Comparison of Three Commercial Methods of Cone-Beam Computed Tomography-Based Dosimetric Analysis of Head-and-Neck Patients with Weight Loss
Bibliographic record
Abstract
Purpose: This investigation compares three commercial methods of cone-beam computed tomography (CBCT)-based dosimetric analysis to a method based on repeat computed tomography (CT). Materials and Methods: Seventeen head-and-neck patients treated in 2020, and with a repeat CT, were included in the analyses. The planning CT was deformed to anatomy in repeat CT to generate a reference plan. Two of the CBCT-based methods generated test plans by deforming the planning CT to CBCT of fraction N using VelocityAI™ and SmartAdapt ® . The third method compared directly calculated doses on the CBCT for fraction 1 and fraction N, using PerFraction™. Maximum dose to spinal cord (Cord_dmax) and dose to 95% volume (D95) of planning target volumes (PTVs) were used to assess “need to replan” criteria. Results: The VelocityAI™ method provided results that most accurately matched the reference plan in “need to replan” criteria using either Cord_dmax or PTV D95. SmartAdapt ® method overestimated the change in Cord_dmax (6.77% vs. 3.85%, P < 0.01) and change in cord volume (9.56% vs. 0.67%, P < 0.01) resulting in increased false positives in “need to replan” criteria, and performed similarly to VelocityAI™ for D95, but yielded more false negatives. PerFraction™ method underestimated Cord_dmax, did not perform any volume deformation, and missed all “need to replan” cases based on cord dose. It also yielded high false negatives using the D95 PTV criteria. Conclusions: The VelocityAI™-based method using fraction N CBCT is most similar to the reference plan using repeat CT; the other two methods had significant differences.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".