Breast tomosynthesis may be a work-in-progress in the RSNA exhibit halls, but researchers are already considering ways to incorporate computer-assisted detection algorithms to improve its efficacy.
Breast tomosynthesis may be a work-in-progress in the RSNA exhibit halls, but researchers are already considering ways to incorporate computer-assisted detection algorithms to improve its efficacy.
Researchers from the University of Chicago and the University of Michigan presented papers Tuesday morning that evaluated several techniques for reconstructing suspected tumors from the multiple slices of tomographic acquisition or from the compiled volume data set. Both groups worked with the GE Digital Breast Tomosynthesis prototype developed by Dr. Daniel Kopans at Massachusetts General Hospital.
The Michigan researchers produced an area of 0.87 under the ROC curve with 80% sensitivity and two false positives per case, using a set of synthesized data. Looking only at projection views, the area under the ROC curve dropped to 0.82. Both were based on 26 cases with 23 masses, including 13 malignancies.
"We achieved fewer false positives with 3D views than with 2D, and the information fusion is more effective," said lead researcher Heang-Ping Chan, Ph.D.
Chicago researchers evaluated only projection views. A standard tomosynthesis scan involves 11 low-dose conebeam projections through the breast at 5-degree angles spread over an arc. The projections intersect with each other at various points and allow the software to confirm areas of suspected masses from more than one angle. For the Chicago trial, any region that was intersected by six or more projections was considered a positive finding. Among 21 cases of confirmed breast masses, the team achieved a sensitivity of 86%, but with six false positives per case.
In addition to projection views and reconstructed volumes, CAD could also be developed to look for suspicious findings across the 50 to 80 reconstructed image slices that could be produced for each set of breast data, the authors said.
Could a Deep Learning Model for Mammography Improve Prediction of DCIS and Invasive Breast Cancer?
April 15th 2024Artificial intelligence (AI) assessment of mammography images may significantly enhance the prediction of invasive breast cancer and ductal carcinoma in situ (DCIS) in women with breast cancer, according to new research presented at the Society for Breast Imaging (SBI) conference.
Mammography-Based AI Abnormality Scoring May Improve Prediction of Invasive Upgrade of DCIS
April 9th 2024Emerging research suggests that an artificial intelligence (AI) score of 75 or greater for mammography abnormalities more than doubles the likelihood of invasive upgrade of ductal carcinoma in situ (DCIS) diagnosed with percutaneous biopsy.
Mammography Study: AI Improves Breast Cancer Detection and Reduces Reading Time with DBT
April 3rd 2024An emerging artificial intelligence (AI) model demonstrated more than 12 percent higher specificity and reduced image reading time by nearly six seconds in comparison to unassisted radiologist interpretation of digital breast tomosynthesis (DBT) images.