ACRIN 6695 perfusion CT as prognostic imaging biomarker in ovarian cancer.
Bibliographic record
Abstract
TPS5114 Background: Tumor size and the cell surface glycoprotein CA125 levels have been traditional biomarkers for ovarian carcinoma, but remain suboptimal for assessing patients receiving chemotherapy. Current morphological criteria do not adequately evaluate lesion necrosis from anti-angiogenic therapy when no tumor volume change is measured. The evaluation of functional biomarkers rather than tumor volume may better distinguish responders from non-responders early in treatment with anti-angiogenic therapy. Perfusion CT can evaluate changes in tumor vascularity including blood flow (BF), blood volume (BV), mean transit time (MTT) and capillary permeability surface product (PS) before and after anti-angiogenic therapy with/out decrease in tumor volume. Objectives: The aims of the study are to evaluate the relationship between changes in tumor perfusion parameters and clinical outcomes of progression free survival, overall survival, and standard RECIST anatomic response criteria. A test-retest perfusion CT scan will also be performed to evaluate reproducibility of perfusion parameters in a subset of participants. Methods: In this collaborative trial, participants will be co-enrolled in the GOG-262 treatment trial. Participants will receive doublet chemotherapy of paclitaxel and carboplatin, followed by anti-angiogenic monoclonal therapy at cycle two. Perfusion CT will be performed at three time points: at baseline prior to therapy (T0), between days 18 and 21 of cycle one chemotherapy (T1), and at 8-10 days (T2) in anti-angiogenic monoclonal therapy. Participants with primary epithelial ovarian, peritoneal or fallopian tube cancer with optimally or suboptimally debulked FIGO Stage II, III or IV disease are eligible for the trial. Lesion eligibility will be evaluated by size and attenuation criteria. Accrual: ACRIN 6695 is activated at 12 GOG sites; 4/78 participants have accrued Discussion: Perfusion CT has been readily incorporated into the pre-existing clinical CT protocols and during scheduled RECIST scans. These perfusion CT functional maps of BF, BV, MTT and PS have been generated using vendor provided software without issue. Contact: Please contact Chaan Ng, MD cng@mdanderson.org for additional information.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".