Structural and functional radiomics for lung cancer

Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form Medomics.

Automated summary

Radiomics plays a crucial role in the detection, diagnosis, and prediction of lung cancer, covering pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis. However, challenges such as limited large datasets, methodology standardization, the black-box nature of deep learning, and reproducibility need to be addressed for the clinical implementation of radiomics. Future directions include developing a safer and more efficient model training mode, merging multi-modality images, and combining multi-discipline or multi-omics to form Medomics.