Converting Treatment Plans From Helical Tomotherapy to L-Shape Linac: Clinical Workflow and Dosimetric Evaluation.

This work evaluated a commercial fallback planning workflow designed to provide cross-platform treatment planning and delivery. A total of 27 helical tomotherapy intensity-modulated radiotherapy plans covering 4 anatomical sites were selected, including 7 brain, 5 unilateral head and neck, 5 bilateral head and neck, 5 pelvis, and 5 prostate cases. All helical tomotherapy plans were converted to 7-field/9-field intensity-modulated radiotherapy and volumetric-modulated radiotherapy plans through fallback dose-mimicking algorithm using a 6-MV beam model. The planning target volume (PTV) coverage ( D1, D99, and homogeneity index) and organs at risk dose constraints were evaluated and compared. Overall, all 3 techniques resulted in relatively inferior target dose coverage compared to helical tomotherapy plans, with higher homogeneity index and maximum dose. The organs at risk dose ratio of fallback to helical tomotherapy plans covered a wide spectrum, from 0.87 to 1.11 on average for all sites, with fallback plans being superior for brain, pelvis, and prostate sites. The quality of fallback plans depends on the delivery technique, field numbers, and angles, as well as user selection of structures for organs at risk. In actual clinical scenario, fallback plans would typically be needed for 1 to 5 fractions of a treatment course in the event of machine breakdown. Our results suggested that <1% dose variance can be introduced in target coverage and/or organs at risk from fallback plans. The presented clinical workflow showed that the fallback plan generation typically takes 10 to 20 minutes per case. Fallback planning provides an expeditious and effective strategy for transferring patients cross platforms, and minimizing the untold risk of a patient missing treatment(s)

The influence of MRI scan position on patients with oropharyngeal cancer undergoing radical radiotherapy

<p>Background: The purpose of this study was to demonstrate how magnetic resonance imaging (MRI) patient position protocols influence registration quality in patients with oropharyngeal cancer undergoing radical radiotherapy and the consequences for gross tumour volume (GTV) definition and radiotherapy planning.</p> <p>Methods and materials: Twenty-two oropharyngeal patients underwent a computed tomography (CT), a diagnostic MRI (MRID) and an MRI in the radiotherapy position within an immobilization mask (MRIRT). Clinicians delineated the GTV on the CT viewing the MRID separately (GTVC); on the CT registered to MRID (GTVD) and on the CT registered to MRIRT (GTVRT). Planning target volumes (PTVs) were denoted similarly. Registration quality was assessed by measuring disparity between structures in the three set-ups. Volumetric modulated arc therapy (VMAT) radiotherapy planning was performed for PTVC, PTVD and PTVRT. To determine the dose received by the reference PTVRT, we optimized for PTVC and PTVD while calculating the dose to PTVRT. Statistical significance was determined using the two-tailed Mann–Whitney or two-tailed paired student t-tests.</p> <p>Results: A significant improvement in registration accuracy was found between CT and MRIRT versus the MRID measuring distances from the centre of structures (geometric mean error of 2.2 mm versus 6.6 mm). The mean GTVC (44.1 cm3) was significantly larger than GTVD (33.7 cm3, p value = 0.027) or GTVRT (30.5 cm3, p value = 0.014). When optimizing the VMAT plans for PTVC and investigating the mean dose to PTVRT neither the dose to 99% (58.8%) nor 95% of the PTV (84.7%) were found to meet the required clinical dose constraints of 90% and 95% respectively. Similarly, when optimizing for PTVD the mean dose to PTVRT did not meet clinical dose constraints for 99% (14.9%) nor 95% of the PTV (66.2%). Only by optimizing for PTVRT were all clinical dose constraints achieved.</p> <p>Conclusions: When oropharyngeal patients MRI scans are performed in the radiotherapy position there are significant improvements in CT-MR image registration, target definition and PTV dose coverage.</p&gt

Improving plan quality and consistency by standardization of dose constraints in prostate cancer patients treated with CyberKnife.

Treatment plans for prostate cancer patients undergoing stereotactic body radiation therapy (SBRT) are often challenging due to the proximity of organs at risk. Today, there are no objective criteria to determine whether an optimal treatment plan has been achieved, and physicians rely on their personal experience to evaluate the plan's quality. In this study, we propose a method for determining rectal and bladder dose constraints achievable for a given patient's anatomy. We expect that this method will improve the overall plan quality and consistency, and facilitate comparison of clinical outcomes across different institutions. The 3D proximity of the organs at risk to the target is quantified by means of the expansion-intersection volume (EIV), which is defined as the intersection volume between the target and the organ at risk expanded by 5 mm. We determine a relationship between EIV and relevant dosimetric parameters, such as the volume of bladder and rectum receiving 75% of the prescription dose (V75%). This relationship can be used to establish institution-specific criteria to guide the treatment planning and evaluation process. A database of 25 prostate patients treated with CyberKnife SBRT is used to validate this approach. There is a linear correlation between EIV and V75% of bladder and rectum, confirming that the dose delivered to rectum and bladder increases with increasing extension and proximity of these organs to the target. This information can be used during the planning stage to facilitate the plan optimization process, and to standardize plan quality and consistency. We have developed a method for determining customized dose constraints for prostate patients treated with robotic SBRT. Although the results are technology specific and based on the experience of a single institution, we expect that the application of this method by other institutions will result in improved standardization of clinical practice

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works