Longitudinal, label-free, quantitative tracking of cell death and viability in a 3D tumor model with OCT
Bibliographic record
Abstract
Three-dimensional in vitro tumor models are highly useful tools for studying tumor growth and treatment response of malignancies such as ovarian cancer. Existing viability and treatment assessment assays, however, face shortcomings when applied to these large, complex, and heterogeneous culture systems. Optical coherence tomography (OCT) is a noninvasive, label-free, optical imaging technique that can visualize live cells and tissues over time with subcellular resolution and millimeters of optical penetration depth. Here, we show that OCT is capable of carrying out high-content, longitudinal assays of 3D culture treatment response. We demonstrate the usage and capability of OCT for the dynamic monitoring of individual and combination therapeutic regimens in vitro, including both chemotherapy drugs and photodynamic therapy (PDT) for ovarian cancer. OCT was validated against the standard LIVE/DEAD Viability/Cytotoxicity Assay in small tumor spheroid cultures, showing excellent correlation with existing standards. Importantly, OCT was shown to be capable of evaluating 3D spheroid treatment response even when traditional viability assays failed. OCT 3D viability imaging revealed synergy between PDT and the standard-of-care chemotherapeutic carboplatin that evolved over time. We believe the efficacy and accuracy of OCT in vitro drug screening will greatly contribute to the field of cancer treatment and therapy evaluation.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".