AUTHOR=Liang Chen , Zheng Meiyu , Zou Han , Han Yu , Zhan Yingying , Xing Yu , Liu Chang , Zuo Chao , Zou Jinhai 

TITLE=Deep learning-based image analysis predicts PD-L1 status from 18F-FDG PET/CT images in non-small-cell lung cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1402994

DOI=10.3389/fonc.2024.1402994

ISSN=2234-943X

ABSTRACT=Background: There is still a lack of clinically validated biomarkers to screen lung cancer patients suitable for programmed dead cell-1 (PD-1)/programmed dead cell receptor-1 (PD-L1) immunotherapy. Detection of PD-L1 expression is invasively operated and some PD-L1-negative patients can also benefit from immunotherapy, thus the joint modeling of both Deep learning images and clinical features was used to improve the prediction performance of PD-L1 expression in non-small cell lung cancer(NSCLC).: Retrospective collection of 101 patients diagnosed with pathology in our hospital who underwent 18F FDG PET/CT scans, with lung cancer tissue Tumor Propulsion Score (TPS) ≥ 1% as a positive expression. Extract lesions after preprocessing PET/CT images and using deep learning 3D DenseNet121 to learn lesions in PET, CT, and PET/CT images, fully connected 1024 features are extracted, and clinical features (age, gender, smoking/no smoking history, lesion diameter, lesion volume, maximum standard uptake value of lesions[SUVmax], mean standard uptake value of lesions[SUVmean], total lesion glycolysis[TLG]) are combined for joint modeling based on structured data Category Embeding Model.Results: area under a receiver operating characteristic (ROC) curve (AUC) and accuracy of predicting PD-L1 positive for PET, CT, and PET/CT test groups were 0.814 ± 0.0152, 0.7212 ± 0.0861, and 0.90 ± 0.0605, 0.806 ± 0.023, 0.70 ± 0.074, and 0.950 ± 0.0250, respectively; After joint clinical feature modeling, the AUC and accuracy of predicting PD-L1 positive for PET/CT were 0.96 ± 0.00905 and 0.950 ± 0.0250, respectively.This study combines the features of 18 F-FDG PET/CT images with clinical features using deep learning to predict the expression of PD-L1 in NSCLC, suggesting that 18 F-FDG PET/CT images can be conducted as biomarkers for PD-L1 expression.