TU‐G‐211‐01: Uptake Volume Histograms: A Novel Avenue towards Delineation of Biological Target Volumes (BTV) in Radiotherapy
Bibliographic record
Abstract
Purpose: To investigate the feasibility of decomposition of differential uptake volume histograms (UVH) derived from FDG‐PET and CT data for uncovering tumor sub‐volumes as a novel approach for defining biological target volumes (BTV) for use in radiotherapy treatment planning. Methods: For a cohort of 27 histopathologically proven non‐small cell lung carcinoma (NSCLC) patients, background uptake values were sampled within contra‐lateral healthy lung over PET slices containing tumor and then scaled by the ratio of tissue densities between healthy lung and tumor derived from CT data. Signal‐to‐background uptake values within volumes of interest encompassing the tumor were scored from which differential uptake volume histograms were constructed. These were subsequently decomposed into the minimum number of analytical functions that yielded acceptable net fits, as assessed by chî2 values. Results: Based on the assumption that each function used to decompose the UVH may correspond to a single sub‐volume comprising the volume of interest sampled, at least four sub‐volumes consistently evolved for our patient population. Furthermore, if crossing points between adjacent functions are interpreted as threshold values that differentiate sub‐volumes, average threshold values between the four sub‐volumes were found to be 0.80±0.21, 1.56±0.48, and 2.96±1.04 for adenocarcinomas, 0.89±0.48, 1.60±0.40, and 2.85±0.75 for large cell carcinomas, and 0.84±0.31, 1.70±0.58, and 3.72±1.68 for squamous cell carcinomas. Conclusions: Our study suggests that FDG‐based PET data could be used to identify biological sub‐volumes within tumor in NSCLC patients. Significant fluctuations in threshold values throughout the patient cohort can be explained as a consequence of large variability in physiological status of the tumor volume for each patient at the time of the PET/CT scan. This further suggests that BTV threshold values may be rather patient‐specific, and could be determined by creation and curve fitting of differential uptake volume histograms on a patient‐specific basis.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".