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[18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

This study aims to develop and validate a radiomics model based on 18F-fluorodeoxyglucose positron emission tomography–computed tomography ([18F]FDG PET-CT) images to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC).

Materials and methods

One hundred eighty-five patients receiving neoadjuvant chemoimmunotherapy for NSCLC at 5 centers from January 2019 to December 2022 were included and divided into a training cohort and a validation cohort. Radiomics models were constructed via the least absolute shrinkage and selection operator (LASSO) method. The performances of models were evaluated by the area under the receiver operating characteristic curve (AUC). In addition, genetic analyses were conducted to reveal the underlying biological basis of the radiomics score.

Results

After the LASSO process, 9 PET-CT radiomics features were selected for pCR prediction. In the validation cohort, the ability of PET-CT radiomics model to predict pCR was shown to have an AUC of 0.818 (95% confidence interval [CI], 0.711, 0.925), which was better than the PET radiomics model (0.728 [95% CI, 0.610, 0.846]), CT radiomics model (0.732 [95% CI, 0.607, 0.857]), and maximum standard uptake value (0.603 [95% CI, 0.473, 0.733]) (p < 0.05). Moreover, a high radiomics score was related to the upregulation of pathways suppressing tumor proliferation and the infiltration of antitumor immune cell.

Conclusion

The proposed PET-CT radiomics model was capable of predicting pCR to neoadjuvant chemoimmunotherapy in NSCLC patients.

Clinical relevance statement

This study indicated that the generated 18F-fluorodeoxyglucose positron emission tomography–computed tomography radiomics model could predict pathological complete response to neoadjuvant chemoimmunotherapy, implying the potential of our radiomics model to personalize the neoadjuvant chemoimmunotherapy in lung cancer patients.

Key Points

• Recognizing patients potentially benefiting neoadjuvant chemoimmunotherapy is critical for individualized therapy of lung cancer.

• [18F]FDG PET-CT radiomics could predict pathological complete response to neoadjuvant immunotherapy in non-small cell lung cancer.

• [18F]FDG PET-CT radiomics model could personalize neoadjuvant chemoimmunotherapy in lung cancer patients.

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Abbreviations

[18F]FDG PET-CT:

18F-fluorodeoxyglucose positron emission tomography–computed tomography

AUC:

Area under curve

GSEA:

Gene set enrichment analysis

ICC:

Intraclass correlation coefficients

IDI:

Integrated discrimination improvement

LASSO:

Least absolute shrinkage and selection operator

NRI:

Net reclassification improvement

NSCLC:

Non-small cell lung cancer

pCR:

Pathological complete response

ROC:

Receiver operating characteristic curve

ssGSEA:

Single sample gene set enrichment analysis

SUVmax:

Maximum standardized uptake value

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Funding

This study was supported by Shanghai Municipal Health Commission (203040225); Ningbo Top Medical and Health Research Program (2022030208); Ningbo Health Branding Subject Fund (PPXK2018-05); and Medicine and Public Health Scientific Projects in Zhejiang Province (2020KY270).

Author information

Author notes

  1. Minglei Yang, Xiaoxiao Li, and Chuang Cai contributed equally to this article.

Authors and Affiliations

  1. Department of Thoracic Surgery, Ningbo No. 2 Hospital, Ningbo, China

    Minglei Yang, Enkuo Zheng & Huangkai Zhu

  2. Shanghai Universal Cloud Medical Imaging Diagnostic Center, Shanghai, China

    Xiaoxiao Li, Chunli Liu & Huazheng Shi

  3. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China

    Chuang Cai

  4. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China

    Minjie Ma

  5. Department of Thoracic Surgery, The Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China

    Wendong Qu

  6. Tailai Inc, Shanghai, China

    Sheng Zhong

  7. Shandong Key Laboratory of Infectious Respiratory Diseases, Shandong Public Health Clinical Center, Shandong University, Shandong, China

    Feng Jin

Authors
  1. Minglei Yang
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Corresponding authors

Correspondence to Wendong Qu, Feng Jin or Huazheng Shi.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Huazheng Shi.

Conflict of interest

Dr. Sheng Zhong is an employee of Tailai Inc. We declare that Tailai Inc. products or services are not related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approvals from Shanghai Universal Cloud Medical Imaging Diagnostic Center, Ningbo No. 2 Hospital, Affiliated Hospital of Zunyi Medical College, Shandong Provincial Chest Hospital, and The First Hospital of Lanzhou University were obtained.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Yang, M., Li, X., Cai, C. et al. [18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10503-8

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  • DOI: https://doi.org/10.1007/s00330-023-10503-8

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