PurposeTo prepare for big data analyses on radiation therapy data, we developed Stature, a tool-supported approach for standardization of structure names in existing radiation therapy plans. We applied the widely endorsed nomenclature standard TG-263 as the mapping target and quantified the structure name inconsistency in 2 real-world data sets.Methods and MaterialsThe clinically relevant structures in the radiation therapy plans were identified by reference to randomized controlled trials. The Stature approach was used by clinicians to identify the synonyms for each relevant structure, which was then mapped to the corresponding TG-263 name. We applied Stature to standardize the structure names for 654 patients with prostate cancer (PCa) and 224 patients with head and neck squamous cell carcinoma (HNSCC) who received curative radiation therapy at our institution between 2007 and 2017. The accuracy of the Stature process was manually validated in a random sample from each cohort. For the HNSCC cohort we measured the resource requirements for Stature, and for the PCa cohort we demonstrated its impact on an example clinical analytics scenario.ResultsAll but 1 synonym group (“Hydrogel”) was mapped to the corresponding TG-263 name, resulting in a TG-263 relabel rate of 99% (8837 of 8925 structures). For the PCa cohort, Stature matched a total of 5969 structures. Of these, 5682 structures were exact matches (ie, following local naming convention), 284 were matched via a synonym, and 3 required manual matching. This original radiation therapy structure names therefore had a naming inconsistency rate of 4.81%. For the HNSCC cohort, Stature mapped a total of 2956 structures (2638 exact, 304 synonym, 14 manual; 10.76% inconsistency rate) and required 7.5 clinician hours. The clinician hours required were one-fifth of those that would be required for manual relabeling. The accuracy of Stature was 99.97% (PCa) and 99.61% (HNSCC).ConclusionsThe Stature approach was highly accurate and had significant resource efficiencies compared with manual curation.

Purpose Our purpose was to report outcomes of a novel palliative radiation therapy protocol that omits computed tomography simulation and prospectively collects electronic patient-reported outcomes (ePROs). Methods and Materials Patients receiving extracranial, nonstereotactic, linear accelerator-based palliative radiation therapy who met inclusion criteria (no mask-based immobilization and a diagnostic computed tomography within 4 weeks) were eligible. Global pain was scored with the 11-point numerical pain rating scale (NPRS). Patients were coded as having osseous or soft tissue metastases and no/mild versus severe baseline pain (NPRS ≥ 5). Pain response at 4 weeks was measured according to the international consensus (no analgesia adjustment). Transition to ePRO questionnaires was completed in 3 phases. Initially, pain assessments were collected on paper for 11 months, then pilot ePROs for 1 month and then, after adjustments, revised ePROs from 1 year onwards. ePRO feasibility criteria were established with reference to the paper-based process and published evidence. Results Between May 2018 and November 2019, 542 consecutive patients were screened, of whom 163 were eligible (30%), and 160 patients were successfully treated. The proportion of patients eligible for the study improved from approximately 20% to 50% by study end. Routine care pain monitoring via ePROs was feasible. One hundred twenty-seven patients had a baseline NPRS recording. Ninety-five patients had osseous (61% severe pain) and 32 had soft tissue (25% severe pain) metastases. Eighty-four patients (66%) were assessable for pain response at 4 weeks. In the 41 patients with severe osseous pain, overall and complete pain response was 78% and 22%, respectively. Conclusions By study completion, 50% of patients receiving palliative extracranial radiation therapy avoided simulation, streamlining the treatment process and maximizing patient convenience. Pain response for patients with severe pain from osseous lesions was equivalent to published evidence.

Introduction: RapidPlan (RP), a knowledge-based planning system, aims to consistently improve plan quality and efficiency in radiotherapy. During the early stages of implementation, some of the challenges include knowing how to optimally train a model and how to integrate RP into a department. We discuss our experience with the implementation of RP into our institution. Methods: We reviewed all patients planned using RP over a 7-month period following inception in our department. Our primary outcome was clinically acceptable plans (used for treatment) with secondary outcomes including model performance and a comparison of efficiency and plan quality between RP and manual planning (MP). Results: Between November 2017 and May 2018, 496 patients were simulated, of which 217 (43.8%) had an available model. RP successfully created a clinically acceptable plan in 87.2% of eligible patients. The individual success of the 24 models ranged from 50% to 100%, with more than 90% success in 15 (62.5%) of the models. In 40% of plans, success was achieved on the 1st optimisation. The overall planning time with RP was reduced by up to 95% compared with MP times. The quality of the RP plans was at least equivalent to historical MP plans in terms of target coverage and organ at risk constraints. Conclusion: While initially time-consuming and resource-intensive to implement, plans optimised with RP demonstrate clinically acceptable plan quality, while significantly improving the efficiency of a department, suggesting RP and its application is a highly effective tool in clinical practice.

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