Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

By A. Rodríguez-Ruiz, Elizabeth A. Krupinski, J. Mordang, K. Schilling, S. Heywang-Köbrunner, I. Sechopoulos, R. Mann. Radiology, 2019

507 citations recorded by the source corpus.

Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements. Results On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time. Published under a CC BY 4.0 license. See also the editorial by Bahl in this issue.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3c151694087e7e8dd0dd555c81a3700c31b148e9

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In Artificial Intelligence

Citations in the corpus

The Effect of Image Resolution on Deep Learning in Radiography. (2020, background): “…applications has investigated whether artificial intelligence can augment a radiologist’s performance for breast cancer detection in mammography (14) data in a time sensitive manner, having marginally reduced CNN performance with a multilabel model compared with a large number of binary…”
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (2019, background): “[35]–[37], some evaluate their models on very small public datasets, InBreast [38] or DDSM [39], which are insufficient for a meaningful evaluation, while others used private datasets with populations of different distributions (on a spectrum between screening population and biopsied subpopulation),”
Artificial intelligence (AI) and big data in cancer and precision oncology (2020, background): “Additionally, AI systems were able to detect cancers regardless of breast density [44].”