Predictive value of 18F-FDG PET/CT radiomics for the PD-L1 expression level in lung adenocarcinoma patients
10.3760/cma.j.cn321828-20210304-00058
- VernacularTitle:18F-FDG PET/CT影像组学对肺腺癌患者PD-L1表达水平的预测价值
- Author:
Huiyuan ZHANG
1
;
Xiangxi MENG
;
Fuxin XIE
;
Yufei SONG
;
Xin ZHOU
;
Lei WANG
;
Nan LI
Author Information
1. 北京大学肿瘤医院暨北京市肿瘤防治研究所核医学科、国家药监局放射性药物研究与评价重点实验室、恶性肿瘤发病机制及转化研究教育部重点实验室 100142
- Keywords:
Lung neoplasms;
Adenocarcinoma;
Programmed cell death 1 receptor;
Positron-emission tomography;
Tomography, X-ray computed;
Deoxyglucose;
Forecasting
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2021;41(8):473-478
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the predictive value of 18F-fluorodeoxyglucose (FDG) PET/CT radiomics for the programmed death ligand-1 (PD-L1) expression level in lung adenocarcinoma patients. Methods:A total of 101 patients (43 males, 58 females; median age 60 years) with histologically confirmed lung adenocarcinoma who received pre-treatment 18F-FDG PET/CT from January 2017 to January 2019 in Peking University Cancer Hospital were included retrospectively. There were 44 patients with positive PD-L1 by immunohistochemical assays, and 57 with PD-L1 negative. Patients were assigned to a training set ( n=71) and a validation set ( n=30). Clinical data, PET/CT radiomics parameters, conventional metabolic parameters, and observed CT characteristics of these patients were included in the models. The filter method and embedded method were used in feature selection. Models based on logistic regression, random forest, XGBoost and Light Gradient Boosting Machine (LightGBM) were trained and evaluated, and the optimal parameters to predict the PD-L1 expression as well as the area under curve (AUC) were attained. Results:All models had predictive ability in the prediction of PD-L1 expression, while LightGBM was more powerful than the others, with the precision for positive and negative predictions of 0.85 and 0.76, respectively. Incorporating clinical data and data derived from thin-section CT images (clinical data+ CT) into the LightGBM, the precision, recall and F1-score for positive and negative patients were 0.71, 0.67, 0.69 and 0.69, 0.73, 0.72, respectively, with the accuracy of 0.70 and the AUC of 0.79. As for clinical data+ PET, the precision, recall and F1-score for positive and negative patients were 0.79, 0.73, 0.76 and 0.75, 0.80, 0.77, respectively, with the accuracy of 0.77 and the AUC of 0.80. As for clinical data+ CT+ PET, the precision, recall and F1-score for positive and negative patients were 0.85, 0.73, 0.79 and 0.76, 0.87, 0.81, respectively, with the accuracy of 0.80 and the AUC of 0.83. Features with significant importance in the model (clinical data+ CT+ PET) were as follows: maximum standardized uptake value (SUV max), peak of standardized uptake value (SUV peak), CT_shape_Maximum2DDiameterSlice, PET_shape_Elongation, PET_gray level co-occurrence matrix (GLCM)_Correlation, etc. Conclusions:Incorporating clinical data, PET/CT radiomics features and conventional metabolic parameters, the PD-L1 expression can be effectively predicted, which help to assist the selection of patients who may benefit from the immunotherapy.
