Abstract PO-060: Individualized prediction of meningioma recurrence risk over prolonged time periods
Bibliographic record
Abstract
Abstract Rationale and purpose: Meningiomas are benign tumors, but significant number of meningiomas display risk of early tumor recurrence. The inter-observer variability among pathologists for grading and some indistinguishable features of meningioma under the microscope prevent accurate prediction of recurrence risk that critically limits appropriate treatment and management of patients who may benefit from adjuvant radiation therapy. We aim to develop individualized prediction of meningioma recurrence risk over prolonged time periods using DNA methylation signatures and other associated clinical factors. Method: The recurrence risk predictor is based on continuous survival random-forest modelling to calculate the probabilities of a recurrence for each meningioma patient over various prolonged time frames. We processed over 500 meningioma samples using our machine-learning model that predicts probability of tumor recurrence based on methylation data. We used Random Forest based Rangers package as the survival model that returns the probabilities of a recurrence not happening over various prolonged time frames. Result: Methylation-based predictor distinguishes high-low risk groups over prolonged time periods. The predictor was validated using external validation cohort based on the selected probes and original training model. We found that methylation based predictor has potential to identify in patients with grade II meningiomas especially, while it also can identify grade I patients with higher recurrence risk over prolonged time periods. Conclusion: There were limitations of standard of care classifications in meningioma especially for Grade 2 patients due to significant intra- and inter-observer variability for grading and selection of patients for treatment. The individualized risk predictor can help determining the decisions regarding adjuvant radiation treatment versus observation along for each meningioma patient in the clinic. Citation Format: Yasin Mamatjan, Farshad Nassiri, Mira Salih, Kenneth Aldape, Gelareh Zadeh. Individualized prediction of meningioma recurrence risk over prolonged time periods [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-060.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".