Detailed Information

Cited 2 time in webofscience Cited 0 time in scopus
Metadata Downloads

A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Canceropen access

Authors
Lim, C.H.[Lim, C.H.]Koh, Y.W.[Koh, Y.W.]Hyun, S.H.[Hyun, S.H.]Lee, S.J.[Lee, S.J.]
Issue Date
Dec-2022
Publisher
International Institute of Anticancer Research
Keywords
machine learning; Non-small cell lung cancer; PD-L1; PET/CT; texture analysis
Citation
Anticancer Research, v.42, no.12, pp.5875 - 5884
Indexed
SCIE
SCOPUS
Journal Title
Anticancer Research
Volume
42
Number
12
Start Page
5875
End Page
5884
URI
https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/105734
DOI
10.21873/anticanres.16096
ISSN
0250-7005
Abstract
Background/Aim: We explored the prediction of programmed cell death ligand 1 (PD-L1) expression level in non-small cell lung cancer using a machine learning approach with positron emission tomography/computed tomography (PET/CT)-based radiomics. Patients and Methods: A total of 312 patients (189 adenocarcinomas, 123 squamous cell carcinomas) who underwent F-18 fluorodeoxyglucose PET/CT were retrospectively analysed. Imaging biomarkers with 46 CT and 48 PET radiomic features were extracted from segmented tumours on PET and CT images using the LIFEx package. Radiomic features were ranked, and the top five best feature subsets were selected using the Gini index based on associations with PD-L1 expression in at least 50% of tumour cells. The areas under the receiver operating characteristic curves (AUCs) of binary classifications afforded by several machine learning algorithms (random forest, neural network, Naïve Bayes, logistic regression, adaptive boosting, stochastic gradient descent, support vector machine) were compared. The model performances were tested by 10-fold cross validation. Results: We developed and validated a PET/CT-based radiomic model predicting PD-L1 expression levels in lung cancer. Long run high grey-level emphasis, homogeneity, mean Hounsfield unit, long run emphasis from CT, and maximum standardised uptake value from PET were the five best feature subsets for positive PD-L1 expression. The Naïve Bayes model (AUC=0.712), with a sensitivity of 75.3% and specificity of 58.2%, outperformed all other classifiers. It was followed by the neural network model (AUC=0.711), random forest (AUC=0.700), logistic regression (AUC=0.673) and adaptive boosting (AUC=0.604). Conclusion: PET/CT-based radiomic features may help clinicians identify tumours with positive PD-L1 expression in a non-invasive manner using machine learning algorithms. © 2022 International Institute of Anticancer Research. All rights reserved.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Medicine > Department of Medicine > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher HYUN, SEUNG HYUP photo

HYUN, SEUNG HYUP
Medicine (Medicine)
Read more

Altmetrics

Total Views & Downloads

BROWSE