Michiro Sasaki1, Mitsuhiro Tozaki2, Alejandro Rodríguez-Ruiz3, Daisuke Yotsumoto4, Yumi Ichiki5, Aiko Terawaki5, Shunichi Oosako5, Yasuaki Sagara4, Yoshiaki Sagara6. 1. Department of Radiology, Sagara Perth Avenue Clinic, 26-13 Shinyashiki-cho, Kagoshima City, Kagoshima, 892-0838, Japan. chacha622@sagara.or.jp. 2. Department of Radiology, Sagara Hospital, 3-31 Matsubara-cho, Kagoshima City, Kagoshima, 892-0833, Japan. 3. ScreenPoint Medical BV, Toernooiveld 300, 6525 EC, Nijmegen, The Netherlands. 4. Department of Breast Surgery, Sagara Hospital, 3-31Mtsubara-cho, Kagoshima City, Kagoshima, 892-0833, Japan. 5. Image Inspection Department, Sagara Perth Avenue Clinic, 26-13 Shinyashiki-cho, Kagoshima City, Kagoshima, 892-0838, Japan. 6. Department of Radiology, Sagara Hospital Affiliated Breast Center, 3-28 Tenokuchi-cho, Kagoshima City, Kagoshima, 892-0845, Japan.
Abstract
BACKGROUND: To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women. METHODS: The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions. RESULTS: The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11; P < 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively. CONCLUSIONS: Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.
BACKGROUND: To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women. METHODS: The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions. RESULTS: The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11; P < 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively. CONCLUSIONS: Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.
Entities:
Keywords:
An interactive decision support score and an examination-based cancer likelihood score; Artificial intelligence (AI); Breast cancer; Digital mammograms
Authors: Anna W Anderson; M Luke Marinovich; Nehmat Houssami; Kathryn P Lowry; Joann G Elmore; Diana S M Buist; Solveig Hofvind; Christoph I Lee Journal: J Am Coll Radiol Date: 2022-01-20 Impact factor: 5.532
Authors: Jesus A Basurto-Hurtado; Irving A Cruz-Albarran; Manuel Toledano-Ayala; Mario Alberto Ibarra-Manzano; Luis A Morales-Hernandez; Carlos A Perez-Ramirez Journal: Cancers (Basel) Date: 2022-07-15 Impact factor: 6.575