Abstract
Background: Early detection is an essential step in decreasing the morbidity and mortality of breast cancer. Mammography is a proven effective tool for early breast cancer detection. The aim and objective of this study is to assess the efficacy of mammography in the evaluation of breast masses based on the Breast Imaging Reporting and Data System (BI-RADS) for differentiating between benign and malignant breast lesions keeping histopathology as gold standard
Materials and Methods: The present study is an analytical study of patients presenting with breast masses, with age group ranging between 31 to 89 years referred to the department of radio-diagnosis Findings of mammogram along with BI-RADS category were correlated with histopathological findings, keeping it as gold standard.
Results: Based on the BI-RADS 50 study cases were categorized and confirmed with histopathology, keeping it as gold standard. The diagnostic accuracy of BI-RADS IV &BI-RADS V was 96% and88 % and was found to be very high .The kappa value also shows statistical significance which were 0.92 and 0.75 respectively for BIRADS V and BIRADS IV and V.
Conclusion: This study proves the diagnostic accuracy of mammography as a method of choice to evaluate breast masses keeping histopathology as gold standard.
Keywords: Breast masses, Mammography, histopathology.
References
1. 1Ascunce N, del Moral A, Murillo A, Alfaro C, Apesteguia L, Ros J, Abascal L, Aizcorbe M, Dominguez F. Early detection programme for breast cancer in Navarra, Spain. Eur J Cancer 1994; 3 Suppl 1:41-8.
2. Ghumro AA, Khaskheli NM, Memon AA, AnsariAG, Awan MS. Clinical profile of patients with breast cancer. J Coll Physician Surg Pak 2002; 12: 28-31. 7.
3. Ohene-Yeboah M, Amaning EP. Spectrum of complaints presented at a specialist breast clinic in kumasi, Ghana. Ghana Medical Journal Sep.2008; 42(3): 110-112.
4. Abhijit M, Anantharaman, Bhoopal S, Ramanujam R. Benign breast diseases: experience at a teaching hospital in rural India. International Journal of Research in Medical Sciences 2008; 1(2): 73-78.
5. Sufian SN, Masroor I, Mirza W, Butt S, Afzal S, Sajjad Z. Evaluation of common risk factors for breast carcinoma in females: a hospital based study in Karachi, Pakistan. Asian Pac J Cancer Prev2015; 16:6347-52.
6. Franceschi D et al. Global Summit Early Detection and Access to Care Panel. Breast Cancer in Limited-Resource Countries: Early Detection and Access to Care. Breast J 2006; 12: S16–S26.
7. KopansDB. Standardized mammographic reporting. Radiol Clin North Am 1992; 30: 257–261.
8. 8.D'Orsi CJ, Kopans DB. Mammographic feature analysis. Semin Roentgenol 1993; 28: 204–230.
9. 9.American College of Radiology. BI-RADS: mammography. In Breast Imaging Reporting and Data System: BI-RADS Atlas (4th edn). American College of Radiology: Reston, VA, 2003.
10. NCCN Clinical practice guidelines in oncology. Breast cancer screening and diagnosis 2012.
11. Liberman L, Abramson AF, Squires FB, Glassman JR, Morris EA, Dershaw DD. The Breast Imaging Reporting and Data System: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol 1998; 171: 35–40.
12. Bérubé M, Curpen B, Ugolini P, Lalonde L, Ouimet-Oliva D. Level of suspicion of a mammographic lesion: use of features defined by BI-RADS lexicon and correlation with large-core breast biopsy. Can Assoc Radiol J1998; 49: 223–228.
13. Harper, P.A., E. Kelly-Fry, J.S. Noe, R.J. Bies and V.P. Jackson, 1983. Ultrasound in the evaluation of solid breast masses.Radiology, 146: 731-736.
14. 14. Stavros, A.T., D. Thickman, C.L. Rapp, M.A. Dennis ,S.H. Parker and G.A. Sisney, 1995. Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions. Radiology, 196: 123-134.
15. Hong, A.S., E.L. Rosen, M.S. Soo and J.A. Baker, 2005. BI-RADS for sonography: Positive and negative predictive values of sonographic features, 184: 1260-1265.
16. Smith RA, Caleffi M, Albert US, Chen THH, Duffy SW, Franceschi D et al. Global Summit Early Detection and Access to Care Panel. Breast Cancer in Limited-Resource Countries: Early Detection and Access to Care. Breast J 2006; 12: S16–S26.
17. Baker JA, Kornguth PJ, Lo JY, Floyd CE. Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology 1995;196: 817–822.
18. Chan KKK, Lui CY, Chu T, Chan KK, Yan AT, Wong K et al. Stratifying Risk for malignancy Using Microcalcification Descriptors from the Breast Imaging Reporting and Data System 4th Edition: Experience in a single Center in Hong Kong. J HK CollRadiol.2009; 11:149-53.
19. Orel SG, Kay N, Reynolds C, Sullivan DC. BI-RADS categorization as a predictor of malignancy. Radiology 1999; 211: 845–850.
20. World Health Organization. Cancer Fact sheet No 297, February 2011. Geneva: WHO;2011.