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CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy

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

Abstract

Objectives

The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both.

Materials and methods

This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient’s CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients.

Results

The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619–0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646–0.846).

Conclusions

Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future.

Key Points

  • The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646–0.846).

  • The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.

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Abbreviations

ALK:

Anaplastic lymphocyte kinase

AUC:

Area under the ROC curve

CEA:

Carcinoembryonic antigen

CT:

Computed tomography

CYFRA21-1:

Cytokeratin 19 fragment antigen 21–1

EGFR:

Epidermal growth factor receptor

GLCM:

Gray Level Co-occurrence Matrix

GLDM:

Gray Level Dependence Matrix

GLRLM:

Gray Level Run Length Matrix

GLSZM:

Gray Level Size Zone Matrix

IP:

Independent predictive

LBP:

Local binary pattern

LoG:

Laplacian of Gaussian

MITK:

Medical Imaging Interaction Toolkit

MRMR:

Minimum Redundancy Maximum Relevance

NCCN:

National Comprehensive Cancer Network

NGTDM:

Neighborhood Gray Tone Difference Matrix

NSCLC:

Non-small cell lung cancer

NSE:

Neuron-specific enolase

OS:

Overall survival

PDL1:

Programmed cell death receptor ligand 1

PFS:

Progression-free survival

RECIST:

Response Evaluation Criteria in Solid Tumors

RF:

Random Forest

ROS1:

C-ros oncogene 1

TKI:

Tyrosine kinase inhibitor

VOI:

Volume of interest

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Acknowledgements

This thesis would not have been possible without the consistent and valuable reference materials that I received from my supervisors, whose insightful guidance and enthusiastic encouragement in the course of my shaping this thesis definitely gained my deepest gratitude.

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers 81871439; 61801474; 81872475; 81372413); Guangdong Provincial Key Research and Development Program (grant number 2019B010152001); Chinese Academy of Sciences-Iranian Vice Presidency for Science and Technology Silk Road Science Fund (grant number GJHZ1857); Science and Technology Plan Projects of Jiangsu (grant number BE2019665); Shandong Key Research and Development Plan (grant numbers 2017CXGC1209; 2017GSF18164); Outstanding Youth Natural Science Foundation of Shandong Province (grant number JQ201423); Jinan Clinical Medicine Science and Technology Innovation Plan (grant number 201704095); National Key Research and Development Program of China (grant number 2016YFC0904700).

Author information

Author notes

  1. Fengchang Yang and Jiayi Zhang contributed equally and are co-first authors.

Authors and Affiliations

  1. Department of Radiology, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China

    Fengchang Yang

  2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China

    Jiayi Zhang, Wei Xia, Rui Zhang, Xingyu Zhao, Junming Jian & Xin Gao

  3. School of Mathematics and Information Engineering, Wenzhou University Oujiang College, Wenzhou, 325035, Zhejiang, China

    Liu Zhou

  4. Department of Radiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250012, Shandong, China

    Haifeng Wei

  5. Cheeloo College of Medicine, Shandong University, Jinan, 250000, Shandong, China

    Jinxue Feng

  6. Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Pharmaceutical Valley New Drug Creation Platform, Jinan New District, No. 3 Building, Jinan, 250101, Shandong, China

    Xin Gao

  7. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong University, No.440 Jiyan Road, Huaiyin District, Jinan, 250100, Shandong, China

    Shuanghu Yuan

  8. Shandong First Medical University and Shandong Academy of Medical Sciences, No. 6699 Qingdao Road, Jinan, 250117, Shandong, China

    Shuanghu Yuan

  9. Zhengzhou University, No. 100 Science Road, Zhengzhou, 450001, Henan, China

    Shuanghu Yuan

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  1. Fengchang Yang
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Corresponding authors

Correspondence to Xin Gao or Shuanghu Yuan.

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Guarantor

The scientific guarantor of this publication is Shuanghu Yuan.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Jiayi Zhang kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• randomized controlled trial

• performed at one institution

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Yang, F., Zhang, J., Zhou, L. et al. CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy. Eur Radiol 32, 1538–1547 (2022). https://doi.org/10.1007/s00330-021-08277-y

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  • DOI: https://doi.org/10.1007/s00330-021-08277-y

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