Assessing the robustness of radiomics/deep learning approach in the identification of efficacy of anti-PD-1 treatment in advanced or metastatic non-small cell lung carcinoma patients

Qianqian Ren; Fu Xiong; Peng Zhu; Xiaona Chang; Guobin Wang; Nan He; Qianna Jin

doi:10.3389/fonc.2022.952749

Assessing the robustness of radiomics/deep learning approach in the identification of efficacy of anti-PD-1 treatment in advanced or metastatic non-small cell lung carcinoma patients

Front Oncol. 2022 Aug 5:12:952749. doi: 10.3389/fonc.2022.952749. eCollection 2022.

Authors

Qianqian Ren^{1

2}, Fu Xiong^{1

2}, Peng Zhu³, Xiaona Chang⁴, Guobin Wang⁵, Nan He⁶, Qianna Jin^{1

2}

Affiliations

¹ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Hubei Province Key Laboratory of Molecular Imaging, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Department of Hepatobiliary Surgery, Wuhan No.1 Hospital, Wuhan, China.
⁴ Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁵ Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁶ Cancer Center, Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Abstract

Administration of anti-PD-1 is now a standard therapy in advanced non-small cell lung carcinoma (NSCLC) patients. The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. This study aimed to develop a robust and non-invasive radiomics/deep learning machine biomarker for predicting the response to immunotherapy in NSCLC patients. Radiomics/deep learning features were exacted from computed tomography (CT) images of NSCLC patients treated with Nivolumab or Pembrolizumab. The robustness of radiomics/deep learning features was assessed against various perturbations, then robust features were selected based on the Intraclass Correlation Coefficient (ICC). Radiomics/deep learning machine-learning classifiers were constructed by combining seven feature exactors, 13 feature selection methods, and 12 classifiers. The optimal model was selected using the mean area under the curve (AUC) and relative standard deviation (RSD). The consistency of image features against various perturbations was high (the range of median ICC: 0.78-0.97), but the consistency was poor in test-retest testing (the range of median ICC: 0.42-0.67). The optimal model, InceptionV3_RELF_Nearest Neighbors classifiers, had the highest prediction efficacy (AUC: 0.96 and RSD: 0.50) for anti-PD-1/PD-L1 treatment. Accuracy (ACC), sensitivity, specificity, precision, and F1 score were 95.24%, 95.00%, 95.50%, 91.67%, and 95.30%, respectively. For successful model robustification, tailoring perturbations for robustness testing to the target dataset is key. Robust radiomics/deep learning features, when paired with machine-learning methodologies, will work on the exactness and the repeatability of anticipating immunotherapy adequacy.

Keywords: NSCLC; deep learning; immunotherapy; radiomics; robustness.