Biologically-Targeted Detection of Primary and Micro-Metastatic Ovarian Cancer
Bibliographic record
Abstract
Ovarian cancer is the leading cause of morbidity/mortality from gynecologic malignancy. Early detection of disease is difficult due to the propensity for ovarian cancer to disseminate throughout the peritoneum. Currently, there is no single accurate test to detect primary or recurrent ovarian cancer. We report a novel clinical strategy using PPF: a multimodal, PET and optical, folate receptor (FR)-targeted agent for ovarian cancer imaging. The capabilities of PPF were evaluated in primary human ovarian cancer cells, in vivo xenografts derived from primary cells and ex vivo patient omemtum, as the heterogeneity and phenotype displayed by patients is retained. Primary cells uptake PPF in a FR-dependent manner demonstrating approximately a 5- to 25-fold increase in fluorescence. By both PET and fluorescence imaging, PPF specifically delineated FR-positive, ovarian cancer xenografts, with similar tumor-to-background ratios of 8.91±0.91 and 7.94±3.94, and micro-metastatic studding (<1mm), which demonstrated a 3.5-fold increase in PPF uptake over adjacent normal tissue. Ex vivo patient omentum demonstrated selective uptake of PFF by tumor deposits. The ability of PPF to identify metastatic deposits <1mm could facilitate more complete debulking (currently, optimal debulking is <10mm residual tumor), by providing a more sensitive imaging strategy improving treatment planning, response assessment and residual/recurrent disease detection. Therefore, PPF is a novel clinical imaging strategy that could substantially improve the prognosis of patients with ovarian cancer by allowing pre-, post- and intra-operative tumor monitoring, detection and possibly treatment throughout all stages of therapy and tumor progression.
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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 | 0.000 |
| 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 | 0.001 | 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".