Sci‐AM2 Sat ‐ 08: Impact of volume definition on prescribed dose in a liver cancer dose escalation study
Bibliographic record
Abstract
The volume of irradiated liver is strongly related to target volume size in extracranial stereotactic radiation therapy (ESRT) of liver tumours. Our ESRT dose escalation study treats liver cancer with prescription doses that are individualized to maintain a constant risk of radiation induced liver disease (RILD) for all patients. The impact of liver volume on normal tissue complication probability (NTCP) calculation was assessed using the whole liver, liver minus gross tumour volume (GTV), liver minus clinical target volume (CTV), and liver minus planning target volume (PTV). Assuming that liver minus GTV is the most appropriate volume to base NTCP on (since it includes all potentially functional liver), each volume was used to calculate the dose for 5% NTCP. NTCP was then recalculated using the liver minus GTV, but with doses determined from the other liver volumes. The relationship between target volume size and dose is also investigated. NTCP calculated with liver minus CTV or liver minus PTV results in extremely high risks of RILD, while using whole liver underestimates the risk and is safer. For all target volumes, the prescription dose can be increased as the target size decreases. Predicted dose for individualized dose escalation for liver cancer is strongly dependent on the liver volume analyzed. We suggest that liver minus CTV and liver minus PTV volumes cannot safely be used to individualize prescription doses for dose escalation for liver cancer. Based on these substantial changes in NTCP, uniform reporting of volumes and NTCP is desirable.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".