A Clinicobiological Model Predicting Survival in Medulloblastoma
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the relative contributions of biological and clinical predictors of survival in patients with medulloblastoma (MB). EXPERIMENTAL DESIGN: Clinical presentation and survival information were obtained for 119 patients who had undergone surgery for MB at the Hospital for Sick Children (Toronto, Ontario, Canada) between 1985 and 2001. A tissue microarray was constructed from the tumor samples. The arrays were assayed for immunohistochemical expression of MYC, p53, platelet-derived growth factor receptor-alpha, ErbB2, MIB-1, and TrkC and for apoptosis (terminal deoxynucleotidyl transferase-mediated nick end labeling). Both univariable and multivariable analyses were conducted to characterize the association between survival and both clinical and biological markers. For the strongest predictors of survival, a weighted predictive score was calculated based on their hazard ratios (HRs). The sum of these scores was then used to give an overall prediction of survival using a nomogram. RESULTS: The four strongest predictors of survival in the final multivariable model were the presence of metastatic disease at presentation (HR, 2.02; P=0.01) and p53 (HR, 2.29; P=0.02), TrkC (HR, 0.65; P=0.14), and ErbB2 (HR, 1.51; P=0.21) immunopositivity. A linear prognostic index was derived, with coefficients equal to the logarithm of these HRs. The 5-year survival rate for patients at the 10th, 50th, and 90th percentiles of the score distribution was 80.0%, 71.0%, and 35.7%, respectively, with radiation therapy and 70.5%, 58.5%, and 20.0%, respectively, without radiation therapy. CONCLUSIONS: In this study, we demonstrate an approach to combining both clinical and biological markers to quantify risk in MB patients. This provides further prognostic information than can be obtained when either clinical factors or biological markers are studied separately and establishes a framework for comparing prognostic markers in future clinical studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".