J C M van Zelst1, T Tan2, B Platel2, M de Jong3, A Steenbakkers2, M Mourits3, A Grivegnee4, C Borelli5, N Karssemeijer2, R M Mann2. 1. Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands. Electronic address: Jan.vanZelst@radboudumc.nl. 2. Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands. 3. Jeroen Bosch Medical Centre, Department of Radiology, 's-Hertogenbosch, The Netherlands. 4. Jules Bordet Institute, Department of Radiology, Brussels, Belgium. 5. Catholic University of the Sacred Heart, Department of Radiological Sciences, Rome, Italy.
Abstract
OBJECTIVE: To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. METHODS: 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n=40) with >1year of follow up, benign (n=30) lesions that were either biopsied or remained stable, and malignant lesions (n=20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. RESULTS: Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p=0.001). Sensitivity of all readers improved (range 5.2-10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4-5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. CONCLUSIONS: Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer.
OBJECTIVE: To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. METHODS: 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n=40) with >1year of follow up, benign (n=30) lesions that were either biopsied or remained stable, and malignant lesions (n=20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. RESULTS: Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p=0.001). Sensitivity of all readers improved (range 5.2-10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4-5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. CONCLUSIONS: Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer.
Authors: Jan C M van Zelst; Tao Tan; Paola Clauser; Angels Domingo; Monique D Dorrius; Daniel Drieling; Michael Golatta; Francisca Gras; Mathijn de Jong; Ruud Pijnappel; Matthieu J C M Rutten; Nico Karssemeijer; Ritse M Mann Journal: Eur Radiol Date: 2018-02-07 Impact factor: 5.315
Authors: Alejandra Cruz-Bernal; Martha M Flores-Barranco; Dora L Almanza-Ojeda; Sergio Ledesma; Mario A Ibarra-Manzano Journal: J Healthc Eng Date: 2018-12-30 Impact factor: 2.682
Authors: Magda Marcon; Alexander Ciritsis; Cristina Rossi; Anton S Becker; Nicole Berger; Moritz C Wurnig; Matthias W Wagner; Thomas Frauenfelder; Andreas Boss Journal: Eur Radiol Exp Date: 2019-11-01