Optimierung und Evaluierung unterschiedlicher Bestrahlungsmodalitäten mit Photonen und Protonen in der Strahlentherapie : Vergleich der Modelle zur Berechnung der Normalgewebekomplikationsrate
Bibliographic record
Abstract
Optimization and evaluation of different radiation therapy modalities using photons and protons Comparison of Normal Tissue Complication Probability modelsThe recent highly conformal radiation treatment modalities allow in most instances optimal dose coverage to the tumor and a reduction of applied dose to normal tissues.Charged particles, such as protons, particularly enable better sparing of the OAR (Organs At Risk) adjacent to the target volume.In the context of this work different treatment modalities for lung, prostate and mamma carcinoma are evaluated and optimized by means of dosimetric parameters and NTCP (Normal Tissue Complication Probability) and TCP calculations (Tumor Control Probability).This includes 3D-Conformal Radiation Therapy (3D-CRT), Intensity Modulated Radiation Therapy (IMRT), passive scattered proton therapy and Intensity Modulated Proton Therapy (IMPT, scanned protons).In case of mamma carcinoma external radiation therapy is opposed to pulsed dose rate brachytherapy.The NTCP calculation is based on different published models and parameter sets.NTCP values calculated with different models and parameter sets can vary up to 39% in single cases for the same treatment plan.Averaged plan ranking shows that scanned protons are superior to other modalities in terms of sparing normal tissue.This doesn't need to hold for particular cases.In general the superior treatment modality can vary from patient to patient, particularly when IMRT, IMPT and passive scattered protons are compared.The results show that for single cases the IMRT competes with passive scattered protons.On average scanned protons result in the best normal tissue sparing with the highest PTV dose coverage.NTCP calculation and the subsequent plan ranking must be considered critically, because different models, parameter sets and reduction schemes result in different NTCP values for the same patient's treatment plan. Publikationen im Rahmen der
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".