Light scattering from cervical cells throughout neoplastic progression: influence of nuclear morphology, DNA content, and chromatin texture
Bibliographic record
Abstract
A number of noninvasive fiber optic optical technologies are under development for real-time diagnosis of neoplasia. We investigate how the light scattering properties of cervical cells are affected by changes in nuclear morphology, DNA content, and chromatin texture, which occur during neoplastic progression. We used a Cyto-Savant computer-assisted image analysis system to acquire quantitative nuclear features measurements from 122 Feulgen-thionin-stained histopathologic sections of cervical tissue. A subset of the measured nuclear features was incorporated into a finite-difference time-domain (FDTD) model of cellular light scattering. The magnitude and angular distribution of scattered light was calculated for cervical cells as a function of pathologic grade. The nuclear atypia strongly affected light scattering properties. The increased size and elevated DNA content of nuclei in high-grade lesions caused the most significant changes in scattering intensity. The spatial dimensions of chromatin texture features and the amplitude of refractive index fluctuations within the nucleus impacted both the angular distribution of scattering angles and the total amount of scattered light. Cellular scattering is sensitive to changes in nuclear morphology that accompany neoplastic progression. Understanding the quantitative relationships between nuclear features and scattering properties will aid in the development of noninvasive optical technologies for detection of precancerous conditions.
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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 | 0.000 |
| 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) | 0.000 | 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".