Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status
Bibliographic record
Abstract
Cancer stands out as one of the fatal diseases people are facing all the time. Each year, a countless number of people die because of the late diagnosis of cancer or wrong treatments. Glioma, one of the most common primary brain tumors, has different aggressiveness and sub-regions, which can affect the risk of disease. Although prediction of overall survival based on multimodal magnetic resonance imaging (MRI) is challenging, in this study, we assess if and how location-based features of tumors can affect overall survival prediction. This approach is evaluated independently and in combination with radiomic features. The process is carried out on a data set entailing MRI images of patients with glioblastoma. To assess the impact of resection status, the data set is divided into two groups, patients were reported as gross total resection and unknown resection status. Then, different machine learning algorithms were used to evaluate how location features are linked with overall survival. Results from regression models indicate that location-based features have considerable effects on the patients' overall survival independently. Additionally, classifier models show an improvement in prediction accuracy by the addition of location-based features to radiomic features.
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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 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".