Accuracy of machine learning in preoperative identification of genetic mutation status in lung cancer: A systematic review and meta-analysis

Jinzhan Chen ¹, Ayun Chen ², Shuwen Yang ¹

⁰Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology +

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May 11, 2024

View abstract on PubMed

Summary

Machine learning (ML) models show promise in detecting genetic mutation status in non-small cell lung cancer (NSCLC). Radiomics-based approaches, particularly with MRI data, offer high accuracy for EGFR mutation detection, though clinical factors remain important.

Area Of Science

Oncology
Radiology
Bioinformatics

Background

Non-small cell lung cancer (NSCLC) treatment is increasingly personalized based on genetic mutation status.
Accurate detection of these mutations is crucial for guiding therapy.
Machine learning (ML) offers potential for non-invasive mutation detection.

Purpose Of The Study

To systematically review and meta-analyze the performance of ML models in detecting genetic mutation status in NSCLC patients.
To evaluate the diagnostic accuracy of radiomics-based ML models using various imaging modalities.

Main Methods

Systematic literature search of PubMed, Cochrane, Embase, and Web of Science up to July 2023.
Meta-analysis of 128 original studies, focusing on ML models (primarily radiomics) for detecting EGFR, ALK, KRAS, and BRAF mutations.
Analysis of models based on clinical features, CT, MRI, and PET-CT radiomics.

Main Results

ML models, particularly radiomics-based, demonstrated significant performance in detecting genetic mutations in NSCLC.
For EGFR mutation detection, aggregated c-indexes in validation sets ranged from 0.750 to 0.822, with MRI-based radiomics showing higher accuracy (0.816).
Combined clinical and radiomics models also showed high performance, with c-indexes exceeding 0.80 for CT, MRI, and PET-CT.

Conclusions

Radiomics-based ML methods show high accuracy for early discrimination of EGFR mutation status in NSCLC.
Clinical variables play a significant role and should be considered alongside radiomics.
Future research should explore radiomics' accuracy for other gene mutations in NSCLC.

Keywords:

Gene mutation Machine learning Non–small cell lung cancer Radiomics