Dataset Information

Evaluation of Hepatic Toxicity after Repeated Stereotactic Body Radiation Therapy for Recurrent Hepatocellular Carcinoma using Deformable Image Registration.

ABSTRACT: This study aimed to evaluate hepatic toxicity after repeated stereotactic body radiation therapy (SBRT) for recurrent hepatocellular carcinoma (HCC) using deformable image registration (DIR). Between January 2007 and December 2015, 85 patients who underwent two sessions of SBRT for HCC treatment were retrospectively analyzed. A DIR technique was used to calculate the cumulative dose of the first and second SBRT to the normal liver by matching two computed tomography simulation images. The Dice similarity coefficient (DSC) index was calculated to evaluate DIR accuracy. Before the first and second SBRT, 6 (7.1%) and 12 (14.1%) patients were Child-Pugh class B, respectively. Median tumor size was 1.7?cm before both SBRT treatments. Mean DSC index value was 0.93, being >0.9 in 79 (92.9%) registrations. Median cumulative mean liver dose (MLD) was 9.3?Gy (interquartile range, 7.6-11.7). Radiation-induced liver disease developed in three patients, and two of them, with Child-Pugh class B, experienced irreversible liver function deterioration following the second SBRT. The DIR method provided reliable information regarding cumulative doses to the liver. In patients with Child-Pugh class A liver function, repeated SBRT for small recurrent HCC could be safely performed with acceptable hepatic toxicity.

SUBMITTER: Lee S

PROVIDER: S-EPMC6212421 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Evaluation of Hepatic Toxicity after Repeated Stereotactic Body Radiation Therapy for Recurrent Hepatocellular Carcinoma using Deformable Image Registration.

Lee Sumin S Kim Hojin H Ji Yunseo Y Cho Byungchul B Kim Su Ssan SS Jung Jinhong J Kwak Jungwon J Park Jin-Hong JH Lee Sang-Wook SW Kim Jong Hoon JH Yoon Sang Min SM

Scientific reports 20181101 1

This study aimed to evaluate hepatic toxicity after repeated stereotactic body radiation therapy (SBRT) for recurrent hepatocellular carcinoma (HCC) using deformable image registration (DIR). Between January 2007 and December 2015, 85 patients who underwent two sessions of SBRT for HCC treatment were retrospectively analyzed. A DIR technique was used to calculate the cumulative dose of the first and second SBRT to the normal liver by matching two computed tomography simulation images. The Dice s ...[more]

PMID: 30385839

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Project description:The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B?spline?based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast?Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM?DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0~3.1mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0–1.9 mm in the prostate, 1.9–2.4 mm in the rectum, and 1.8–2.1 mm over the entire patient body. Sinusoidal errors induced by B?spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient?specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient?dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy. PACS numbers: 87.10.Kn, 87.55.km, 87.55.Qr, 87.57.nj

Project description:The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B-spline-based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast-Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM-DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0 ~ 3.1 mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0-1.9 mm in the prostate, 1.9-2.4mm in the rectum, and 1.8-2.1 mm over the entire patient body. Sinusoidal errors induced by B-spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient-specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy.

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: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.

Project description:PurposeThe registration of multiple imaging studies to radiation therapy computed tomography simulation, including magnetic resonance imaging, positron emission tomography-computed tomography, etc. is a widely used strategy in radiation oncology treatment planning, and these registrations have valuable roles in image guidance, dose composition/accumulation, and treatment delivery adaptation. The NRG Oncology Medical Physics subcommittee formed a working group to investigate feasible workflows for a self-study credentialing process of image registration commissioning.Methods and materialsThe American Association of Physicists in Medicine (AAPM) Task Group 132 (TG132) report on the use of image registration and fusion algorithms in radiation therapy provides basic guidelines for quality assurance and quality control of the image registration algorithms and the overall clinical process. The report recommends a series of tests and the corresponding metrics that should be evaluated and reported during commissioning and routine quality assurance, as well as a set of recommendations for vendors. The NRG Oncology medical physics subcommittee working group found incompatibility of some digital phantoms with commercial systems. Thus, there is still a need to provide further recommendations in terms of compatible digital phantoms, clinical feasible workflow, and achievable thresholds, especially for future clinical trials involving deformable image registration algorithms. Nine institutions participated and evaluated 4 commonly used commercial imaging registration software and various versions in the field of radiation oncology.Results and conclusionsThe NRG Oncology Working Group on image registration commissioning herein provides recommendations on the use of digital phantom/data sets and analytical software access for institutions and clinics to perform their own self-study evaluation of commercial imaging systems that might be employed for coregistration in radiation therapy treatment planning and image guidance procedures. Evaluation metrics and their corresponding values were given as guidelines to establish practical tolerances. Vendor compliance for image registration commissioning was evaluated, and recommendations were given for future development.

Dataset Information

Evaluation of Hepatic Toxicity after Repeated Stereotactic Body Radiation Therapy for Recurrent Hepatocellular Carcinoma using Deformable Image Registration.

Publications

Evaluation of Hepatic Toxicity after Repeated Stereotactic Body Radiation Therapy for Recurrent Hepatocellular Carcinoma using Deformable Image Registration.

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