ArticleFrontiers in oncology2022
How Can a High-Performance Screening Strategy Be Determined for Cervical Cancer Prevention? Evidence From a Hierarchical Clustering Analysis of a Multicentric Clinical Study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- The Mclust Analysis of Tumor Budding Unveils the Role of the Collagen Family in Cervical Cancer Progression.Life (Basel, Switzerland) · 2024Article
- Article
- Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to explore the cluster patterns of cervical cancer screening strategies for detecting high-grade precancerous lesions in terms of benefits, costs, and efficiency.
methodsA total of 2,065 referral women aged 25-64 years were recruited and underwent human papillomavirus (HPV) testing, liquid-based cytology with manual reading, and cytology with artificial intelligence (AI)-assisted reading. All women were assessed by colposcopy and histological examination. We formed 14 screening strategies based on primary cytology screening, primary HPV screening incorporating HPV-16/18 genotyping triage, cytology triage, or both, and co-testing. The primary outcomes were cervical intraepithelial neoplasia grade 2 or worse (CIN2+) and grade 3 or worse (CIN3+). The hierarchical clustering method was applied to multifaceted indicators, and then, the resulting clusters were described in terms of benefits, costs, efficiency, and their interaction. This study was registered (No. ChiCTR2000034131).
resultsThe relative sensitivity of HPV-based strategies compared with cytology alone with the threshold of LSIL+ ranged from 0.68 to 1.19 for CIN2+ detection and from 0.72 to 1.11 for CIN3+ detection, whereas the relative specificity ranged from 0.55 to 1.43 for CIN2+ detection and from 0.51 to 1.51 for CIN3+ detection. Five significant clusters according to the trade-off among benefits, costs, and efficiency were identified. The cluster including four primary HPV screening strategies showed the optimal balance. HPV testing with HPV-16/18 genotyping and AI-based cytology triage presented the optimal trade-off for CIN3+ detection relative to cytology alone in terms of relative sensitivity (1.06), relative specificity (0.72), colposcopies for 1 CIN3+ (3.7 vs. 3.1), a load of follow-up for women with HPV-positive and normal cytology (7.0% vs. 22.3%), and the work of manual cytology reading (35.1% vs. 100%).
conclusionsOur study provided clinical and methodological evidence on the choice of HPV-based screening strategies. The cluster including primary HPV screening with genotyping and cytology triage showed an optimal balance among benefit, cost, and efficiency.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.