Evaluation of pseudoprogression in patients with glioblastoma
Bibliographic record
Abstract
BACKGROUND: Management of glioblastoma is complicated by pseudoprogression, a radiological phenomenon mimicking progression. This retrospective cohort study investigated the incidence, prognostic implications, and most clinically appropriate definition of pseudoprogression. METHODS: Consecutive glioblastoma patients treated at the Juravinski Hospital and Cancer Centre, Hamilton, Ontario between 2004 and 2012 with temozolomide chemoradiotherapy and with contrast-enhanced MRI at standard imaging intervals were included. At each imaging interval, patient responses as per the RECIST (Response Evaluation Criteria in Solid Tumors), MacDonald, and RANO (Response Assessment in Neuro-Oncology) criteria were reported. Based on each set of criteria, subjects were classified as having disease response, stable disease, pseudoprogression, or true progression. The primary outcome was overall survival. RESULTS: The incidence of pseudoprogression among 130 glioblastoma patients treated with chemoradiotherapy was 15%, 19%, and 23% as defined by RANO, MacDonald, and RECIST criteria, respectively. Using the RANO definition, median survival for patients with pseudoprogression was 13.0 months compared with 12.5 months for patients with stable disease (hazard ratio [HR]=0.70; 95% confidence interval [CI], 0.35-1.42). Similarly, using the MacDonald definition, median survival for the pseudoprogression group was 11.8 months compared with 12.0 months for the stable disease group (HR=0.86; 95% CI, 0.47-1.58). Furthermore, disease response compared with stable disease was also similar using the RANO (HR=0.52; 95% CI, 0.20-1.35) and MacDonald (HR=0.51: 95% CI, 0.20-1.31) definitions. CONCLUSIONS: Of all conventional glioblastoma response criteria, the RANO criteria gave the lowest incidence of pseudoprogression. Regardless of criteria, patients with pseudoprogression did not have statistically significant difference in survival compared with patients with stable disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".