Stereotactic online adaptive radiotherapy (STAR) has been shown to improve the dosimetric therapeutic index (DTI) of SBRT for pancreatic cancer and other upper abdominal malignancies. The feasibility of this technique using a commercially available, online adaptive platform coupled with a ring-gantry Linac and artificial intelligence (AI)-assisted workflows, using cone-beam computed tomography (CBCT) image-guidance, has yet to be evaluated. We conducted a prospective in silico imaging clinical trial of CBCT-guided STAR (CT-STAR), with the hypothesis that CT-STAR would be feasible in silico and would improve the DTI for upper abdominal SBRT. Five patients with upper abdominal malignancies (3 pancreatic, 1 liver, 1 oligometastatic lymph node) who were otherwise undergoing definitive SBRT in 5 fractions (fx) were imaged using high-quality daily CBCT on the ring gantry Linac on each of their clinical treatment days. For all patients, initial plans (50Gy/5fx) were created using clinical planning CT images. CT-STAR was then simulated using the daily CBCT images, comprising all steps of the CT-STAR workflow: image approval, AI-driven autocontouring of influential organs-at-risk (OARs; liver, duodenum, stomach), physician editing of auto-contours, physician editing of the clinical target volume (CTV) and additional OARs (bowel), automated adaptive plan generation and dose volume histogram (DVH) comparison to the initial plan as projected on the daily anatomy, plan approval, pre-delivery quality assurance, and plan delivery. All plans used a strict isotoxicity approach, such that OAR constraints were met at each fx, with PTV coverage increased or decreased as permitted. Feasibility was defined as successful completion of all steps of the CT-STAR process in > 80% of simulated fx. A total of 23 CT-STAR fx were simulated. Median CTV and PTV at baseline were 86.9 cm3 (range, 5.0-115.8 cm3) and 170.9 cm3 (19.4-216.2 cm3). 61 AI OAR contours were autogenerated and reviewed with editing by the physician. AI driven auto-planning led to creation of acceptable plans (OAR constraints met, coverage acceptable) for all fx. In 100% of fx, the adaptive plan was selected over the initial plan, either because the baseline plan initially violated >/ = 1 OAR constraints (15/23fx, 65.2%) or because CTV/PTV coverage was improved by >5% (institutional clinical threshold for adaptation). After all plan re-optimization, the median per-fraction value for the mean PTV dose was 10.61 Gy (9.88-11.33 Gy) and the median max PTV dose was 13.68 Gy (12.51-14.98 Gy). The most common OAR constraint violations prior to adaptation were the duodenum (65.2% fx) and stomach (52.2% fx). 100% of violations were resolved with adaptation. All steps of CT-STAR were successfully completed in 23/23 fx. AI-assisted, CT-STAR is feasible in silico and improves the DTI of SBRT to upper abdominal cancers. Prospective clinical evaluation of this approach is indicated.

Adaptive radiotherapy (ART) is a specialized, multi-step process requiring substantial physician time and expertise to satisfactorily treat patients. Implementing specialized technologies is difficult due to the time commitment and unique physician expertise required for successful completion of treatment. As use of ART increases, the ability to provide tele-ART may ease workflow burdens and improve overall patient access to specialty care within an institutional, regional, or broader network. To address this need, we designed and piloted a novel tele-ART system in a multi-satellite academic network. We hypothesized that multiple potential failure modes would exist within the tele-ART system and conducted a risk analysis to identify potential failures prior to deploying tele-ART for routine clinical care. The institutional tele-ART system includes a commercial grade frame-grabber capturing up to 1900x1200 at 60fps, to connect the treatment delivery system (TDS) to a secondary computer. The treatment team including physics then video calls the covering radiation oncologist (RO) via a HIPAA compliant commercial collaboration platform (CP), which shares the captured frames from the secondary computer. Evaluation of image registration metrics between the TDS screen and RO's shared screen was performed via cross-correlation, as was lag time through the CP. The RO can control the view-only secondary screen and communicate with the team through the audio of the video call, thus providing remote image and ART plan review in real time. Failure modes of this process were evaluated by FMEA analysis. Specifically, a process map was created assuming the RO is absent from the machine but available within the department or in-network satellites. FMEA scoring utilizing a risk priority number (RPN) process was individually completed by 2 physicists, 2 therapists, and 3 ROs, all experienced in ART. The cross correlation between the TDS screen and the RO shared screen was 0.96. The lag in the CP video sharing was 0.05s. 129 failure modes (FM) were identified in our process map. Multiple repeat FM were identified at different steps of the process. The FM most often scored in the top 20% of RPN scores was RO distraction during multiple ART workflow steps due to the possibility of an RO being in the midst of a competing task. Other FMs with RPNs in the top 20% included misinterpretation of data or missed data due to system lag or changes in visual/audio quality. A novel method to perform view-only tele-ART has been created at our institution. FMEA analysis successfully identified several high-risk aspects of a tele-ART system. Quality measures are now underway to address these failure modes prior to routine clinical implementation of this service, which is a first step in providing highly specialized ART treatment to the broader radiation oncology community.