Evaluation of Needle Core Biopsy Reporting Categories with Histopathological Findings on Breast Excision Specimens
Objective: To correlate histopathological outcome on breast excision specimens with reporting categories of Needle Core Biopsy, designated by the Royal College of Pathologists, London (UK), with special emphasis on B3 category lesions. Study Design: Cross-sectional study. Place and Duration of Study: Histopathology Department, Foundation University Medical College, Islamabad Pakistan, from Jul 2022 to May 2023. Methodology: Data of 162 cases of Needle Core Biopsies (NCB) breast with subsequent excisions were collected through the Medix Online Histopathology Reporting System (MOHRS). The different categories on NCB were evaluated and compared with the outcome on subsequent excisions. Results: Out of total cases of NCB, 18(11%) cases were B2 category, 16(10%) cases were B3 category, 4(2.5%) cases were B4 category, and 121(74.6%) cases belonged to B5 category lesions. Among the B5 category lesions, four cases were diagnosed as B5a, while the remaining 117(72%) were assigned B5b. In the B3 category, 11(68.75%) cases followed the diagnosis on further excision, while 5(31.25%) cases showed discordance. The overall positive predictive value (PPV) for malignancy in B3 lesions was 0.43, without statistical significance between subcategories. The B5b category lesions showed complete concordance with the diagnosis of invasive carcinoma on further excision. Conclusion: Needle Core Biopsy is a powerful tool in the diagnosis of breast lesions for better treatment plan on radical surgery. However, the B3 category encompasses a wide range of lesions that have uncertain malignant potential, and their PPV for malignancy is quite variable. ..........
Authors
- Tariq Sarfraz (ORCID: https://orcid.org/0009-0007-4059-1430)
- Rubina Riaz
- Yasmin Wahid
- Rehana Ramzan
- Saba Aneeqa
- Salma Afzal
Institutions
- Foundation University Medical College (PK)
Publication Details
- Journal
- Pakistan Armed Forces Medical Journal
- Published
- 2025-08-30
- DOI
- https://doi.org/10.51253/pafmj.v75i4.10434
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00