Share Email Print
cover

Proceedings Paper

Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support?
Author(s): Alejandro Rodriguez-Ruiz; Jan-Jurre Mordang; Nico Karssemeijer; Ioannis Sechopoulos; Ritse M. Mann
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

For more than a decade, radiologists have used traditional computer aided detection systems to read mammograms, but mainly because of a low computer specificity may not improve their screening performance, according to several studies. The breakthrough in deep learning techniques has boosted the performance of machine learning algorithms, also for breast cancer detection in mammography. The objective of this study was to determine whether radiologists improve their breast cancer detection performance when they concurrently use a deep learningbased computer system for decision support, compared to when they read mammography unaided. A retrospective, fully-crossed, multi-reader multi-case (MRMC) study was designed to compare this. The employed decision support system was Transpara™ (Screenpoint Medical, Nijmegen, the Netherlands). Radiologists interact by clicking an area on the mammogram, for which the computer system displays its cancer likelihood score (1-100). In total, 240 cases (100 cancers, 40 false positive recalls, 100 normals) acquired with two different mammography systems were retrospectively collected. Seven radiologists scored each case once with, and once without the use of decision support, providing a forced BI-RADS® score and a level of suspiciousness (1-100). MRMC analysis of variance of the area under the receiver operating characteristic curves (AUC), and specificity and sensitivity were computed. When using decision support, the AUC increased from 0.87 to 0.89 (P=0.043) and specificity increased from 73% to 78% (P=0.030), while sensitivity did not significantly increment (84% to 87%, P=0.180). In conclusion, radiologists significantly improved their performance when using a deep learningbased computer system as decision support.

Paper Details

Date Published: 6 July 2018
PDF: 10 pages
Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 1071803 (6 July 2018); doi: 10.1117/12.2317937
Show Author Affiliations
Alejandro Rodriguez-Ruiz, Radboud Univ. Medical Ctr. (Netherlands)
Jan-Jurre Mordang, Screenpoint Medical BV (Netherlands)
Nico Karssemeijer, Radboud Univ. Medical Ctr. (Netherlands)
Screenpoint Medical BV (Netherlands)
Ioannis Sechopoulos, Radboud Univ. Medical Ctr. (Netherlands)
Dutch Expert Ctr. for Screening (Netherlands)
Ritse M. Mann, Radboud Univ. Medical Ctr. (Netherlands)


Published in SPIE Proceedings Vol. 10718:
14th International Workshop on Breast Imaging (IWBI 2018)
Elizabeth A. Krupinski, Editor(s)

© SPIE. Terms of Use
Back to Top