CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer.
10.3881/j.issn.1000-503X.15705
- Author:
Zhen-Chen ZHU
1
;
Min-Jiang CHEN
2
;
Lan SONG
1
;
Jin-Hua WANG
1
;
Ge HU
3
;
Wei HAN
4
;
Wei-Xiong TAN
5
;
Zhen ZHOU
5
;
Xin SUI
1
;
Wei SONG
1
;
Zheng-Yu JIN
1
Author Information
1. Department of Radiology, PUMC Hospital,CAMS and PUMC,Beijing 100730,China.
2. Department of Respiratory and Critical Care Medicine, PUMC Hospital,CAMS and PUMC,Beijing 100730,China.
3. Translational Medicine Center, PUMC Hospital,CAMS and PUMC,Beijing 100730,China.
4. Department of Epidemiology and Biostatistics,Institute of Basic Medical Sciences,CAMS and PUMC,Beijing 100005,China.
5. Beijing Deepwise & League of PHD Technology Co.,Ltd.,Beijing 100081,China.
- Publication Type:Journal Article
- Keywords:
CT;
immune checkpoint inhibitor;
non-small cell lung cancer;
weighted radiomic score
- MeSH:
Humans;
Carcinoma, Non-Small-Cell Lung/drug therapy*;
Lung Neoplasms/drug therapy*;
B7-H1 Antigen/therapeutic use*;
Retrospective Studies;
Programmed Cell Death 1 Receptor;
Tomography, X-Ray Computed;
Immunotherapy
- From:
Acta Academiae Medicinae Sinicae
2023;45(5):794-802
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.
