; Yang Shao; Fang Wu
Journal:  Transl Lung Cancer Res       Date:  2022-01

4.  Correlation analysis between metabolic tumor burden measured by positron emission tomography/computed tomography and the 2015 World Health Organization classification of lung adenocarcinoma, with a risk prediction model of tumor spread through air spaces.

Authors:  Xiao-Yi Wang; Yan-Feng Zhao; Lin Yang; Ying Liu; Yi-Kun Yang; Ning Wu
Journal:  Transl Cancer Res       Date:  2020-10       Impact factor: 1.241

  4 in total

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