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Theranostics 2021; 11(5):2098-2107. doi:10.7150/thno.48027 This issue Cite

Research Paper

Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images

Panwen Tian^1#, Bingxi He^2,3#, Wei Mu^4#, Kunqin Liu⁵, Li Liu⁶, Hao Zeng⁷, Yujie Liu⁷, Lili Jiang⁸, Ping Zhou⁸, Zhipei Huang^2✉, Di Dong^3,9,10✉, Weimin Li^7✉

1. Department of Respiratory and Critical Care Medicine, Lung Cancer Treatment Centre, West China Hospital, West China Hospital, Sichuan University, Sichuan, China.
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China.
3. CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
4. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China.
5. Department of clinical medicine, Sichuan vocational college of health and rehabilitation, Zigong, Sichuan, China.
6. Department of Respiratory Medicine, West China-Guang'an Hospital, Sichuan University, Sichuan, China.
7. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
8. Department of Pathology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
9. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
10. Zhuhai Precision Medical Center, Zhuhai People's Hospital (affiliated with Jinan University), Zhuhai, China.
#These authors contributed equally to this work and should be considered as co-first authors.

✉ Corresponding authors: Zhipei Huang (E-mail: zhphuangac.cn); Di Dong (E-mail: di.dongac.cn); and, Weimin Li (E-mail: weimin003com).

Abstract

Rationale: This study aimed to use computed tomography (CT) images to assess PD-L1 expression in non-small cell lung cancer (NSCLC) and predict response to immunotherapy.

Methods: We retrospectively analyzed a PD-L1 expression dataset that consisted of 939 consecutive stage IIIB-IV NSCLC patients with pretreatment CT images. A deep convolutional neural network was trained and optimized with CT images from the training cohort (n = 750) and validation cohort (n = 93) to obtain a PD-L1 expression signature (PD-L1ES), which was evaluated using the test cohort (n = 96). Finally, a separate immunotherapy cohort (n = 94) was used to assess the prognostic value of PD-L1ES with respect to clinical outcome.

Results: PD-L1ES was able to predict high PD-L1 expression (PD-L1 ≥ 50%) with areas under the receiver operating characteristic curve (AUC) of 0.78 (95% confidence interval (CI): 0.75~0.80), 0.71 (95% CI: 0.59~0.81), and 0.76 (95% CI: 0.66~0.85) in the training, validation, and test cohorts, respectively. In patients treated with anti-PD-1 antibody, low PD-L1ES was associated with improved progression-free survival (PFS) (median PFS 363 days in low score group vs 183 days in high score group; hazard ratio [HR]: 2.57, 95% CI: 1.22~5.44; P = 0.010). Additionally, when PD-L1ES was combined with a clinical model that was trained using age, sex, smoking history and family history of malignancy, the response to immunotherapy could be better predicted compared to either PD-L1ES or the clinical model alone.

Conclusions: The deep learning model provides a noninvasive method to predict high PD-L1 expression of NSCLC and to infer clinical outcomes in response to immunotherapy. Additionally, this deep learning model combined with clinical models demonstrated improved stratification capabilities.

Keywords: PD-L1 expression, deep learning, computed tomography, immunotherapy, non-small cell lung cancer

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