Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study.

Journal: Frontiers in immunology
PMID: 38596674

Abstract

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung cancer (NSCLC), and identifying candidates likely responding to this advanced treatment is of important clinical significance. The current multi-institutional study aims to develop a deep learning model to predict pathologic complete response (pCR) to neoadjuvant immunotherapy in NSCLC based on computed tomography (CT) imaging and further prob the biologic foundation of the proposed deep learning signature. A total of 248 participants administrated with neoadjuvant immunotherapy followed by surgery for NSCLC at Ruijin Hospital, Ningbo Hwamei Hospital, and Affiliated Hospital of Zunyi Medical University from January 2019 to September 2023 were enrolled. The imaging data within 2 weeks prior to neoadjuvant chemoimmunotherapy were retrospectively extracted. Patients from Ruijin Hospital were grouped as the training set (n = 104) and the validation set (n = 69) at the 6:4 ratio, and other participants from Ningbo Hwamei Hospital and Affiliated Hospital of Zunyi Medical University served as an external cohort (n = 75). For the entire population, pCR was obtained in 29.4% (n = 73) of cases. The areas under the curve (AUCs) of our deep learning signature for pCR prediction were 0.775 (95% confidence interval [CI]: 0.649 - 0.901) and 0.743 (95% CI: 0.618 - 0.869) in the validation set and the external cohort, significantly superior than 0.579 (95% CI: 0.468 - 0.689) and 0.569 (95% CI: 0.454 - 0.683) of the clinical model. Furthermore, higher deep learning scores correlated to the upregulation for pathways of cell metabolism and more antitumor immune infiltration in microenvironment. Our developed deep learning model is capable of predicting pCR to neoadjuvant chemoimmunotherapy in patients with NSCLC.

Authors

  • Wendong Qu
    Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
  • Cheng Chen
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Chuang Cai
    College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China.
  • Ming Gong
    Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
  • Qian Luo
    Behavioral Biology Branch, Walter Reed Army Research Institute Silver Spring, MD, USA.
  • Yongxiang Song
    From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song).
  • Minglei Yang
    Biomedical Engineering, CT Collaboration of Siemens Healthineers, No. 278, Zhouzhu Road, Pudong New District, Shanghai, 201318, People's Republic of China.
  • Min Shi
    School of Education, Fuzhou University of International Studies and Trade, 350000, China.

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