A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study

Minghao Wu; Yanyan Zhang; Jianing Zhang; Yuwei Zhang; Yina Wang; Feng Chen; Yahong Luo; Shuai He; Yulin Liu; Qian Yang; Yanying Li; Hong Wei; Hong Zhang; Nian Lu; Sicong Wang; Yan Guo; Zhaoxiang Ye; Ying Liu

doi:10.3389/fonc.2021.688679

A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study

Front Oncol. 2022 Jan 10:11:688679. doi: 10.3389/fonc.2021.688679. eCollection 2021.

Authors

Minghao Wu^{1

2}, Yanyan Zhang², Jianing Zhang², Yuwei Zhang², Yina Wang³, Feng Chen⁴, Yahong Luo⁵, Shuai He⁵, Yulin Liu⁶, Qian Yang⁶, Yanying Li⁷, Hong Wei⁸, Hong Zhang⁹, Nian Lu¹⁰, Sicong Wang¹¹, Yan Guo¹¹, Zhaoxiang Ye², Ying Liu²

Affiliations

¹ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
² Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
³ Department of Medical Oncology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁴ Department of Radiology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁵ Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China.
⁶ Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁷ Department of Thoracic Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
⁸ Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
⁹ Department of Radiology, Tianjin Chest Hospital, Tianjin, China.
¹⁰ Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Guangzhou, China.
¹¹ Prognostic Diagnosis, GE Healthcare China, Beijing, China.

Abstract

Objective: Based on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC).

Methods: 131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models' predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models.

Results: For TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (P=0.86, P=0.79).

Conclusion: Compared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support.

Keywords: computed tomography; immunotherapy; non-small-cell lung cancer; radiomics; response prediction.