14 MACHINE-LEARNING MODELS FOR PREDICTION OF RADIONECROSIS IN PATIENTS UNDERGOING INTRACRANIAL STEREOTACTIC RADIOSURGERY FOR BRAIN METASTASIS
Bibliographic record
Abstract
Abstract PURPOSE To utilize machine learning and radiomic analysis to construct a model that can be used as a predictive tool for radiation necrosis (RN) after stereotatic radiotherapy (SRT) for brain metastasis (BM). MATERIALS AND METHODS Patients who had SRT in Vancouver, BC, between the years 2019-2023 were included. There are 31 lesions in the RN cohort, as defined as having radiologic evidence of tumor growth post SRT and spontaneous regression with or without steroids. In the control arm, there are 30 lesions which did not develop RN. Patients with preceding surgical resection or those who passed away within 6 months of receiving SRS were excluded. For each lesion, dosimetric and geometric parameters such as size of the gross tumor volume (GTV), planning target volume (PTV), conformity index (CI), gradient index (GI), and homogeneity index (HI) were extracted. We then constructed a random forest model with 50 estimators, a maximum depth of 5 as a regularization parameter, and an 80-20 train-test split to predict whether the patient develops RN in the region of the lesion. In the future, we intend to use deep-learning model with MRI imaging and dose distribution to expand our model. RESULTS The RN group had an average GTV that was statistically larger than the control group (3.65cc, 1.12cc, p=0.03), as well as a larger PTV (4.9cc, 1.7cc, p=0.02). The RN and control group received an average Dmax of 35Gy (p=0.26). The biologically equivalent dose (EQD2 ), with an alpha/beta ratio of 2, given to the RN and control group were 92Gy and 90Gy respectively (p=0.38).However, the volume of normal brain receiving >12 Gy (V12) in the RN group was significantly larger compared to the control group (27.8cc, 9.8cc, p=0.04). There was no statistical difference between the RN and control group with respect to quality metrics including CI (0.9, 1.0, p=0.27), GI (10.7, 7.4, p=0.59), and HI (1.3, 1.3, p=0.31). The random forest model achieved an accuracy of 83% in predicting RN outcomes in the test dataset. The top five most important features in descending order were the EQD2/2, EQD2/12, gradient index, volume enclosed in the 50% isodose surface, and maximum dose in tumor as a percentage of the prescription dose, with feature importance scores of 0.115, 0.097, 0.091, 0.088 and 0.072 respectively. CONCLUSIONS While this pilot project is still in its early stages, the preliminary findings reveal a model that may accurately predict RN in patients who receive SRS.
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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 | 0.004 |
| 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 | 0.001 | 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".