Abstract
Objective
To establish and validate a risk prediction model for cervical high-grade squamous
intraepithelial lesions (HSIL).
Methods
This retrospective study included patients who underwent cervical biopsies at the
Cervical Disease Centre of Maternal and Child Hospital of Hubei Province between January
2021 and December 2021.
Results
A total of 1630 patients were divided into the HSIL + cervical lesion group (n = 186)
and the ≤ LSIL cervical lesions group (n = 1444). LSIL, ASC-H, HSIL and SCC, high-risk
HPV, HPV16, HPV18/45, multiple HPV strains, acetowhite epithelium, atypical vessels,
and mosaicity were independently associated with HSIL + lesions. These factors were
used to establish a risk prediction model with a demonstrated area under the curve
(AUC) of 0.851 and a C-index of 0.829. Calibration curve analysis showed that the
model performed well, with a mean absolute error (MAE) of 0.005. The decision curve
showed that the model created by combining the risk factors was more specific and
sensitive than each predictive variable.
Conclusion
The model for predicting HSIL demonstrated promising predictive capability and might
help identify patients requiring biopsy and treatment.
Abbreviations:
CIN (cervical intraepithelial neoplasias), HPV (human papilloma virus), TCT (ThinPrep cytological test), NILM (negative for intraepithelial lesion or malignancy), ASC-US (atypical cytology of undetermined significance), LSIL (low-grade squamous intraepithelial lesion), HSIL (high-grade squamous intraepithelial lesion), ASC-H (atypical squamous cells cannot exclude high-grade), SCC (squamous cell carcinoma), TCT (Thinprep cytologic test), SCJ (squamocolumnar junction), TBS (the Bethesda System), AGC (abnormal blood glandular cells), AIS (situ adenocarcinoma), ACC (adenocarcinoma), ROC (receiver operating characteristic), AUC (area under the curve), MAE (mean absolute error)Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to European Journal of Obstetrics and Gynecology and Reproductive BiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Cervical Cancer Screening: Past, Present, and Future.Sex Med Rev. 2020; 8: 28-37
- Cervical Cancer Prevalence, Incidence and Mortality in Low and Middle Income Countries: A Systematic Review.Asian Pac J Cancer Prev. 2018; 19: 319-324
Cohen PA, Jhingran A, Oaknin A, Denny LJTL. Cervical cancer. 2019; 393: 169-82.
Bruni L, Barrionuevo-Rosas L, Albero G, al. e. ICO information centre on HPV and cancer (HPV information centre). Human papillomavirus and related diseases in the world Summary Report 2017; 27.
- The growing burden of cancer in India: epidemiology and social context.Lancet Oncol. 2014; 15: e205-e212
- Elimination of cervical cancer: challenges for developing countries.Ecancermedicalscience. 2019; 13: 975
- Cervical Cancer Treatment Delays and Associated Factors in a Cohort of Women From a Developing Country.J Glob Oncol. 2019; 5: 1-11
Egawa N, Doorbar JJVR. The low-risk papillomaviruses. 2017; 231: 119-27.
- Clinical Utility of Human Papillomavirus Genotyping in Cervical Cancer Screening: A Systematic Review.J Low Genit Tract Dis. 2020; 24: 1-13
- Efficacy of Pap test in combination with ThinPrep cytological test in screening for cervical cancer.Asian Pac J Cancer Prev. 2012; 13: 1651-1655
- The clinical research of Thinprep Cytology Test (TCT) combined with HPV-DNA detection in screening cervical cancer.Cell Mol Biol (Noisy-le-grand). 2017; 63: 92-95
- Benchmarking CIN 3+ risk as the basis for incorporating HPV and Pap cotesting into cervical screening and management guidelines.J Low Genit Tract Dis. 2013; 17: S28-S35
- 2019 ASCCP Risk-Based Management Consensus Guidelines for Abnormal Cervical Cancer Screening Tests and Cancer Precursors.J Low Genit Tract Dis. 2020; 24: 102-131
- Incident cervical HPV infections in young women: transition probabilities for CIN and infection clearance.Cancer Epidemiol Biomarkers Prev. 2011; 20: 287-296
- The elevated 10-year risk of cervical precancer and cancer in women with human papillomavirus (HPV) type 16 or 18 and the possible utility of type-specific HPV testing in clinical practice.J Natl Cancer Inst. 2005; 97: 1072-1079
- Persistence of type-specific human papillomavirus infection and increased long-term risk of cervical cancer.J Natl Cancer Inst. 2011; 103: 1387-1396
- Use of primary high-risk human papillomavirus testing for cervical cancer screening: interim clinical guidance.Gynecol Oncol. 2015; 136: 178-182
- Risk Prediction of Cervical Cancer and Precancers by Type-Specific Human Papillomavirus: Evidence from a Population-Based Cohort Study in China.Cancer Prev Res (Phila). 2017; 10: 745-751
- The risk prediction models for occurrence of cervical cancer: a systematic review.Zhonghua Liu Xing Bing Xue Za Zhi. 2021; 42: 1855-1862
- Towards a data-driven system for personalized cervical cancer risk stratification.Sci Rep. 2022; 12: 12083
- Predictions of cervical cancer identification by photonic method combined with machine learning.Sci Rep. 2022; 12: 3762
- 2012 updated consensus guidelines for the management of abnormal cervical cancer screening tests and cancer precursors.Obstet Gynecol. 2013; 121: 829-846
- HPV E6/E7 mRNA test for the detection of high grade cervical intraepithelial neoplasia (CIN2+): a systematic review.Infect Agent Cancer. 2020; 15: 9
- Squamous intraepithelial lesions (SIL: LSIL, HSIL, ASCUS, ASC-H, LSIL-H) of Uterine Cervix and Bethesda System.Cytojournal. 2021; 18: 16
- The association between serum squamous cell carcinoma antigen and recurrence and survival of patients with cervical squamous cell carcinoma: A systematic review and meta-analysis.Gynecol Oncol. 2018; 150: 190-200
- The precision prevention and therapy of HPV-related cervical cancer: new concepts and clinical implications.Cancer Med. 2018; 7: 5217-5236
- Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study.Cancer Cell Int. 2021; 21: 35
- Deep learning based cervical screening by the cross-modal integration of colposcopy, cytology, and HPV test.Int J Med Inform. 2022; 159104675
- Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis.NPJ Digit Med. 2022; 5: 19
Article info
Publication history
Published online: December 06, 2022
Accepted:
December 4,
2022
Received in revised form:
November 21,
2022
Received:
June 15,
2022
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
