A two-step system to differentiate mass and nonmass images on MRI improves breast cancer detection.
Optimizing both feature selection and training classifiers for mass and nonmass lesions improves the accuracy of computer-aided diagnosis (CAD) for breast MR imaging, according to a study published in Radiology.
Researchers from Canada sought to determine suitable features and optimal classifier design for a CAD system to help clinicians differentiate between mass and nonmass enhancements detected by contrast MRI breast imaging.
The researchers respectively collected 280 histologically proven mass lesions and 129 histologically proven nonmass lesions determined through MR imaging studies. The Breast Imaging Reporting and Data System classification of mass and nonmass enhancement was obtained from radiologic reports.
After extracting 176 features, the researchers ranked them according to kinetic, texture, and morphologic features for mass and nonmass lesions. They found that the best classifier performance was a two-stage cascade classifier, whereby the researchers used mass versus nonmass followed by malignant versus benign classification, as compared with a single, or one-shot, classifier, which was benign versus malignant. “Our proposed two-stage cascade classifier decreases the overall misclassification rate by 12 percent, with 72 of 409 missed diagnoses with cascade versus 82 of 409 missed diagnoses with one-shot classifier,” the authors wrote.
By separating the optimizing features, the researchers found that breast MR imaging with CAD was improved over using a one-shot classifier. They suggested that this may provide an advantage to women who are at high risk for developing breast cancer.
Leading Breast Radiologists Discuss the USPSTF Breast Cancer Screening Recommendations
May 17th 2024In recognition of National Women’s Health Week, Dana Bonaminio, MD, Amy Patel, MD, and Stacy Smith-Foley, MD, shared their thoughts and perspectives on the recently updated breast cancer screening recommendations from the United States Preventive Services Task Force (USPSTF).
Appealing Prior Authorization Denials: Can it be Effective for Emerging Technologies?
May 14th 2024While radiologists and other providers may be discouraged by insurer denials saying the use of a technological advance is “unproven and investigational,” 82 percent of appeals for prior authorization denials were approved in 2021.
ACR Collaborative Model Achieves 20 Percent Improvement in PI-QUAL Scores for Prostate MRI
May 9th 2024Using a learning network model to discuss challenges and share insights among radiology departments from five different organizations, researchers noted that 87 percent of audited prostate MRI exams had PI-QUAL scores > 4 at the conclusion of the collaborative program.
MRI-Based Deep Learning Algorithm Shows Comparable Detection of csPCa to Radiologists
May 8th 2024In a study involving over 1,000 visible prostate lesions on biparametric MRI, a deep learning algorithm detected 96 percent of clinically significant prostate cancer (csPCa) in comparison to a 98 percent detection rate for an expert genitourinary radiologist.