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Introduction Deformable image registration (DIR) is a required tool in any adaptive radiotherapy program to help account for anatomical changes that occur during a multifraction treatment. SmartAdapt is a DIR tool from Varian incorporated within the eclipse treatment planning system, that can be used for contour propagation and transfer of PET, MRI, or computed tomography (CT) data. The purpose of this work is to evaluate the registration and contour propagation accuracy of SmartAdapt for thoracic CT studies using the guidelines from AAPM TG 132. Methods To evaluate the registration accuracy of SmartAdapt the mean target registration error (TRE) was measured for ten landmarked 4DCT images from the https://www.dir‐labs.com/ which included 300 landmarks matching the inspiration and expiration phase images. To further characterize the registration accuracy, the magnitude of deformation for each 4DCT was measured and compared against the mean TRE for each study. Contour propagation accuracy was evaluated using 22 randomly selected lung cancer cases from our center where there was either a replan, or the patient was treated for a new lesion within the lung. Contours evaluated included the right and left lung, esophagus, spinal canal, heart and the GTV and the results were quantified using the DICE similarity coefficient. Results The mean TRE from all ten cases was 1.89 mm, the maximum mean TRE per case was 3.8 mm from case #8, which also had the most landmark pairs with displacements >2 cm. For contour propagation accuracy, the DICE coefficient results for left lung, right lung, heart, esophagus, and spinal canal were 0.93, 0.94, 0.90, 0.61, and 0.82 respectively. Conclusion The results from our study demonstrate that for thoracic images SmartAdapt in most cases will be accurate to below 2 mm in registration error unless there is deformation greater than 2 cm.

Background Head and neck cancers are commonly treated with radiation therapy, but due to possible volume changes, plan adaptation may be required during the course of treatment. Currently, plan adaptations consume significant clinical resources. Existing methods to evaluate the need for plan adaptation requires deformable image registration (DIR) to a new CT simulation or daily cone beam CT (CBCT) images and the recalculation of the dose distribution. In this study, we explore a tool to assist the decision for plan adaptation using a CBCT without re‐computation of dose, allowing for rapid online assessment. Methods This study involved 18 head and neck cancer patients treated with CBCT image guidance who had their treatment plan modified based on a new CT simulation (ReCT). Dose changes were estimated using different methods and compared to the current gold standard of using DIR between the planning CT scan (PCT) and ReCT with recomputed dose. The first and second methods used DIR between the PCT and daily CBCT with the planned dose or recalculated dose from the ReCT respectively, with the dose transferred to the CBCT using rigid registration. The necessity of plan adaptation was assessed by the change in dose to 95% of the planning target volume (D95) and mean dose to the parotids. Results The treatment plans were adapted clinically for all 18 patients but only 7 actually needed an adaptation yielding 11 unnecessary adaptations. Applying a method using the daily CBCT with the planned dose distribution would have yielded only four unnecessary adaptations and no missed adaptations: a significant improvement from that done clinically. Conclusion Using the DIR between the planning CT and daily CBCT can flag cases for plan adaptation before every fraction while not requiring a new re‐planning CT scan and dose recalculation.

Aim: New parameters that correlate with overall survival were identified in patients with liver lesions treated with radiation therapy. Methods: Pretreatment information and parameters of radiation treatment plans for 129 metastatic and 66 hepatocellular carcinoma liver cancer patients were analyzed. Study end points included overall survival collected from patient charts and electronic records. Results: Two practical nomograms were constructed for primary hepatocellular carcinoma and liver metastasis patients. For patients with a Child-Pugh A, radiation dose escalation provided a significant survival benefit. However, for those with Child-Pugh B or C, increasing dose does not impact on survival. Conclusion: The developed models can potentially guide dose selection and provide prognostic information but still require external validation.

Introduction According to the Surveillance, Epidemiology and End Results (SEER) data, cancerous involvement of the liver is on an increase over the last three decades. It occurs worldwide in all races and carries a poor prognosis. Currently, considerable progress has been made in patient selection, staging, surgery, chemotherapy agents, and stereotactic radiotherapy in both primary and metastatic liver cancers with improved outcomes. While there is evidence of the prognostic factors of liver function, the involvement of the portal vein, inferior vena cava thrombosis, lesion size, radiation dose, number of fractions, and SBRT techniques, there is no study evaluating outcomes with the location of the lesion. Our aim in this retrospective study was to explore the correlation of tumor location from the portal vein bifurcation (vascular wall) and the radiotherapy outcome (survival) in hepatocellular cancer. Methods Contrast-enhanced computed tomography (CT) studies in 86 patients with liver cancer were retrospectively reviewed in an institutional review board (IRB)-approved database to determine the distance to the bifurcation point of the portal vein from tumor’s centre of mass (distance tumor bifurcation: DTB) and from the edge point of the planning target volume closest to the bifurcation (distance edge bifurcation: DEB). The mean dose to the sphere of 1 cm diameter around the bifurcation point (mean dose at bifurcation: MDB) was calculated. These parameters were tested as predictors of patient outcomes using univariate and multivariate analysis as two groups of patients. Results Only the DEB correlation with survival for hepatocellular carcinoma (HCC) was found to be significant (P = 0.028). A larger MDB is caused by a smaller DTB and a smaller DEB. The hazard ratio for DTB, DEB, and MDB were 0.48, 0.41, and 1.05, respectively. The DEB was found to be a better predictor of outcomes (overall survival) compared to the DTB and MDB parameters. The close proximity of the tumor to the blood supply vessels was a decisive factor. The DTB parameter is also dependent on the size of the tumor and this factor weakens the correlation of this parameter on survival data. The inclusion of the dosimetric and geometric location, as well as distance parameters in predictive models for liver cancer patients, was shown to benefit the pre-selection of treatment options for liver cancer patients treated with radiotherapy. Conclusion For hepatocellular cancer patients, the distance between the edge point of the planning treatment volume (PTV) to the portal vein bifurcation (DEB) of more than 2 cm was found to be a predictor of survival.

Deformable image registration (DIR) is emerging as a tool in radiation therapy for calculating the cumulative dose distribution across multiple fractions of treatment. Unfortunately, due to the variable nature of DIR algorithms and dependence of performance on image quality, registration errors can result in dose accumulation errors. In this study, landmarked images were used to characterize the DIR error throughout an image space and determine its impact on dosimetric analysis. Ten thoracic 4DCT images with 300 landmarks per image study matching the end-inspiration and end-expiration phases were obtained from 'dir-labs'. DIR was performed using commercial software MIM Maestro. The range of dose uncertainty (RDU) was calculated at each landmark pair as the maximum and minimum of the doses within a sphere around the landmark in the end-expiration phase. The radius of the sphere was defined by a measure of DIR error which included either the actual DIR error, mean DIR error per study, constant errors of 2 or 5 mm, inverse consistency error, transitivity error or the distance discordance metric (DDM). The RDUs were evaluated using the magnitude of dose uncertainty (MDU) and inclusion rate (IR) of actual error lying within the predicted RDU. The RDU was calculated for 300 landmark pairs on each 4DCT study for all measures of DIR error. The most representative RDU was determined using the actual DIR error with a MDU of 2.5 Gy and IR of 97%. Across all other measures of DIR error, the DDM was most predictive with a MDU of 2.5 Gy and IR of 86%, closest to the actual DIR error. The proposed method represents the range of dosimetric uncertainty of DIR error using either landmarks at specific voxels or measures of registration accuracy throughout the volume.

Deformable image registration (DIR) has become a common tool in medical imaging across both diagnostic and treatment specialties, but the methods used offer varying levels of accuracy. Evaluation of DIR is commonly performed using manually selected landmarks, which is subjective, tedious and time consuming. We propose a semi-automated method that saves time and provides accuracy comparable to manual selection. Three landmarking methods including manual (with two independent observers), scale invariant feature transform (SIFT), and SIFT with manual editing (SIFT-M) were tested on 10 thoracic 4DCT image studies corresponding to the 0% and 50% phases of respiration. Results of each method were evaluated against a gold standard (GS) landmark set comparing both mean and proximal landmark displacements. The proximal method compares the local deformation magnitude between a test landmark pair and the closest GS pair. Statistical analysis was done using an intra class correlation (ICC) between test and GS displacement values. The creation time per landmark pair was 22, 34, 2.3, and 4.3 s for observers 1 and 2, SIFT, and SIFT-M methods respectively. Across 20 lungs from the 10 CT studies, the ICC values between the GS and observer 1 and 2, SIFT, and SIFT-M methods were 0.85, 0.85, 0.84, and 0.82 for mean lung deformation, and 0.97, 0.98, 0.91, and 0.96 for proximal landmark deformation, respectively. SIFT and SIFT-M methods have an accuracy that is comparable to manual methods when tested against a GS landmark set while saving 90% of the time. The number and distribution of landmarks significantly affected the analysis as manifested by the different results for mean deformation and proximal landmark deformation methods. Automatic landmark methods offer a promising alternative to manual landmarking, if the quantity, quality and distribution of landmarks can be optimized for the intended application.

Objective: To describe a database created for storing and analysis of patient specific data related to pre-treatment condition, treatment planning, and outcomes with a long term future objective to predict the optimal radiation therapy for new patients. Method: Construction of the centralized database for the collection of sufficient information for outcome analysis and modeling will be comprised of a SQL database and a commercial DICOM-RT PACS (MIM) server. Development of dedicated software for automatic transfer of DICOM-RT files from different sources to MIM PACS through unique import procedures. Planning dose objectives and constraints from Tomoplan (Accuray), Pinnacle (Philips) and Eclipse (Varian) treatment planning systems and daily position correction information from treatment units are transferred to the SQL database. Results and conclusion: A centralized database for all patient specific data, treatment planning and outcome information allows for determining correlations between treatment parameters and patient outcomes. The proximity between tumor and organs at risk is demonstrated as useful in determining optimal planning parameters in addition to the planning data of previously treated patients. The proposed database can perform automated analysis regarding quality assurance, dose accumulation for multiple treatments on different machines and assist physicians in choosing the optimal treatment modality.