ABSTRACT
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
ABSTRACT
Endoglin is a proliferation-associated cell membrane antigen and overexpressed in the angiogenic vasculature of solid tumors. However, the applications of endoglin (ENG)-targeted radioimmunotheray in hepatocellular carcinoma have not been reported yet. Therefore, the aim of this study was the visualization of both the development of hepatocellular carcinoma (HCC) tumor burden and therapeutic effect with ENG-targeted (131)I-anti-ENG mAb (A8), via in vivo noninvasive fluorescence imaging (NIFLI) of SMMC7721-green fluorescent protein (GFP) cells. A8 showed a dose-dependent, time-dependent suppression on the proliferation of SMMC7721-GFP cells and human umbilical vein endothelial cells (HUVECs) in vitro. Tube formation assay showed that (131)I-A8 markedly inhibits HUVECs to form extensive and enclosed tube networks. The results showed that the radiochemical purity of (131)I-A8 was 92.8 % and (131)I-A8 maintained more stable in serum than in saline and had high affinity against SMMC7721-GFP cells. The pharmacokinetics of (131)I-A8 was in accordance with the two-compartment model, with a rapid distribution phase and a slow decline phase. NIFLI exhibited a good relation between the fluorescent signal and tumor volume in vivo. Furthermore, treatment with (131)I-A8 resulted in significant tumor-growth suppression on the basis of the reducing fluorescent signal and a remarkably decreased tumor weight in treated animals. These results were further verified by RT-PCR and immunohistochemistry staining. Our findings indicate that (131)I-A8 can be used as ENG-targeted therapy for hepatocellular carcinoma, and noninvasive fluorescence imaging provides valuable information on tumor burden and effectiveness of therapy.
