T he reduction of lung cancer mortality by almost 20% in the National Lung Screening Trial can be partially attributed to the extensive use of low-dose CT for lung cancer screening in high-risk populations, which led to the improved detection of pulmonary nodules and early stage lung cancers (1,2). Pulmonary nodules are classified as solid, pure ground-glass, and part-solid nodules (PSNs) based on CT phenotyping, with PSNs being an important cancer predictor in the Brock model that is widely used to assess the malignant risk of pulmonary nodules (3). Moreover, adenocarcinomas manifesting as PSNs have been suggested to be a distinct subtype, most of which are confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA) by abnormality, requiring a different management strategy due to different clinical-pathologic characteristics (4). Furthermore, evidence from histological specimens suggests that the solid components of lung nodules have a close-knit association with the invasive component of adenocarcinomas (5-7). Among the different subtypes of lung adenocarcinoma, IA has the worst prognosis, with the others having an almost 100% survival probability (8). Therefore, lobectomy is often recommended for patients with IA, whereas limited resections are suggested for patients with AIS or MIA (9).
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.”
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