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Wang J, Wang J, Huang X, Zhou Y, Qi J, Sun X, Nie J, Hu Z, Wang S, Hong B, Wang H. CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer. BMC Med Imaging 2024; 24:45. [PMID: 38360550 PMCID: PMC10870537 DOI: 10.1186/s12880-024-01221-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/03/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC. METHOD Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application. RESULTS A total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109-0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528-0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669-0.9569) for predicting the efficacy of immunotherapy in NSCLC. CONCLUSION The model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.
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Affiliation(s)
- Jiexiao Wang
- School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Jialiang Wang
- School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Xiang Huang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Yanfei Zhou
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Jian Qi
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- University of Science and Technology of China, Hefei, Anhui, China
| | - Xiaojun Sun
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- University of Science and Technology of China, Hefei, Anhui, China
| | - Jinfu Nie
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Zongtao Hu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Shujie Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China.
| | - Bo Hong
- School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, China.
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China.
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China.
| | - Hongzhi Wang
- School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, China.
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China.
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China.
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