Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. Of all malignant tumors, HCC has the sixth highest incidence and the third highest mortality [1,2,3,4]. The number of HCC patients in China accounts for 55% of the total number of HCC patients worldwide, and the mortality rate of HCC is increasing worldwide . Currently, curative treatment for HCC is surgical resection, and liver transplantation [6, 7]. Unfortunately, postoperative recurrence remains an important problem for patients after HCC treatment. It has been reported that the recurrence rate of HCC is as high as 70% within 5 years after surgery and 30% within 5 years after liver transplantation . Because of the high recurrence rate of HCC, the survival time of HCC patients is significantly shortened .
Vascular invasion, including both macrovascular and microvascular invasion (MVI), refers to invasive manifestation of tumors and is an independent risk factor for tumor recurrence after surgery [6, 10]. Macrovascular invasion is defined as invasion of the tumor into a major vessel, which can be identified during macroscopic examinations or radiological imaging; in contrast, MVI is defined as the presence of tumor cells in the endothelial-lined vascular lumen, which is only visible under a microscope .
Because MVI is histopathologically diagnosed, it is difficult to diagnose MVI by CT or MRI before surgery. However, early detection and treatment of HCC can reduce the recurrence rate, but HCC is often found in the middle and late stages [11,12,13]. Therefore, it is very important to determine whether MVI exists before surgery to help create further treatment plans and provide early intervention measures, which ultimately helps to reduce the recurrence rate for patients after hepatectomy or liver transplantation. At present, some studies have attempted to diagnose MVI preoperatively by radiologic imaging and specific laboratory tests, and some radiologic studies have shown that MVI is closely related to many factors, including tumor size, tumor number, tumor margin, histologic grade, gross classification of the HCC, among others [10, 14]. However, the feasibility of using these features to identify MVI is still controversial and depends on the subjective judgment of the diagnostic radiologists.
At present, diagnosis through imaging mainly relies on direct observation by radiologists. Radiologists use contrast differences between different tissues to identify diseases and analyze the factors for a diagnosis. The knowledge base, diagnostic experience, work status, etc. of the radiologist can, therefore, affect the accuracy of the diagnosis. Additionally, radiological images contain more information than what is visible to the clinician’s eye, and these “hidden” data can provide much more insight into the tissue of interest than previously thought. Thus, radiomics was born as a “new” method. Radiomics is defined as the high-throughput extraction of quantitative imaging features or textures (radiomics) to decode tissue pathology and create a high-dimensional data set for feature extraction [15, 16]. Radiomics aims to quantify tumor heterogeneity related to changes in cellularity, necrosis, angiogenesis, and extracellular matrix deposition in the tumor microenvironment.
Radiomics provides possibility for early and accurate diagnosis of MVI in HCC patients. Previous studies have shown that radiomics can identify MVI in patients with HCC preoperatively [17,18,19]. However, due to a lack of standardization in radiomics, there have been no studies that analyzed whether there are differences in the results obtained using different radiomics dimensionality reduction methods and modeling methods, and there are no research reports on which dimensionality reduction and modeling methods are most suitable for radiomics.
In this paper, we discuss the application of radiomics for diagnosing MVI by combining the dimensionality reduction and modeling methods commonly used in radiomics research, comparing different methods to evaluate the diagnostic performance of each model and determining the best radiomics model.