Nomogram to Predict Subsequent Brain Metastasis in Patients With Metastatic Breast Cancer
Bibliographic record
Abstract
PURPOSE Brain metastasis is usually a fatal event in patients with stage IV breast cancer. We hypothesized that its occurrence can be predicted if a clinical nomogram can be developed, thus allowing for selection of enriched patient populations for prevention trials. PATIENTS AND METHODS Electronic medical records of patients with metastatic breast cancer were retrospectively reviewed for the period between January 2000 and February 2007 under a study approved by the institutional review board. A multivariate logistic regression analysis of selected prognostic features was done. A nomogram to predict brain metastasis was constructed and validated in a cohort of 128 patients with brain metastasis treated at the Cross Cancer Institute (Edmonton, Alberta, Canada). Results Of 2,136 patients with breast cancer, 362 developed subsequent brain metastasis. Age, grade, negative status of estrogen receptor and human epidermal growth factor receptor 2, number of metastatic sites (one v > one), and short disease-free survival were significantly and independently associated with subsequent brain metastasis. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.68 (95% CI, 0.66 to 0.69) in the training set. The validation set showed a good discrimination with an AUC of 0.74 (95% CI, 0.70 to 0.79). The nomogram was well calibrated, with no significant difference between the predicted and the observed probabilities. CONCLUSION We have developed a robust tool that is able to predict subsequent brain metastasis in patients with breast cancer with nonbrain metastatic disease. Selection of an enriched patient population at high risk for brain metastasis will facilitate the design of trials aiming at its prevention.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".