Cervix cancer regression measured using weekly MR imaging during fractionated radiotherapy: Radiobiologic modeling and correlation with tumor hypoxia
Bibliographic record
Abstract
5547 Background: To utilize cervix cancer volumetry, as measured with MR imaging during definitive chemoirradiation (RT-CT), to derive radiobiological parameters using a mathematical model of tumor regression, and compare them to pre-treatment measurements of tumor hypoxia. Methods: Twenty-eight patients receiving RT-CT for cervix cancer underwent weekly magnetic resonance imaging (MRI) scans. Tumor volume was assessed on each of these scans and the rate of regression plotted. A mathematical model of tumor regression was formulated to simulate the relationship between three independent radiobiological parameters, 1) surviving fraction of cells after 2 Gy, SF2, 2) the cell clearance constant Tc, and 3) the cellular proliferation constant Tp. Non-linear regression analysis was applied to fit the MR-derived tumor volumes to the mathematical model and to derive SF2 and Tc values for each patient. These were compared to pre-treatment hypoxia measurements. Results: Initial tumor volume ranged between 8 and 209 cm3. Relative reduction in volume during treatment was 0.02 to 0.79. Simulations using representative values of the independent biologic variables derived from the literature showed SF2 and Tc to strongly influence the shape of the volume response curves. Non-linear regression analysis yielded a median SF2 of 0.71 and median Tc of 10 days. Radioresistant tumors (SF2 >0.71) were significantly more hypoxic at diagnosis (p=0.02). Conclusion: Based on serial MR imaging during treatment, a marked variation in cervix tumor regression is seen from patient to patient. Through our radiobiological model, tumors can be classified as radioresistant or radiosensitive, which correlates with hypoxia [Table: see text] No significant financial relationships to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".