Liver cirrhosis and tumor location can affect the range of intrahepatic microwave ablation zone.

BACKGROUND: The range of an ablation zone (AZ) plays a crucial role in the treatment effect of microwave ablation (MWA). The aim of this study was to analyze the factors influencing the AZ range. METHODS: Fourteen factors in four areas were included: patient-related factors (sex, age), disease-related factors (tumor location, liver cirrhosis), serological factors (ALT, AST, total protein, albumin, total bilirubin, direct bilirubin, and platelets), and MWA parameters (ablation time, power, and needle type). Multiple sequence MRI was used to delineate AZ by three radiologists using 3D Slicer. MATLAB was used to calculate the AZ length, width, and area of the largest section. Linear regression analysis was used to analyze influencing factors. Moreover, a subgroup analysis was conducted for patients with viral hepatitis. RESULT: 220 patients with 290 tumors were included between 2010-2021. In addition to MWA parameters, cirrhosis and tumor location were significant factors that influenced AZ (p < 0.001). The standardized coefficient (beta) of cirrhosis (cirrhosis vs. non-cirrhosis) was positive, which meant cirrhosis would lead to a decrease in AZ range. The beta of tumor location (near the hepatic hilar zone, intermediate zone, and periphery zone) was negative, indicating that AZ range decreased as the tumor location approached the hepatic hilum. For viral hepatitis patients, Fibrosis 4 (FIB4) score was a significant factor influencing AZ (p < 0.001), and the beta was negative, indicating that AZ range decreased as FIB4 increased. CONCLUSION: Liver cirrhosis, tumor location, and FIB4 affect the AZ range and should be considered when planning MWA parameters.

Are all local tumour progressions of HCC related to thermal ablation? A study of the causes and classification of local tumour progression.

OBJECTIVES: Local tumour progression (LTP) is believed to be a negative consequence of imperfect thermal ablation, but we wondered if all LTP is truly due to imperfect ablation. METHODS: This study included 185 LTPs occurring within 1 cm of the ablation zone (AZ) after clinical curative thermal ablation for ≤ 5 cm hepatocellular carcinoma between 2010 and 2019. The AZ was divided into 8 quadrants by coronal, sagittal, and horizontal planes. Two methods, visual assessment through pre- and post-MRI (VA) and tumour mapping for 3D visualisation pre- and post-MRI fusion (MF), were used to assess which AZ quadrant included the shortest ablation margin (AM) by three doctors. LTP subclassification was based on whether LTP contacted the AZ margin (contacted LTP and dissociated-type LTP) and occurrence at different time points (12, 18, and 24 months). RESULTS: Fleiss's Kappa of VA and MF was 0.769 and 0.886, respectively. Cohen's Kappa coefficient between VA and MF was 0.830. For all LTPs, 98/185 (53.0%) occurred in the shortest AM quadrant, which showed a significant central tendency (p < 0.001). However, only 8/51 (15.7%) dissociated - type LTPs and 6/39 (15.4%) LTPs after 24 months occurred in the shortest AM quadrant, which showed no evenly distributed difference (p = 0.360 and 0.303). CONCLUSIONS: MF is an accurate and convenient method to assess the shortest AM quadrant. LTP is a central tendency in the shortest AM quadrant, but dissociated-type and LTPs after 24 months are not, and these LTP types could be considered nonablation-related LTPs. KEY POINTS: • LTPs are not evenly distributed around the AZ. More than half of LTPs occur in the shortest AM quadrant. • Subgroup analysis showed that the occurrence of contacted-type LTPs (tumour margin has direct contact with the AZ) within 24 months after ablation indeed had a high proportion in the shortest AM quadrant, and they could be called ablation-related LTPs. • However, the dissociated-type LTPs (tumour margin adjacent to but not in contact with the AZ) or LTPs occurring beyond 24 months after ablation were evenly distributed around the AZ, and they could be called nonablation-related LTPs.

Radiomics analysis of ultrasound to predict recurrence of hepatocellular carcinoma after microwave ablation.

OBJECTIVE: To develop and validate an ultrasonic radiomics model for predicting the recurrence and differentiation of hepatocellular carcinoma (HCC). Convolutional neural network (CNN) ResNet 18 and Pyradiomics were used to analyze gray-scale-ultrasonic images to predict the prognosis and degree of differentiation of HCC. METHODS: This retrospective study enrolled 513 patients with HCC who underwent preoperative grayscale-ultrasonic imaging, and their clinical characteristics were observed. Patients were randomly divided into training (n = 413) and validation (n = 100) cohorts. CNN ResNet 18 and Pyradiomics were used to analyze ultrasonic images of HCC and peritumoral images to develop a prognostic and differentiation model. Clinical characteristics were integrated into the radiomics model and patients were stratified into high- and low-risk groups. The predictive effect was evaluated using the C-index and receiver operating characteristic (ROC) curve. RESULTS: The model combined with ResNet 18 and clinical characteristics achieved a good predictive ability. The C-indices of early recurrence (ER), late recurrence (LR), and recurrence-free survival (RFS) were 0.695 (0.561-0.789), 0.715 (0.623-0.800) and 0.721 (0.647-0.795), respectively, in the validation cohort, which was superior to the clinical model and ultrasonic semantic model. The model could stratify patients into high- and low-risk groups, which showed significant differences (p < 0.001) in ER, LR, and RFS. The area under the curve for predicting the degree of HCC differentiation was 0.855 and 0.709 in the training and validation cohorts, respectively. CONCLUSION: We developed and validated a radiomics model to predict HCC recurrence and HCC differentiation, which could also acquire pathological information in a noninvasive manner.KEY RESULTSA hepatocellular carcinoma (HCC) prognostic prediction model was developed and validated by convolutional neural network (CNN) ResNet 18-based gray-scale ultrasound (US).A differentiation of HCC prediction model was developed for preoperative prediction avoiding invasive operation.Compared with Pyradiomics, CNN ResNet was more suitable for extracting information from US images.