Detection of Cervical Intraepithelial Neoplasias and Cancers in Cervical Tissue by In Vivo Light Scattering
Bibliographic record
Abstract
OBJECTIVE: To examine the utility of in vivo elastic light scattering measurements to identify cervical intraepithelial neoplasias (CIN) 2/3 and cancers in women undergoing colposcopy and to determine the effects of patient characteristics such as menstrual status on the elastic light scattering spectroscopic measurements. MATERIALS AND METHODS: A fiber optic probe was used to measure light transport in the cervical epithelium of patients undergoing colposcopy. Spectroscopic results from 151 patients were compared with histopathology of the measured and biopsied sites. A method of classifying the measured sites into two clinically relevant categories was developed and tested using five-fold cross-validation. RESULTS: Statistically significant effects by age at diagnosis, menopausal status, timing of the menstrual cycle, and oral contraceptive use were identified, and adjustments based upon these measurements were incorporated in the classification algorithm. A sensitivity of 77±5% and a specificity of 62±2% were obtained for separating CIN 2/3 and cancer from other pathologies and normal tissue. CONCLUSIONS: The effects of both menstrual status and age should be taken into account in the algorithm for classifying tissue sites based on elastic light scattering spectroscopy. When this is done, elastic light scattering spectroscopy shows good potential for real-time diagnosis of cervical tissue at colposcopy. Guiding biopsy location is one potential near-term clinical application area, while facilitating "see and treat" protocols is a longer term goal. Improvements in accuracy are essential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".