Open AccessStudy to Evaluate the Role of Computer aided Detection Using Full Field Digital Mammography in Breast Cancer Imaging
Author(s) -
Radhakrishnan Naveen,
N Aarthi Reddy
Publication year - 2020
Publication title -
asian journal of medical radiological research
Language(s) - English
Resource type - Journals
eISSN - 2347-338X
pISSN - 2347-3371
DOI - 10.47009/ajmrr.2020.8.2.15
Subject(s) - breast cancer , mammography , medicine , digital mammography , radiology , cancer , histopathology , breast cancer screening , computer aided diagnosis , incidence (geometry) , pathology , physics , optics
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 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.