TU-A-BRC-07: Toward Predicting the Location of GBM Recurrence after Radiotherapy Using Patient-Specic DTI and Numerical Modelling
Bibliographic record
Abstract
Purpose: Patients diagnosed with glioblastoma multiforme (GBM), the most aggressive form of brain tumour, have a median survival of 15 months when receiving standard treatment. We have developed a three-dimensional reaction-diffusion model which uses patient-specific diffusion tensor imaging (DTI) and real dose distributions to simulate the growth and radiotherapy treatment of a GBM. We highlight paths for tumour recurrence and predict a gain in survival when treatment margins are adjusted according to model inputs. Methods: A DTI sequence is added to pre- and post-treatment MRI for ten GBM patients receiving standard treatment in our department. Our numerical model uses clinically available images to initialize the tumour cell density and to measure invasion velocity. Patient- specific DTI is used to model tumour cell motility and to prioritize migration along white matter fibres. The treatment dose distribution is used to simulate the radiotherapy treatment actually received by the patient. Finally, we simulate an alternative treatment plan that increases the dose in the region where the model predicts the formation of a recurrent tumour. Results: The general behaviour of the model was evaluated and found to be adequate for a patient with no DTI but whose post-treatment images were available. For another patient, model initialization with pre- treatment DTI shows that a second tumour focus forms outside the original radiation field in a region where the tumour migration is high due to white matter fibres. The application of an alternative virtual plan integrating the location of the recurrence suggests a 2-month gain of survival time, based on tumour cell density. Conclusions: Our results indicate that a reaction- diffusion model using patient-specific DTI information could potentially be used to modify GBM treatment margins, leading to an increased survival time. Integrating a long-term outcome study will allow us to verify the predictive efficiency of the model. This work is funded by the Fonds quebecois de la recherche sur la nature et les technologies (FQRNT) and by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".