HISTOPATHOLOGICAL FEATURES AND THERAPEUTIC OUTCOMES IN CERVICAL INTRAEPITHELIAL NEOPLASIA: A CROSS-SPECIALTY CLINICAL INVESTIGATION
Main Article Content
Keywords
Histopathological Features, Therapeutic Outcomes, Cervical Intraepithelial Neoplasia, Clinical Investigation
Abstract
Background: Cervical Intraepithelial Neoplasia (CIN) is a pre-cancerous condition associated with persistent infection by high-risk human papillomavirus (HPV). CIN is graded into three levels (CIN 1, CIN 2, CIN 3) based on histopathological features, with treatment strategies varying depending on the severity of the lesion. This study aims to assess the histopathological characteristics of CIN and evaluate therapeutic outcomes in a cohort of 75 patients.
Methods: This retrospective study analyzed 75 patients diagnosed with CIN between January 2020 and December 2020. Patients were classified by CIN grade and underwent different treatments, including conservative management, ablative therapies (cryotherapy, laser ablation), or excisional therapies (LEEP, conization), based on lesion severity. Therapeutic outcomes, including lesion resolution, recurrence, and progression, were tracked over a two-year follow-up period. Factors such as patient age, HPV type, immune status, and excision margin status were also analyzed for their impact on outcomes.
Results: Of the 75 patients, 42.7% had CIN 1, 29.3% had CIN 2, and 28% had CIN 3. Lesion regression was observed in 71.9% of CIN 1 patients managed conservatively. Recurrence was most frequent in CIN 3 (14.3%), followed by CIN 2 (18.2%) and CIN 1 (9.4%). Positive excision margins, high-risk HPV infection, and compromised immune status were significant predictors of recurrence. Patients with high-risk HPV strains had a 27% recurrence rate compared to 9% for other HPV types. Immune-compromised patients had a 60% recurrence rate compared to 15% for immunocompetent patients.
Conclusions: Histopathological features and therapeutic outcomes in CIN are strongly influenced by lesion severity, HPV type, immune status, and excision margins. While low-grade CIN often regresses, higher-grade lesions require aggressive intervention and close monitoring. Personalized treatment strategies based on patient characteristics and lesion factors are essential to optimize therapeutic success and minimize recurrence. The findings support the need for ongoing HPV vaccination and effective screening programs to prevent CIN progression to cervical cancer.
References
2. Darragh, T.M., Colgan, T.J., Cox, J.T., Heller, D.S., Henry, M.R., Luff, R.D., McCalmont, T., Nayar, R., Palefsky, J.M., Stoler, M.H. et al., 2012. The Lower Anogenital Squamous Terminology Standardization Project for HPV-Associated Lesions: Background and consensus recommendations from the College of American Pathologists and the American Society for Colposcopy and Cervical Pathology. Archives of Pathology & Laboratory Medicine, 136(10), pp.1266–1297.
3. Mills, A.M., Carrilho, C., Focchi, G.R.A., Kong, C.S., Park, K.J., Regauer, S. & Saco, A., 2019. Squamous intraepithelial lesions of the uterine cervix. In WHO Classification of Tumors Editorial Board. Female Genital Tumors, 5th ed. Lyon, France: International Agency for Research on Cancer, pp.342–346.
4. McCredie, M.R., Sharples, K.J., Paul, C., Baranyai, J., Medley, G., Jones, R.W. & Skegg, D.C., 2008. Natural history of cervical neoplasia and risk of invasive cancer in women with cervical intraepithelial neoplasia 3: A retrospective cohort study. The Lancet Oncology, 9(5), pp.425–434.
5. Massad, L.S., Einstein, M.H., Huh, W.K., Katki, H.A., Kinney, W.K., Schiffman, M., Solomon, D., Wentzensen, N. & Lawson, H.W., 2013. 2012 updated consensus guidelines for the management of abnormal cervical cancer screening tests and cancer precursors. Journal of Lower Genital Tract Disease, 17(S1), pp.S1–S27.
6. Perkins, R.B., Guido, R.S., Castle, P.E., Chelmow, D., Einstein, M.H., Garcia, F., Huh, W.K., Kim, J.J., Moscicki, A.B., Nayar, R. et al., 2020. 2019 ASCCP Risk-Based Management Consensus Guidelines for Abnormal Cervical Cancer Screening Tests and Cancer Precursors. Journal of Lower Genital Tract Disease, 24(2), pp.102–131.
7. Stoler, M.H. & Schiffman, M., 2001. Interobserver reproducibility of cervical cytologic and histologic interpretations: Realistic estimates from the ASCUS-LSIL Triage Study. JAMA, 285(11), pp.1500–1505.
8. Castle, P.E., Stoler, M.H., Solomon, D. & Schiffman, M., 2007. The relationship of community biopsy-diagnosed cervical intraepithelial neoplasia grade 2 to the quality control pathology-reviewed diagnoses: An ALTS report. American Journal of Clinical Pathology, 127(6), pp.805–815.
9. Carreon, J.D., Sherman, M.E., Guillen, D., Solomon, D., Herrero, R., Jeronimo, J., Wacholder, S., Rodriguez, A.C., Morales, J., Hutchinson, M. et al., 2007. CIN2 is a much less reproducible and less valid diagnosis than CIN3: Results from a histological review of population-based cervical samples. International Journal of Gynecological Pathology, 26(4), pp.441–446.
10. Adesina, A., Chumba, D., Nelson, A.M., Orem, J., Roberts, D.J., Wabinga, H., Wilson, M. & Rebbeck, T.R., 2013. Improvement of pathology in sub-Saharan Africa. The Lancet Oncology, 14(4), pp.e152–e157.
11. Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C. & Litjens, G., 2020. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: A diagnostic study. The Lancet Oncology, 21(2), pp.233–241.
12. Courtiol, P., Maussion, C., Moarii, M., Pronier, E., Pilcer, S., Sefta, M., Manceron, P., Toldo, S., Zaslavskiy, M., Le Stang, N. et al., 2019. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nature Medicine, 25(10), pp.1519–1525.
13. Halicek, M., Shahedi, M., Little, J.V., Chen, A.Y., Myers, L.L., Sumer, B.D. & Fei, B., 2019. Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks. Scientific Reports, 9(1), p.14043.
14. Lucas, M., Jansen, I., Savci-Heijink, C.D., Meijer, S.L., de Boer, O.J., van Leeuwen, T.G., de Bruin, D.M. & Marquering, H.A., 2019. Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Archiv, 475(1), pp.77–83.
15. Valente, P.T. & Schantz, H.D., 2001. Cytology automation: An overview. Laboratory Medicine, 32(11), pp.686–690.
16. Landau, M.S. & Pantanowitz, L., 2019. Artificial intelligence in cytopathology: A review of the literature and overview of commercial landscape. Journal of the American Society of Cytopathology, 8(4), pp.230–241.
17. Guo, P., Almubarak, H., Banerjee, K., Stanley, R.J., Long, R., Antani, S., Thoma, G., Zuna, R., Frazier, S.R. & Moss, R.H. et al., 2016. Enhancements in localized classification for uterine cervical cancer digital histology image assessment. Journal of Pathology Informatics, 7(1), p.51.
18. Sornapudi, S., Stanley, R.J., Stoecker, W.V., Almubarak, H., Long, R., Antani, S., Thoma, G., Zuna, R. & Frazier, S.R., 2018. Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels. Journal of Pathology Informatics, 9(1), p.5.