PC3 - 179 Analysis of Glioblastoma Physical Characteristics in Patients Benefiting from Tumor Treating Electric Fields Therapy
Bibliographic record
Abstract
Tumor treating electric fields (TTFields) are an established treatment for glioblastoma patients. But the anatomical and physical characteristics of the brain and tumor contributing to treatment efficacy are unknown. We contoured gross tumor volume (GTV) using ScanIP and measured tumor size according to the RANO criteria on 5 patients with recurrent glioblastomas who benefited from TTFields (cohort 1) and 5 who did not (cohort 2). Tumor surface area and geometric centroid distance (GCD) from the bilateral ventricles were computed. Wilcoxon rank sum test was used to compare these physical parameters between the two cohorts. The results showed that the respective median GTV was 11.6cm3 and 38.1cm3 (P=0.0591), while the respective median GCD was 5.0cm and 5.3cm (P=0.6761), in cohort 1 and 2. The tumor size had a median of 8.2cm2 in cohort 1, as compared to a median of 53.9cm2 in cohort 2 (P=0.0591). The surface area had a median of 56.2cm2 in cohort 1, as compared to a median of 214.0cm2 in cohort 2 (P=0.4034). After removing an outlier from cohort 1 and another from cohort 2, the respective median GTV was 9.7cm3 and 41.3cm3 (P=0.1003), and the respective median surface area was 46.5cm2 and 236.0cm2 (P=0.0304). The respective median surface area/GCD was 13.3cm and 38.9cm (P=0.0304) and the respective median GTV/GCD was 2.7cm2 and 8.5cm2 (P=0.0304). The data suggest that the ratio of surface area/GCD and GTV/GCD, which are proportional to the tissue capacitance, may be an important parameter for optimizing TTFields efficacy in the treatment of glioblastoma.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".