Detecting tumor progression in glioma: current standards and new techniques
Bibliographic record
Abstract
INTRODUCTION: The post-treatment monitoring of glioma patients remains an area of active research and development. Conventional imaging with MRI is a highly sensitive modality for detecting and monitoring primary and secondary brain tumors and includes multi-parametric sequences to better characterize the disease. Standardized schemes for measuring response to treatment are in wide clinical use; however, the introduction of new therapeutics have introduced new patterns of response that can confound interpretation of conventional MRI and can cause uncertainty in the proper management following therapy. Areas covered: A summary of current and evolving techniques for assessing glioma response in this era of new therapies that address these challenges are presented in this review. While this review focuses more on clinical and early clinical methodologies for MRI and nuclear medicine techniques some promising pre-clinical techniques are also presented. Expert commentary: While successful single institution results have been widely reported in the literature, any new methodologies must be undertaken in multi-center settings. Additionally, the need for standardization of protocols in quantitative measured are an important area that must be addressed for new and promising techniques to be implemented to a wide array of patients.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".