Tumor control probability (TCP) in prostate cancer: Role of radiobiological parameters and radiation dose escalation
Bibliographic record
Abstract
The objective of this work was to assess the relative impact of radiobiological parameters and radiation dose escalation on Tumor Control Probability for prostate cancer patients treated with radiation. Radiobiological parameters included alpha/beta ratios, cell surviving fraction at 2 Gy (SF(2) and clonogenic cell density (CCD). Using the Niemierko method, TCP was calculated in ten prostate cancer patients as a function of increasing radiation doses (70-140 Gy), alpha/beta ratios (1.5-20), SF(2) (0.3-0.7) and CCD (10-20 million cells/cm(3). At 70 Gy and CCD of 10 million/cm(3), TCP was above 99% for SF(2) of 0.3 or 0.4, 97.4%-98.6% for SF(2) of 0.5 and less than 2% for SF(2) of 0.6 or 0.7. With dose escalation, TCP values above 99% were demonstrated at 80 Gy for SF(2) of 0.5 and 100 Gy for SF(2) of 0.6. For SF(2) of 0.7, TCP above 99% was demonstrated with 100 Gy and CCD of 10(4)cells/cm(3) or 140 Gy and CCD of 10(7) cells/cm(3). TCP decreased with lower alpha/beta of 1.5, but at a much smaller scale compared to SF(2) changes. TCP modeling predicts that SF(2) and CCD are dominant predictors of radioresistance in prostate cancer. Radiation doses of 100 Gy or greater may be required for tumors with SF(2) of 0.6 or above. Relating clinical tumor prognostic indicators such as Gleason score and PSA to radiobiological parameters will allow us to identify subsets of patients in need of higher radiation doses and adjuvant therapy to maximize treatment outcomes.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".