Dataset Information

Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration.

ABSTRACT: Aim: To assess the ablative margin (AM) after microwave ablation (MWA) for hepatocellular carcinoma (HCC) with a deep learning-based deformable image registration (DIR) technique and analyze the relation between the AM and local tumor progression (LTP). Patients and Methods: From November 2012 to April 2019, 141 consecutive patients with single HCC (diameter ? 5 cm) who underwent MWA were reviewed. Baseline characteristics were collected to identify the risk factors for the determination of LTP after MWA. Contrast-enhanced magnetic resonance imaging scans were performed within 1 month before and 3 months after treatment. Complete ablation was confirmed for all lesions. The AM was measured based on the margin size between the tumor region and the deformed ablative region. To correct the misalignment, DIR between images before and after ablation was achieved by an unsupervised landmark-constrained convolutional neural network. The patients were classified into two groups according to their AMs: group A (AM ? 5 mm) and group B (AM > 5 mm). The cumulative LTP rates were compared between the two groups using Kaplan-Meier curves and the log-rank test. Multivariate analyses were performed on clinicopathological variables to identify factors affecting LTP. Results: After a median follow-up period of 28.9 months, LTP was found in 19 patients. The mean tumor and ablation zone sizes were 2.3 ± 0.9 cm and 3.8 ± 1.2 cm, respectively. The mean minimum ablation margin was 3.4 ± 0.7 mm (range, 0-16 mm). The DIR technique had higher AUC for 2-year LTP without a significant difference compared with the registration assessment without DL (P = 0.325). The 6-, 12-, and 24-month LTP rates were 9.9, 20.6, and 24.8%, respectively, in group A, and 4.0, 8.4, and 8.4%, respectively, in group B. There were significant differences between the two groups (P = 0.011). Multivariate analysis showed that being >65 years of age (P = 0.032, hazard ratio (HR): 2.463, 95% confidence interval (CI), 1.028-6.152) and AM ? 5 mm (P = 0.010, HR: 3.195, 95% CI, 1.324-7.752) were independent risk factors for LTP after MWA. Conclusion: The novel technology of unsupervised landmark-constrained convolutional neural network-based DIR is feasible and useful in evaluating the ablative effect of MWA for HCC.

SUBMITTER: An C

PROVIDER: S-EPMC7546854 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration.

An Chao C Jiang Yiquan Y Huang Zhimei Z Gu Yangkui Y Zhang Tianqi T Ma Ling L Huang Jinhua J

Frontiers in oncology 20200924

<b>Aim:</b> To assess the ablative margin (AM) after microwave ablation (MWA) for hepatocellular carcinoma (HCC) with a deep learning-based deformable image registration (DIR) technique and analyze the relation between the AM and local tumor progression (LTP). <b>Patients and Methods:</b> From November 2012 to April 2019, 141 consecutive patients with single HCC (diameter ≤ 5 cm) who underwent MWA were reviewed. Baseline characteristics were collected to identify the risk factors for the determi ...[more]

PMID: 33102233

Similar Datasets

Project description:PurposeStandard planning target volume (PTV) margins for lung stereotactic ablative radiation therapy (SABR) are 5 mm. High-dose-rate volumetric modulated arc therapy delivered using flattening filter-free (FFF) beams with modern immobilization systems may allow for PTV margin reduction. This study assesses whether PTV margins can be reduced from 5 to 3 mm.MethodsTarget intrafractional motions derived from pretreatment and posttreatment cone beam computed tomography (CBCT) scans for 33 patients receiving lung SABR treated with 10XFFF energy and 5-mm PTV margins from 2016 to 2019 were used to calculate the required PTV margin. Deformable registration of the planning CT scan and internal gross tumor volume (IGTV) contour to posttreatment CBCT scans for 36 consecutive patients with 4 fraction schedules was completed to capture volume changes and intrafractional movement. Plans were replanned with 3-mm margins and recalculated on each deformed CT scan to assess deformed IGTV (d-IGTV) coverage and organ-at-risk doses.ResultsMargin analysis showed PTV margins may be reduced to 3 mm. The mean d-IGTV coverage (percentage of the d-IGTV receiving ≥100% of the prescription dose [V100%] and the minimum dose covering 99.9% of the d-IGTV volume [D99.9%]) over 4 fractions for each patient was >95% with both margins. With 5-mm PTV margins, all 144 fractions had a d-IGTV V100% of >95% and a D99.9% >95%. With 3-mm PTV margins, the d-IGTV V100% was >95% in 99.3% of fractions (143 of 144) and the D99.9% was >95% in 98.6% of fractions (142 of 144). With 3-mm PTV margins, significant reductions in body V50%, body V80%, the volume of the lung receiving ≥20 Gy, and the mean lung dose and chest wall dose to 0.035 cm3 and 30 cm3 were observed (all P < .001). Using theoretical models, the normal tissue complication probability for radiation pneumonitis decreased by a mean of 0.8% (range, 0.1%-2.7%), and the mean 2-year tumor control probability was 96.1% and 95.2% with 5-mm and 3-mm PTV margins, respectively.ConclusionWith modern treatment and immobilization techniques in lung SABR, 3-mm PTV margins maintain acceptable IGTV coverage, modestly reduce toxicity to organs at risk, and maintain a calculated 2-year local control rate of >95%.

Project description:ObjectivesTo investigate the usefulness of minimal ablative margin (MAM) control by intra-procedural contrast-enhanced CT (CECT) in microwave ablation (MWA) of liver tumors.MethodsA total of 334 consecutive liver tumors (240 hepatocellular carcinomas [HCCs] and 94 colorectal liver metastases [CRLMs]) in 172 patients treated with percutaneous MWA were retrospectively included. MAM of each tumor was assessed after expected ablation completion using intra-procedural CECT, allowing within-session additional ablation to any potentially insufficient margin. On immediate post-MWA MRI, complete ablation coverage of tumor and final MAM status were determined. The cumulative local tumor progression (LTP) rate was estimated by using the Kaplan-Meier method. To identify predictors of LTP, Cox regression analysis with a shared frailty model was performed.ResultsIntra-procedural CECT findings prompted additional ablation in 18.9% (63/334) of tumors. Final complete ablation coverage of tumor and sufficient MAM were determined by MRI to be achieved in 99.4% (332/334) and 77.5% (259/334), and their estimated 6-month, 1-year, and 2-year LTP rates were 3.2%, 7.5%, and 12.9%; and 1.0%, 2.1%, and 6.9%, respectively. Insufficient MAM on post-MWA MRI, perivascular tumor location, and tumor size (cm) were independent risk factors for LTP (hazard ratio = 14.4, 6.0, and 1.1, p < 0.001, p = 0.003, and p = 0.011, respectively), while subcapsular location and histology (HCC vs CRLM) were not.ConclusionsIn MWA of liver tumors, intra-procedural CECT monitoring of minimal ablative margin facilitates identification of potentially suboptimal margins and guides immediate additional intra-session ablation to maximize rates of margin-sufficient ablations, the latter being a highly predictive marker for excellent long-term local tumor control.Key points• In MWA of liver tumors, intra-procedural CECT can identify potentially suboptimal minimal ablative margin, leading to immediate additional ablation in a single treatment session. • Achieving a finally sufficient ablative margin through the MWA with intra-procedural CECT monitoring of minimal ablative margin results in excellent local tumor control.

Dataset Information

Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration.

Publications

Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets