Citations

Abstract Several complications are associated with total shoulder arthroplasty (TSA), including glenoid loosening and joint instability. These complications are linked to imbalances in glenohumeral muscle forces, which cause eccentric loading that occurs on the outer edge of the glenoid component, rather than in the center of the glenoid, and can be damaging to the implant and destabilizing the joint. The purpose of this study was to develop musculoskeletal models of TSA patients using their skeletal geometry acquired from CT and their kinematics acquired with high-speed stereo radiography (HSSR) to predict glenohumeral joint loading and muscle forces for abduction and flexion and describe how these loads differ from their native contralateral side. CT data was collected for both shoulders of 6 TSA patients (three anatomic and three reverse). Models were developed using CT reconstructions of each patient’s scapula and humerus, and placed implant geometry with CT-derived alignment. Mesh morphing was used to create personalized muscle attachment sites on the scapula. Simulation of abduction and flexion to quantify muscle and joint forces was driven with limb-specific HSSR kinematics. At 90 deg abduction, reverse TSA increased superior shear force by 11.2% bodyweight (BW) and decreased medial compressive force by 27.5%BW compared to each patient’s contralateral shoulder. Lateral deltoid elevation moment arms and stability ratios increased 9.61 mm and 0.58, respectively. At 90 deg abduction, anatomic TSA patients increased humeral internal rotation by 52.7 deg and decreased rotator cuff force by 9.4%BW. Patient-specific musculoskeletal models quantified differences in glenohumeral joint mechanics. This study revealed muscle and kinematic mechanisms that could lead to edge loading and glenoid loosening in TSA and can inform personalized surgical planning for implant alignment with the goal of improving patient outcomes.

Abstract Task Group (TG) 314 of the American Association of Physicists in Medicine (AAPM) was charged to develop guidance for recovering from fault states in radiation therapy, specifically regarding the delivery of photon or electron beams using a linear accelerator (linac) including ancillary systems. The fault conditions addressed may involve software, hardware, or a combination of causes. The report provides detailed recommendations for the proactive steps to be taken before a fault, the actions to be taken at the time of a fault, and the safety steps before returning a linac to clinical service, as well as the activities that device manufacturers and standard organizations can do to prevent and resolve the faults. A user‐maintained log of prior faults; establishment of remote access by the vendor; and user training in emergency gantry, couch, and door motions are all useful proactive steps. At the moment of downtime and after ensuring the safety of the patient, the report stresses the importance of capturing fault information, prompt contact with the service engineer after the initial assessment, and considerations for communicating the estimated duration before the linac is returned to service. The medical physicist has a critical responsibility to assess the impact of the fault on patient care. Before resuming clinical use, the medical physicist must both determine the level of testing required to ensure safe operation of the linac and ensure any partially or totally delivered treatments have been correctly saved for accurate completion of the treatment fraction. The report stresses the roles of the radiation therapist, medical physicist, and service engineer to efficiently and safely address linac downtime. The appendices contain a description of the efforts of several organizations regarding linac safety: Integrating the Healthcare Enterprise—Radiation Oncology, International Standards Organization/International Electrotechnical Commission, Radiation Oncology Safety Stakeholder Initiative, and the AAPM Vendor Relations and Product Usability Subcommittee. Disclaimer: The recommendations of this TG should not be used to establish regulations. These recommendations are guidelines for Qualified Medical Physicists and others to use and appropriately interpret for their institution and clinical setting. Each institution may have site‐specific or state‐mandated needs and requirements which may modify their usage of these recommendations.

BackgroundWith radiotherapy having entered the era of image guidance, or image‐guided radiation therapy (IGRT), imaging procedures are routinely performed for patient positioning and target localization. The imaging dose delivered may result in excessive dose to sensitive organs and potentially increase the chance of secondary cancers and, therefore, needs to be managed.AimsThis task group was charged with: a) providing an overview on imaging dose, including megavoltage electronic portal imaging (MV EPI), kilovoltage digital radiography (kV DR), Tomotherapy MV‐CT, megavoltage cone‐beam CT (MV‐CBCT) and kilovoltage cone‐beam CT (kV‐CBCT), and b) providing general guidelines for commissioning dose calculation methods and managing imaging dose to patients.Materials & MethodsWe briefly review the dose to radiotherapy (RT) patients resulting from different image guidance procedures and list typical organ doses resulting from MV and kV image acquisition procedures.ResultsWe provide recommendations for managing the imaging dose, including different methods for its calculation, and techniques for reducing it. The recommended threshold beyond which imaging dose should be considered in the treatment planning process is 5% of the therapeutic target dose.DiscussionAlthough the imaging dose resulting from current kV acquisition procedures is generally below this threshold, the ALARA principle should always be applied in practice. Medical physicists should make radiation oncologists aware of the imaging doses delivered to patients under their care.ConclusionBalancing ALARA with the requirement for effective target localization requires that imaging dose be managed based on the consideration of weighing risks and benefits to the patient.

The authors compared the relative dosimetric merits of Gamma Knife (GK) and CyberKnife (CK) in 15 patients with 26 brain metastases. All patients were initially treated with the Leksell GK 4C. The same patients were used to generate comparative CK treatment plans. The tissue volume receiving more than 12 Gy (V12), the difference between V12 and tumor volume (V12net), homogeneity index (HI), and gradient indices (GI25, GI50) were calculated. Peripheral dose falloff and three conformity indices were compared. The median tumor volume was 2.50 cm3 (range, 0. 044‐19.9). A median dose of 18 Gy (range, 15‐22) was prescribed. In GK and CK plans, doses were prescribed to the 40‐50% and 77‐92% isodose lines, respectively. Comparing GK to CK, the respective parametric values were: minimum dose ( vs. , ); mean dose ( vs., ); maximum dose ( vs., ); and HI ( vs. , ). The median dosimetric indices (GK vs. CK, with range) were: RTOG_CI, 1.76 (1.12‐4.14) vs. 1.53 (1.16‐2.12), ; CI, 1.76 (1.15‐4.14) vs. 1.55 (1.18‐2.21), ; nCI, 1.76 (1.59‐4.14) vs. 1.57 (1.20‐2.30), ; GI50, 2.91 (2.48‐3.67) vs. 4.90 (3.42‐11.68), ; GI25, 6.58 (4.18‐10.20) vs. 14.85 (8.80‐48.37), . Average volume ratio (AVR) differences favored GK at multiple normalized isodose levels . We concluded that in patients with brain metastases, CK and GK resulted in dosimetrically comparable plans that were nearly equivalent in several metrics, including target coverage and minimum dose within the target. Compared to GK, CK produced more homogenous plans with significantly lower mean and maximum doses, and achieved more conformal plans by RTOG_CI criteria. By GI and AVR analyses, GK plans had sharper peripheral dose falloff in most cases.PACS number: 89.20.‐a

300 Background: The National Radiation Oncology Registry (NROR) is developing an IT infrastructure to support the efficient collection, aggregation, and analysis of data concerning cancer patients treated with radiotherapy across the United States. Methods: Detailed requirements were developed to emphasize the following distinguishing goals for the system: 1) streamline the process of data collection by minimizing manual data entry; 2) support the collection of patient-reported outcomes (PROs) and their linkage with clinician-observed outcomes; 3) facilitate data integration within the NROR and between the NROR and other registries; 4) allow for evolution of the system in response to new learning; and 5) promote sustainability and ongoing improvement through an open standard of interoperability. Results: An innovative technical architecture has been designed to meet the goals described. The system maximizes efficiency through automated extraction and electronic transfer of data from radiation oncology information systems. PROs are collected directly from patients via a web-based portal and linked to data submitted from participating institutions. A data dictionary has been developed based on national standards and controlled vocabularies to facilitate data integration. Contemporary forms-based database technology is used in combination with traditional relational technology to permit both flexible evolution of the system and efficient data analysis. A unique “gateway” architecture and open transmission standard makes it possible for commercial products to interoperate with the registry in the future. Conclusions: The NROR prototype system will be launched later this year to collect data for a pilot registry of prostate cancer patients. Its performance and usability will be important factors in the success of the pilot. If effective, the platform will support the expansion of the NROR to benefit patients, clinicians, researchers, payors, device manufacturers, and policy-makers by capturing reliable, population-based information on radiation treatment delivery and health outcomes.