PO-0774: Optimal beam quality for Linac-based Spatially Fractionated Grid Radiation Therapy (SFGRT)
Bibliographic record
Abstract
Purpose/Objective: Most of contemporary efforts to incorporate functional information into radiotherapy treatment planning are based on an attempt to replace CT/MRI-based GTV with PET based GTV.Functional information might also be used to define a sub-volume within CT-based GTV [Ling et al., Int J Radiat Oncol Biol Phys 2000;47:551-60], a method called dose painting by contours (DPBC).Instead, PET data was suggested to be used to gradually shape dose according to voxel intensities [Bentzen, Lancet Oncol 2005;6:112-7], a method called dose painting by numbers (DPBN).We discuss possibilities of these two alternatives in regards to differential uptake volume histogram method we developed to segment FDG-based biological sub-volumes on a cohort of 31 NSCLC patients that underwent PET/CT scan prior to surgery.Materials and Methods: Background uptake in PET scan was defined as weighted mean over FDG uptake values within contra-lateral healthy lung through slices containing the tumor, and then scaled by factor of 3 to account for the difference in tissue density between healthy lung and solid tumor.Each PET slice raw data was divided by the background uptake to obtain local signal-to-background ratio (S/B) images.By sampling a region of interest containing the tumor on S/B images, an uptake volume histogram was constructed and then decomposed for each patient (Fig. 1.a).Results: Distinct volumes were observed in uptake volume histograms and fitted using composition of six Gaussians.We hypothesize that they may represent different physiological regions within tumor (Fig. 1. b).Threshold values for these volumes are found by cross-section of corresponding fitted Gaussian curves (Fig.1.a).Immunohistopathological correlations to these sub-volumes are necessary to validate the method.The highest uptake sub-volume (named 'glycolytic BTV,' by hypothesis only) may consist of both well aerated regions (due to Warburg effect) as well as hypoxic regions (Fig. 1.c) and boost to higher doses might be considered clinically viable within DPBC method.On the other hand, use of DPBN and delivering low dose to the central section of the tumor may lead to undesired outcomes as the 'necrotic' tissue certainly lacks oxygen.By contrast, one may also consider delivering a high dose to 'necrotic' BTV to prevent possible recurrence by dormant cells under severe hypoxia.Conclusions: Multiple biological target volumes might be derived on a patient-to-patient basis.This concept is in synergy with a contemporary custom-made patient-specific oncologic treatment planning philosophy.However, at the microscopic level it is not possible to define a sharp cut-off separating one biological phenotype from another.One has to keep in mind that any biological target volume (Fig. 1. d) could be only defined as abundance rather than a distinct volume containing one and only one biological phenotype.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".