Cancer presents a significant global public health challenge, impacting human health on a broad scale. In recent years, the rapid advancement of big data-based bioinformatics has unveiled crucial potential in precision oncology through various omics research methods. The advent of radiomics has notably expanded the application scope of medical imaging in the field. However, due to the multi-level and multifactorial nature of tumor initiation and progression, a single omics information remains insufficient to meet the demands for advancing precision oncology strategies. Multi-omics research has become an emerging trend. The research paradigm integrating radiomics with other omics offers a novel perspective for personalized decision-making in oncology. Nevertheless, there persists a need to introduce more integrated new technologies and theories to expedite the progress of this field.
Lung cancer is the malignant tumor with the highest incidence and mortality rates worldwide, and its high lethality is primarily due to its subtle early symptoms, with most cases being diagnosed at an advanced stage. Currently, the diagnosis of lung cancer mainly relies on tissue biopsy to obtain pathological evidence, but this method has limitations such as high invasiveness, restricted sampling, and the risk of complications. Therefore, developing safe, effective, and non-invasive strategies for the early screening and diagnosis of lung cancer (stages Ⅰ/Ⅱ) holds significant clinical importance. As a key component of liquid biopsy, exosomes can stably carry a variety of biological molecular information from their cells of origin. Studies have shown that microRNAs, long non-coding RNAs, circular RNAs, and specific proteins abundantly present in exosomes exhibit abnormal expression during the development of lung cancer, demonstrating high diagnostic value. Compared to traditional detection methods, exosome-based detection offers advantages such as non-invasiveness, repeatability, ease of operation, and cost-effectiveness. This article systematically reviews recent research progress on exosomes as liquid biopsy biomarkers for the early diagnosis and screening of lung cancer, focusing on their potential clinical applications, and explores the prospects of exsomes in the early intervention, precise diagnosis, and prognosis improvement for lung cancer.
With the widespread adoption of lung cancer screening and growing public awareness, the detection rate of pulmonary nodules has increased substantially, posing new challenges for clinical management. Artificial intelligence (AI) has emerged as a powerful tool across the entire management spectrum of pulmonary nodules. Beyond improving detection sensitivity and consistency in chest radiographs and low-dose CT, AI has demonstrated promising applications in malignancy risk assessment, molecular subtype prediction, preoperative 3D planning, intraoperative navigation, and postoperative monitoring. This review summarizes recent advances in the application of AI to pulmonary nodule screening, longitudinal evaluation, pathology prediction, multi-omics integration, and perioperative management. It also discusses the technical characteristics, clinical performance, current limitations, and future prospects of various AI models. The continuous development of AI is reshaping the clinical pathway of pulmonary nodules toward more efficient and individualized care.