KEY TAKEAWAYS
- The study aimed to investigate the enhanced detection of epithelial cell abnormalities in cervical smear samples through the incorporation of cell block analysis.
- Researchers noticed that cell block analysis shows promise for superior diagnostic accuracy, particularly in detecting HSIL.
Cervical cancer, a global health concern with high mortality, necessitates advances in screening for its elimination. Elif Kuzucular and her team aimed to assess the impact of cell block analysis on detecting epithelial cell abnormalities in cervical smear samples.
Researchers performed an inclusive analysis involving 559 patients with suspected cervical pathology. Both liquid-based cytology (LBC) and cell blocks were employed for sample analysis. Biopsy results from 149 out of the 559 patients were subsequently obtained.
About 50 patients diagnosed with HSIL by biopsy showed significant disparities in LBC and cell block results. Only 12 were identified as HSIL by LBC, with 22 as LSIL, 12 as ASCUS, and 4 as ASC-H (p < 0.001). In contrast, cell block analysis identified 20 as HSIL, 17 as LSIL, 7 as NILM, 4 as ‘unsatisfactory,’ and 2 as ASC (p < 0.001). LBC detected only 1 of the 10 patients with biopsy-diagnosed tumors, where 7 were HSIL, 1 ASCUS, and 1 AGC. Cell block analysis results in patients with biopsy-diagnosed tumors were 7 HSIL, 1 tumor, 1 ASC, and 1 LSIL.
The study concluded that cell block analysis exhibits potential superiority over LBC in diagnosing cervical pathologies, especially in detecting HSIL. Despite both methods demonstrating limitations in tumor diagnosis, cell blocks emerge as a valuable complementary tool to LBC, offering enhanced diagnostic accuracy and the advantage of revealing histological architecture.
No Fundings were provided for this study.
Source: https://pubmed.ncbi.nlm.nih.gov/38266543/
Kuzucular E, Ozden F, Muezzinoglu B. Comparison of liquid-based cytology and cell blocks prepared from cell remnants for diagnosis of cervical pathology. Ann Diagn Pathol. 2024 Apr;69:152265. doi: 10.1016/j.anndiagpath.2024.152265. Epub 2024 Jan 12. PMID: 38266543.
