Computer-aided diagnosis in breast tomosynthesis and chest CT

Can Artificial Intelligence Beat Humans in Detecting Breast Malignancy on Mammograms?

BACKGROUND

The study was aimed at identifying how useful Computer-Aided Detection (CAD) could be in reducing false-negative reporting in mammography and early detection of breast cancer at an early stage as the best protection is early detection.

MATERIALS AND METHODS

This retrospective study was conducted in a tertiary care setup of Atomic Energy Cancer Hospital, Nuclear Medicine, Oncology and Radiotherapy Institute (AECH-NORI), where 33 patients with suspicious findings on mammography and subsequent biopsy-proven malignancy were included. The findings of mammography including the lesion type, breast parenchymal density, and sensitivity of CAD detection, as well as the final biopsy results, were recorded. A second group of 40 normal screening mammograms was also included who had no symptoms, had Breast Imaging-Reporting and Data System category I(BI-RADS I) mammograms, and had no pathology identified on correlative sonomammography as well.

RESULTS

A total of 35 masses, 11 pleomorphic clusters of microcalcification, five clustered foci of macrocalcification, and nine lesions with pleomorphic clusters of microcalcification and two with pleomorphic clusters of microcalcification only were included. The CAD system was able to identify 26 masses (74%), eight lesions with pleomorphic clusters of microcalcification (72%), five foci of macrocalcification (100%), six lesions with pleomorphic clusters of microcalcification (66%), and two pleomorphic clusters of microcalcification without formed mass (100%). The overall sensitivity of the CAD system was 75.8%. CAD was able to identify 13 out of 16 masses with invasive ductal carcinoma (81.3%), eight out of nine lesions proven as invasive ductal carcinoma with ductal carcinoma in situ (DCIS) (88.9%), two out of five masses with invasive lobular carcinoma (40%), four out of four masses with invasive mammary carcinoma (100%), and zero out of one lesion identified as medullary carcinoma (0%). There was 100% detection for pleomorphic clusters of microcalcification without formed mass with CAD marking two out of two mammograms.

CONCLUSION

CAD performed better with combined lesions, accurately marked pleomorphic clusters of microcalcification, and identified small lesions in predominant fibrofatty parenchymal density but was not reliable in dense breast, areas of asymmetric increased density, summation artifacts, edematous breast parenchyma, and retroareolar lesions. It also performed poorly with ill-defined lesions of invasive lobular carcinoma. Human intelligence hence beats CAD for the diagnosis of breast malignancy in mammograms as per our experience.

A statistical model of retinal optical coherence tomography image data

Optical coherence tomography (OCT) is an important mode of biomedical imaging for the diagnosis and management of ocular disease. Here we report on the construction of a synthetic retinal OCT image data set that may be used for quantitative analysis of image processing methods. Synthetic image data were generated from statistical characteristics of real images (n = 14). Features include: multiple stratified layers with representative thickness, boundary gradients, contour, and intensity distributions derived from real data. The synthetic data also include retinal vasculature with typical signal obscuration beneath vessels. This synthetic retinal image can provide a realistic simulated data set to help quantify the performance of image processing algorithms.

Breast tomosynthesis

Digital mammography has been well-evaluated for the diagnosis of breast cancer. The scientific data show that mammography alone, especially in dense breast parenchyma, has its weaknesses. These weaknesses are due to the low contrast of tumors in comparison with the surrounding parenchyma and the overlying structures that mask tumors. The initial results from tomosynthesis studies show a tendency for better imaging and higher accuracy and lower recall rates with tomosynthesis. We present in this article a literature review of the development of breast tomosynthesis and follow it with case examples.

[Physical aspects of different tomosynthesis systems]

Digital breast tomosynthesis (DBT) is a new image processing technique based on digital mammography technology. Image slices of the stationary compressed breast are reconstructed from multiple images taken at different angles of the X-ray tube at the same time. The main goal is to achieve a similar radiation dose exposure as common encountered in traditional digital mammography. One of the key advantages of DBT is that lesions are less likely to be hidden amongst normal tissues as they are in traditional digital mammography. This way the quality of diagnosis can be improved, especially for dense breasts. Current DBT implementations from several manufacturers differ in certain features such as scanning angle, number of projections, scanning time, pixel size, reconstruction methods and type of tube movement. A comparison and description of these different characteristics as well as a discussion on the proposed number of imaging planes and related radiation dose requirements are given.