SU‐E‐T‐427: Cell Surviving Fractions Derived From Tumor‐Volume Variation During Radiotherapy for Non‐Small Cell Lung Cancer: Comparison with Predictive Assays
Bibliographic record
Abstract
Purpose: To show that a distribution of cell surviving fractions S2 in a heterogeneous group of patients can be derived from tumor‐volume variation curves during radiotherapy for non‐small cell lung cancer. Methods: Our analysis was based on two data sets of tumor‐volume variation curves for heterogeneous groups of 17 patients treated for nonsmall cell lung cancer with conventional dose fractionation. The data sets were obtained previously at two independent institutions by using megavoltage (MV) computed tomography (CT). Statistical distributions of cell surviving fractions S2 and cell clearance half‐lives of lethally damaged cells T1/2 have been reconstructed in each patient group by using a version of the two‐level cell population tumor response model and a simulated annealing algorithm. The reconstructed statistical distributions of the cell surviving fractions have been compared to the distributions measured using predictive assays in vitro. Results: Non‐small cell lung cancer presents certain difficulties for modeling surviving fractions using tumor‐volume variation curves because of relatively large fractional hypoxic volume, low gradient of tumor‐volume response, and possible uncertainties due to breathing motion. Despite these difficulties, cell surviving fractions S2 for non‐small cell lung cancer derived from tumor‐volume variation measured at different institutions have similar probability density functions (PDFs) with mean values of 0.30 and 0.43 and standard deviations of 0.13 and 0.18, respectively. The PDFs for cell surviving fractions S2 reconstructed from tumor volume variation agree with the PDF measured in vitro. Comparison of the reconstructed cell surviving fractions with patient survival data shows that the patient survival time decreases as the cell surviving fraction increases. Conclusion: The data obtained in this work suggests that the cell surviving fractions S2 can be reconstructed from the tumor volume variation curves measured during radiotherapy with conventional fractionation. The proposed method can be used for treatment evaluation and adaptation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".