Publications by authors named "Kairong Song"
Purpose: This study aimed to develop two preoperative magnetic resonance imaging (MRI) based models for detecting and classifying microvascular invasion (MVI) in early-stage small hepatocellular carcinoma (sHCC) patients.
Methods: MVI is graded as M0 (no invasion), M1 (invasion of five or fewer vessels located within 1 cm of the tumor's peritumoral region), and M2 (invasion of more than five vessels or those located more than 1 cm from the tumor's surface). This study enrolled 395 early-stage sHCC (≤ 3 cm) patients from three centers who underwent preoperative gadopentetate-enhanced MRI.
Background: Vessels encapsulating tumor clusters (VETC) represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma (HCC). However, it seems that no one have focused on predicting VETC status in small HCC (sHCC). This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC (≤ 3 cm) patients.
View Article and Find Full Text PDFArticle Synopsis
- Vessels Encapsulating Tumor Clusters (VETC) are important markers for predicting recurrence and survival in hepatocellular carcinoma (HCC) patients, but little research has focused on using MRI features to predict these patterns.
- This study analyzed 252 patients across three hospitals to identify predictive factors for VETC status, creating two prediction models based on those factors and assessing their accuracy.
- Model 1, which included all identified predictors, performed better than Model 2 (which excluded certain features), showing consistently higher Area Under the Curve (AUC) scores and improvements in prediction accuracy.
Unlabelled: It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response.
View Article and Find Full Text PDFObjective: To evaluate the usefulness of the apparent diffusion coefficient (ADC) in differentiating between benign and malignant LR-3 lesions classified by Liver Imaging Reporting and Data System 2018 (LI-RADS v2018).
Methods: Retrospectively analyzed 88 patients with liver nodules confirmed by pathology and classified as LR-3 by LI-RADS. All patients underwent preoperative contrast-enhanced MR examination, and the following patient-related imaging features were collected: tumor size,nonrim APHE, nonperipheral "washout", enhancing "capsule", mild-moderate T2 hyperintensity, fat in mass, restricted diffusion, and nodule-in-nodule architecture.
Purpose: The purpose of this study was to establish a model for predicting early recurrence (≤2 years) of hepatocellular carcinoma (HCC) after anatomical hepatectomy based on the hepatobiliary phase (HBP) imaging characteristics of gadobenate-enhanced MRI.
Methods: A total of 155 patients who underwent anatomical hepatectomy HCC therapy and gadobenate-enhanced MRI were included retrospectively. The patients were divided into the early recurrence-free group ( = 103) and the early recurrence group ( = 52).