Demonstration of real-time positron emission tomography biology-guided radiotherapy delivery to targets.

BACKGROUND: Biology-guided radiotherapy (BgRT) is a novel technology that uses positron emission tomography (PET) data to direct radiotherapy delivery in real-time. BgRT enables the precise delivery of radiation doses based on the PET signals emanating from PET-avid tumors on the fly. In this way, BgRT uniquely utilizes radiotracer uptake as a biological beacon for controlling and adjusting dose delivery in real-time to account for target motion. PURPOSE: To demonstrate using real-time PET for BgRT delivery on the RefleXion X1 radiotherapy machine. The X1 radiotherapy machine is a rotating ring-gantry radiotherapy system that generates a nominal 6MV photon beam, PET, and computed tomography (CT) components. The system utilizes emitted photons from PET-avid targets to deliver effective radiation beamlets or pulses to the tumor in real-time. METHODS: This study demonstrated a real-time PET BgRT delivery experiment under three scenarios. These scenarios included BgRT delivering to (S1 ) a static target in a homogeneous and heterogeneous environment, (S2 ) a static target with a hot avoidance structure and partial PET-avid target, and (S3 ) a moving target. The first step was to create stereotactic body radiotherapy (SBRT) and BgRT plans (offline PET data supported) using RefleXion's custom-built treatment planning system (TPS). Additionally, to create a BgRT plan using PET-guided delivery, the targets were filled with 18F-Fluorodeoxyglucose (FDG), which represents a tumor/target, that is, PET-avid. The background materials were created in the insert with homogeneous water medium (for S1 ) and heterogeneous water with styrofoam mesh medium. A heterogeneous background medium simulated soft tissue surrounding the tumor. The treatment plan was then delivered to the experimental setups using a pre-commercial version of the X1 machine. As a final step, the dosimetric accuracy for S1 and S2 was assessed using the ArcCheck analysis tool-the gamma criteria of 3%/3 mm. For S3 , the delivery dose was quantified using EBT-XD radiochromic film. The accuracy criteria were based on coverage, where 100% of the clinical target volume (CTV) receives at least 97% of the prescription dose, and the maximum dose in the CTV was ≤130% of the maximum planned dose (97 % ≤ CTV ≤ 130%). RESULTS: For the S1, both SBRT and BgRT deliveries had gamma pass rates greater than 95% (SBRT range: 96.9%-100%, BgRT range: 95.2%-98.9%), while in S2 , the gamma pass rate was 98% for SBRT and between 95.2% and 98.9% for BgRT plan delivering. For S3 , both SBRT and BgRT motion deliveries met CTV dose coverage requirements, with BgRT plans delivering a very high dose to the target. The CTV dose ranges were (a) SBRT:100.4%-120.4%, and (b) BgRT: 121.3%-139.9%. CONCLUSIONS: This phantom-based study demonstrated that PET signals from PET-avid tumors can be utilized to direct real-time dose delivery to the tumor accurately, which is comparable to the dosimetric accuracy of SBRT. Furthermore, BgRT delivered a PET-signal controlled dose to the moving target, equivalent to the dose distribution to the static target. A future study will compare the performance of BgRT with conventional image-guided radiotherapy.

Responding to the rofecoxib withdrawal crisis: a new model for notifying patients at risk and their health care providers.

BACKGROUND: We decided to inform our patients of the withdrawal of rofecoxib, one of the largest drug withdrawals in United States history, and instruct them to contact their providers for guidance. OBJECTIVE: To identify and inform patients and providers affected by the rofecoxib withdrawal. DESIGN: Descriptive observational study. SETTING: Tertiary care center with an electronic medical record (EMR) system. PATIENTS: Patients with an active rofecoxib prescription within the EMR. INTERVENTION: Existing information technology and traditional communication resources were used to automate the identifying and notifying of patients and providers and to deactivate rofecoxib prescriptions in the EMR. MEASUREMENTS: Characteristics of patients receiving rofecoxib at our institution, details of their prescription and provider, number of EMR alerts, and medication discontinuations. RESULTS: The 11,699 patients with a rofecoxib prescription in our practice were sent notifications within 24 hours of the withdrawal. LIMITATIONS: We did not directly measure the effect of our notification on patients or providers. CONCLUSIONS: Information technology enabled our institution to rapidly identify and notify individual patients and their providers about an important drug withdrawal. The methods modeled a feasible way for health care organizations with EMRs to participate in notification processes that may be applicable in a variety of situations.

Managing technology in a complex healthcare delivery system.

Although clinical management is generally best handled regionally in a large system, e-health is the exception. E-health is managed centrally and not regionally because the patient access is not regional--it is virtual. Also, when patient demand, not business rationalization pressure, is the driver for change, it makes business sense to modify the management form from a regional to a centralized function.