» Articles » PMID: 28258739

Analysis of CT Features and Quantitative Texture Analysis in Patients with Lung Adenocarcinoma: a Correlation with EGFR Mutations and Survival Rates

Overview
Journal Clin Radiol
Specialty Radiology
Date 2017 Mar 5
PMID 28258739
Citations 37
Authors
Affiliations
Soon will be listed here.
Abstract

Aim: To investigate the correlation between conventional computed tomography (CT) features, quantitative texture analysis (QTA), epidermal growth factor receptor (EGFR) mutations, and survival rates in patients with lung adenocarcinoma.

Materials And Methods: Sixty-eight patients were evaluated for conventional CT features and QTA in this retrospective study. A multiple logistic regression analysis and receiver operating characteristics (ROC) curve analysis versus death and EGFR status was performed for CT features and QTA in order to assess correlation between CT features, QTA, EGFR mutations, and survival rates. A p-value <0.05 was regarded to indicate a statistically significant association.

Results: An EGFR mutation was identified in 26/68 tumours (38.2%). A negative association was found between EGFR mutation and emphysema (p < 0.0001) whereas a positive correlation was found with necrosis (p=0.017), air bronchogram (p=0.0304), and locoregional infiltration (p=0.0018). Mean, standard deviation, and skewness were found to have significant correlation with EGFR mutation (p=0.0001; p=0.0001; p=0.0459; Fig 3). The only parameter correlated with the event death was entropy (r=0.2708; p=0.0329).

Conclusion: Both qualitative and quantitative analysis disclosed potential associations between CT features and QTA parameters, EGFR mutations and prognosis; these correlations need to be confirmed in larger studies to be used as imaging biomarkers in the management of patients affected by lung adenocarcinoma.

Citing Articles

Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images.

Ping X, Jiang N, Meng Q, Hu C Tomography. 2024; 10(7):1042-1053.

PMID: 39058050 PMC: 11280730. DOI: 10.3390/tomography10070078.


CT-based radiomics and clinical characteristics for predicting bone metastasis in lung adenocarcinoma patients.

Su Q, Wang B, Guo J, Nie P, Xu W Transl Lung Cancer Res. 2024; 13(4):721-732.

PMID: 38736485 PMC: 11082709. DOI: 10.21037/tlcr-24-38.


A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer.

Ma T, Zhang Y, Zhao M, Wang L, Wang H, Ye Z Front Genet. 2023; 14:1283090.

PMID: 38028587 PMC: 10657897. DOI: 10.3389/fgene.2023.1283090.


Computed Tomography-Based Quantitative Texture Analysis and Gut Microbial Community Signatures Predict Survival in Non-Small Cell Lung Cancer.

Dora D, Weiss G, Megyesfalvi Z, Gallfy G, Dulka E, Kerpel-Fronius A Cancers (Basel). 2023; 15(20).

PMID: 37894458 PMC: 10605408. DOI: 10.3390/cancers15205091.


CT-based radiomics models may predict the early efficacy of microwave ablation in malignant lung tumors.

Zhu F, Yang C, Xia Y, Wang J, Zou J, Zhao L Cancer Imaging. 2023; 23(1):60.

PMID: 37308918 PMC: 10258945. DOI: 10.1186/s40644-023-00571-w.