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MRI radiomics for brain metastasis sub-pathology classification from non-small cell lung cancer: a machine learning, multicenter study

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Abstract

The objective of this study is to develop a machine-learning model that can accurately distinguish between different histologic types of brain lesions in patients with non-small cell lung cancer (NSCLC) when it is not safe or feasible to perform a biopsy. To achieve this goal, the study utilized data from two patient cohorts: 116 patients from Xiangya Hospital and 35 patients from Yueyang Central Hospital. A total of eight machine learning algorithms, including Xgboost, were compared. Additionally, a 3-dimensional convolutional neural network was trained using transfer learning to further evaluate the performance of these models. The SHapley Additive exPlanations (SHAP) method was developed to determine the most important features in the best-performing model after hyperparameter optimization. The results showed that the area under the curve (AUC) for the classification of brain lesions as either lung adenocarcinoma or squamous carcinoma ranged from 0.60 to 0.87. The model based on single radiomics features extracted from contrast-enhanced T1 MRI and utilizing the Xgboost algorithm demonstrated the highest performance (AUC: 0.85) in the internal validation set and adequate performance (AUC: 0.80) in the independent external validation set. The SHAP values also revealed the impact of individual features on the classification results. In conclusion, the use of a radiomics model incorporating contrast-enhanced T1 MRI, Xgboost, and SHAP algorithms shows promise in accurately and interpretably identifying brain lesions in patients with NSCLC.

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Data Availability

The code is open source and freely available at https://github.com/BboyT/BM_NSCLC_subpathology. Data supporting the findings of this study can be provided upon journal request.

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Acknowledgements

This work was supported in part by the High Performance Computing Center of Central South University.

Funding

The work was supported in part by the Science Foundation of Hunan Province (grant number: 2022JJ30992); The work was supported in part by the Project Program of the National Clinical Research Center for Geriatric Disorders(Xiangya Hospital, Grant No. 2022LNJJ10); The work was supported in part by the ERC IMI (101005122), the H2020 (952172), the MRC (MC/PC/21013), the Royal Society (IEC/NSFC/211235), the NVIDIA Academic Hardware Grant Program, NIHR Imperial Biomedical Research Centre (RDA01), Imperial–Nanyang Technological University Collaboration Fund, UKRI MRC with MSIT and NRF Fund, and the UKRI Future Leaders Fellowship (MR/V023799/1).

Author information

Author notes

  1. Zijian Zhang and Rongrong Zhou contributed equally as co-senior authors.

Authors and Affiliations

  1. Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China

    Fuxing Deng, Zhiyuan Liu, Lishui Niu, Xianjing Chu, Zijian Zhang & Rongrong Zhou

  2. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan Province, 410008, China

    Fuxing Deng, Zhiyuan Liu, Lishui Niu, Xianjing Chu, Zijian Zhang & Rongrong Zhou

  3. Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha, 410008, China

    Fuxing Deng, Zhiyuan Liu, Lishui Niu, Xianjing Chu, Quan Cheng, Zijian Zhang & Rongrong Zhou

  4. Department of Radiology, Yueyang Central Hospital, Yueyang, 414000, China

    Wei Fang

  5. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410008, China

    Quan Cheng

  6. National Heart and Lung Institute, Imperial College London, London, UK

    Guang Yang

  7. Cardiovascular Research Centre, Royal Brompton Hospital, London, UK

    Guang Yang

  8. School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK

    Guang Yang

Authors
  1. Fuxing Deng
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  2. Zhiyuan Liu
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  9. Guang Yang
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Contributions

Fuxing Deng: Statistical analysis, machine learning model and writing the paper. Zhiyuan Liu: Identifying cases, experiments. Wei Fang; Identifying cases. Lishui Niu: Discussion and editing the paper. Xianjing Chu: Paper editing and identifying cases. Quan Cheng: Identifying cases and discussion. Zijian Zhang: study design, analysis and editing the paper. RongRong Zhou: Study design, paper editing, financial support and overall study supervision. Zijian Zhang and RongRong Zhou are senior and corresponding authors who contributed equally to this study. Guang Yang: Supervision and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Zijian Zhang or Rongrong Zhou.

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The authors declare no conflict of interest.

Ethics approval

The multicenter study was conducted in accordance with the Declaration of Helsinki and was based on retrospectively identified or prospectively acquired brain metastasis MRI data collection in the participating centers, according to the local institutional review boards guidelines and the Central South University, Xiangya hospital institution ethical committees’ approvals. (Referencenumber:202210235).

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Deng, F., Liu, Z., Fang, W. et al. MRI radiomics for brain metastasis sub-pathology classification from non-small cell lung cancer: a machine learning, multicenter study. Phys Eng Sci Med 46, 1309–1320 (2023). https://doi.org/10.1007/s13246-023-01300-0

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  • DOI: https://doi.org/10.1007/s13246-023-01300-0

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