3T MR-based treatment planning for radiotherapy of brain lesions
Bibliographic record
Abstract
Purpose. The aim of this work is to develop a complete treatment planning procedure for radiation therapy of intracranial lesions based solely on 3T magnetic resonance imaging (MRI), i.e. MRI simulation. Methods. The proposed 3T MR-based radiotherapy treatment planning procedure consists of converting the MR images into CT-like images by assigning electron density information (related to CT values) to organ structures. Firstly, the 3D distortion field present in the MR volumes is determined and rectified by using an in-house developed distortion correction method. The MR volumes are segmented into anatomical structures, i.e. brain, bone and scalp, by using a combination of the »Profile« and »Autocontouring« tools available on Pinnacle (Philips Medical Systems) treatment planning system (TPS). Bulk electron density values are assigned to the 3D volumes in Pinnacle by overriding their default MR values. Once the MR images contain the target volume along with the electron density information, they are ready to be used for dose calculations. The resulting CT+MR and MR only based plans were compared in terms of isodose distributions and dose-volume histograms (DVHs). For plan ranking we use a tumor-control probability (TCP)-based procedure for heterogeneous irradiation, which does not require the knowledge of radiobiological parameters. Results. For all patients investigated, the 3T MR only and CT+MR-based plans are in good agreement in terms of isodose distributions, DVHs and TCPs (within 1%) following our clinical criteria. Conclusions. The proposed 3T MR only based treatment planning procedure performs as good as the standard clinical procedure that relies on both CT and MR studies. MRI simulation can significantly reduce the patient treatment cost and save staff and machine time, and avoid any errors that may be associated with the image fusion process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.002 |
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".