Pooled analysis of the performance of liquid‐based cytology in population‐based cervical cancer screening studies in China
Bibliographic record
Abstract
BACKGROUND: Liquid-based cytology (LBC) has been widely used for cervical cancer screening. Despite numerous studies and systematic reviews, to the authors' knowledge few large studies to date have focused on biopsy-confirmed cervical lesions and controversy remains concerning its diagnostic accuracy. The objective of the current study was to assess LBC for detecting biopsy-confirmed cervical intraepithelial neoplasia (CIN) and cancer. METHODS: A pooled analysis of LBC using data from 13 population-based, cross-sectional, cervical cancer screening studies performed in China from 1999 to 2008 was performed. Participants (n = 26,782) received LBC and human papillomavirus testing. Women found to be positive on screening were referred for colposcopy and biopsy. The accuracy of LBC for detecting biopsy-confirmed CIN of type 2 or worse (CIN2+) as well as CIN type 3 or worse (CIN3+) lesions was analyzed. RESULTS: Of 25,830 women included in the analysis, CIN2+ was found in 107 of 2612 with atypical squamous cells (4.1%), 142 of 923 with low-grade squamous intraepithelial neoplasia (15.4%), 512 of 784 with high-grade squamous intraepithelial neoplasia (65.3%), 29 of 30 with squamous cell carcinoma (96.7%), 4 of 27 with atypical glandular cells (14.8%), and 85 of 21,454 with normal cytology results (0.4%). No invasive cancers were found to have atypical squamous cells, atypical glandular cells, or cytologically normal slides. The overall sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of LBC for detecting CIN2+ were 81.0%, 95.4%, 38.3%, 99.3 %, and 94.9%, respectively. Although Hybrid Capture 2 was more sensitive than LBC, the specificity, positive predictive value, and overall accuracy of LBC were higher than those of Hybrid Capture2 at 85.2%, 18.6%, and 85.5%, respectively. CONCLUSIONS: The results of the current study indicate that the performance of LBC can effectively predict the risk of existing CIN2+ and may be a good screening tool for cervical cancer prevention in a developing country.
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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 | 0.041 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".