NDLI: 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.

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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.

Content Provider	Europe PMC
Author	Wang, Xiao-Yi Zhao, Yan-Feng Yang, Lin Liu, Ying Yang, Yi-Kun Wu, Ning
Copyright Year	2020
Abstract	BackgroundTumor spread through air spaces (STAS) is an important pattern of invasion and impacts the frequency and location of recurrence. The objective was to assess the correlation between metabolic tumor burden of positron emission tomography/computed tomography (PET/CT) and 2015 World Health Organization (WHO) classification of lung adenocarcinoma, and to establish a risk prediction model of STAS.MethodsWe reviewed 127 consecutive patients. The SUVmax, SUVmean, SUVpeak, MTV, TLG, diameter, and CTV were measured. All risk factors were analyzed by multivariate logistic regression analysis; regression coefficients and odds ratios were calculated for independent risk factors. A STAS risk prediction model was created using the regression coefficients to determine the predictive probability (PP).ResultsThe nodule types and SUV were significantly correlated with 2015 WHO pathological categories (P<0.001). Most of (83.3%) the lepidic predominant adenocarcinoma (LPA) appeared as non-solid or part-solid nodules with the lowest SUV (P<0.05). There was a significant difference in STAS distribution among different nodule types (P=0.000). STAS was significantly correlated with SUVmax (P=0.000), SUVmean (P=0.000), SUVpeak (P=0.000), TLG (P=0.001), and diameter (P=0.044). The risk prediction model of STAS was established. The PP of STAS and the incidence of STAS were analyzed using the ROC curve (AUC =0.759, P=0.000). The sensitivity, specificity, and accuracy of the predictive model for STAS were 47.1%, 88.6%, and 71.1%, respectively.ConclusionsThe LPA appeared as non-solid nodule with low SUV without STAS has a good prognosis. SUV and TLG are valuable predictive indices in the prediction of STAS. The predictive model developed in predicting the incidence of STAS has good specificity and accuracy.
ISSN	2218676X
Journal	Translational Cancer Research
Volume Number	9
PubMed Central reference number	PMC8798457
Issue Number	10
PubMed reference number	35117249
e-ISSN	22196803
DOI	10.21037/tcr-20-1934
Language	English
Publisher	AME Publishing Company
Publisher Date	2020-10-01
Access Restriction	Open
Rights License	Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/. 2020 Translational Cancer Research. All rights reserved.
Subject Keyword	PET/CT lung adenocarcinoma 2015 WHO classification spread through air spaces (STAS) multivariate logistic regression analysis risk factor
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Cancer Research Oncology

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