DART: diagnostic-CT-enabled planning: a randomized trial in palliative radiation therapy (study protocol)
Bibliographic record
Abstract
BACKGROUND: Palliative radiotherapy (PRT) is an effective treatment for managing symptoms of advanced cancer. At least half of all radiation treatments are delivered with palliative intent, aimed at relieving symptoms, such as pain or shortness of breath. Symptomatic patients must receive PRT quickly, therefore expeditious treatment planning is essential. Standard radiation planning requires a dedicated CT scan acquired at the cancer centre, called a 'CT simulation', which facilitates treatment planning (i.e. tumor delineation, placement of radiation beams and dose calculation). However, the CT simulation process creates a bottleneck and often leads to delays in starting treatment. Other researchers have indicated that CT simulation can be replaced by the use of standard diagnostic CT scans for target delineation and planning, which are normally acquired through the radiology department as part of standard patient workup. The goals of this feasibility study are to assess the efficacy, acceptability and scalability of diagnostic-CT-enabled planning, compared to conventional CT simulation planning, for patients receiving PRT to bone, soft tissue and lung disease. METHODS: This is a randomized, phase II study, with 33 PRT patients to be randomized in a 1:2 ratio between conventional CT simulation (Arm 1), and the diagnostic CT enabled planning workflow (Arm 2). Patients will be stratified by treatment target volume (bone and soft tissue metastasis vs. primary or metastatic intrathoracic disease targets). The primary endpoint is the amount of time the patient spends at the cancer centre. Secondary endpoints include efficacy (rate of plan deliverability and rate of plan acceptability on blinded dose distribution review), stakeholder acceptability (based on patient and clinician perception of acceptability questionnaires) and scalability. DISCUSSION: This study will investigate the efficacy, acceptability and scalability of a "sim-free" PRT pathway compared to conventional CT simulation. The workflow may provide opportunity for resource optimization by using pre-existing diagnostic imaging and requires minimal investment due to its similarity to current PRT models. It also offers potential benefit to patients by eliminating an imaging procedure, reducing the amount of time spent at the cancer centre, and expediting time to treatment. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT05233904. Date of registration: February 10, 2022; current version: 1.4 on April 29, 2022.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".