Study to Evaluate the Role of Computer aided Detection Using Full Field Digital Mammography in Breast Cancer Imaging
Breast Cancer Imaging
Background: Mammography is acknowledged as the single most effective method of screening for breast cancer and is credited with helping to reduce breast cancer mortality by approximately 30%. CAD systems are a new tool in detecting breast cancers on screening mammograms and in detecting potentially suspicious abnormalities on a mammogram. The aim & objective is to main aim of the present study to evaluate the performance of Computer Aided Detection using Full Field Digital Mammography in Breast Cancer imaging. Subjects and Methods:In the present study, Cases with lump breast with clinical suspicion of breast cancer and post op recurrence of breast cancer were imaged with FFDM and images were read on the viewing monitor without and with the aid of CAD software. The present study confirms that the diagnosis of breast cancer is made only following histopathology of respected specimen. Results: The maximum incidence was in 41-50 years and 51-60 years which was 13 cases in each group (30 %). There were 25 cases out of 40 (62.5%) in which the lesion was marked by CAD. Out of which in 20 cases (50%) only one lesion was marked by CAD and in 4 cases (10%) two lesions were marked by CAD. The total number of lesions marked by CAD was 25 (62.5%). Majority of patients had scattered fibro glandular density of breast. This was present in 19 patients (47.5%). 10 patients (20%) had heterogeneously dense breast, 07 patients (17.5%) had fatty breast and 04 patients (10%) had extremely dense breast. In majority of cases the lesion type was mass alone which was present in 26 cases (60%). While 10 cases (25%) presented as mass with microcalcifications and 4 cases (10%) presented with microcalcifications alone. In 24 cases there was no spread of cluster of micro calcification (60%). In 7 cases (17.5%) the spread of cluster of micro calcification was <10mm, in 4 cases (10%) the spread of cluster of micro calcification was 21-30 mm and in 5 cases ( 12.5%) the spread of cluster of micro calcification was > 40mm. In majority of cases the HPE revealed DCIS which was seen in 22 cases (55%), 08 cases (20%) were invasive ductal carcinoma and 02 cases was invasive lobular carcinoma. In 22 cases (55%) the BIRADS for the breast affected with cancer was BIRADS-V. While in 14 cases (35%) the score was BIRADS-IV, 04 cases (10%) the score was BIRADS-VI and in 02 case (5%) the score was BIRADS-III. The sensitivity, specificity and accuracy of CAD for detection of mass were 70%, 100% and 85% respectively and for detection of cluster of microcalcification were 100% respectively. Conclusion: CAD with FFDM is good at detection of Microcalcifications. Detection of masses is better without the aid of CAD as compared to CAD. However detection of lesion improves if reading of mammogram is done both with and without CAD.
2. Jemal A, Clegg LX, Ward E, Ries LA, Wu X. Annual report to the nation on the status of cancer. Cancer. 1975;101:3–27.
3. Helms RL, O’Hea EL, Corso M. Body image issues in women with breast cancer. Psychol Health Med. 2008;13(3):313–325. Available from: https://dx.doi.org/10. 1080/13548500701405509.
4. Giger ML. Computer-aided diagnosis of breast lesions in medical images. Comput Sci Eng. 2000;2(5):39–45. Available from: https://dx.doi.org/10.1109/5992.877391.
5. Rangayyan RM, Chapelhill AFF, Usa NC. Detection of asymmetry between left and right mammograms. Proceedings of the 7th International Workshop on Digital Mammography. 2004;p. 651–658.
6. The JS, Schilling KJ, Hoffmeister JW, Friedmann E, McGinnis R, Holcomb RG. Detection of Breast Cancer with Full-Field Digital Mammography and Computer-Aided Detection. Am J Roentgenol. 2009;192(2):337–340. Available from: https://dx. doi.org/10.2214/ajr.07.3884.
7. Brem RF, Rapelyea JA, Zisman G, Hoffmeister JW, DeSimio MP. Evaluation of breast cancer with a computer-aided detection system by mammographic appearance and histopathology. Cancer. 2005;104(5):931–935. Available from: https://dx.doi. org/10.1002/cncr.21255.
8. Malich A, Sauner D, Marx C, Facius M, Boehm T, Pfleiderer SO, et al. Influence of Breast Lesion Size and Histologic Findings on Tumor Detection Rate of a Computer-aided Detection System. Radiology. 2003;228(3):851–856. Available from: https://dx. doi.org/10.1148/radiol.2283011906.
Copyright (c) 2020 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.