Utility of PETCT over conventional imaging in ovarian cancer.
Bibliographic record
Abstract
e17522 Background: Evidence supports PETCT in nodal assessment of epithelial ovarian cancer (EOC) during staging; and investigation of recurrence, where its sensitivity and specificity is superior over conventional imaging (CI). Limited data suggests PETCT impacts EOC management in 34% to 57% of cases, including when CI has recently been performed. This study evaluated whether PETCT provides additional information over CI in EOC and if results change management. Methods: All EOC patients who underwent publicly funded PETCT at BC Cancer Agency AND recent ( < 6 weeks) CI were eligible. Medical charts were retrospectively reviewed with descriptive analysis performed. Results: Of 270 PETCT scans performed between January 2007 and September 2017, 106 had recent CI, thus were eligible. Most common PETCT indications were: 1) investigating first recurrence (30.2%), 2) assessment for secondary debulking (21.7%) and 3) investigating progression in documented relapse (19.8%). PETCT identified greater disease burden than CI in 39.6%, typically greater nodal involvement. PETCT identified lower disease burden than CI in 14.2%. However, in 77.4% of all cases, PETCT and CI had the same clinical implications. In 33.0%, management changed following PETCT from; no treatment to treatment (17.9%), adoption of a different treatment (10.4%) and from treatment to no treatment (4.7%). In the 22.6 % of cases where PETCT and CI drew different clinical implications, management changed in 41.7%. Conclusions: Overall, PETCT was relatively rarely used to investigate EOC. Although PETCT provided greater detail regarding disease burden which may be important in select cases, we found PETCT and CI often drew the same clinical implications. Thus, for the majority CI was sufficient. While treatment changes following PETCT were frequently seen, impact on true clinical outcomes is unknown. PETCT in EOC should remain individualised.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".