Paper
24 March 2016 Quantra reproduces BI-RADS assessment on a two-point scale
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Abstract
PURPOSE: To assess the performance of QuantraTM in reproducing BI-RADS® mammographic breast density (MBD) assessment. METHODS: Two methods of MBD assessment were used (QuantraTM and BI-RADS®). Volumetric breast density measurement from 292 raw projection images was performed using QuantraTM. BI-RADS® assessment was performed by three radiologists and a majority report (consensus of at least two radiologists) was generated. Interreader agreement (κ), agreement, and the sensitivity and specificity of QuantraTM in reproducing BI-RADS® rating were calculated on a four-grade (1, 2, 3, and 4) and two-grade (1–2 vs. 3–4) scale. RESULTS: The majority BI-RADS® report in the dataset consisted of 9.6% (n = 28), 35.3% (n = 103), 27.1% (n = 79), and 28.1% (n = 82) for BI-RADS® 1, 2, 3, and 4 respectively. Intra-reader agreement (κ) was 0.86 (95%CI: 0.83 – 0.91) to 0.88 (95%CI: 0.85 – 0.93) on a four-grade and 0.88 (95%CI: 0.83 – 0.92) to 0.91 (95%CI: 0.88 – 0.95) on a two-grade scale. Inter-reader agreement (κ) was substantial [0.66 (95%CI: 0.62 – 0.71) to 0.75 (95%CI: 0.70 – 0.81)] on a four-grade scale and substantial to almost perfect [0.77 (95%CI: 0.73 – 0.82) to 0.89 (95%CI: 0.84 – 0.93)] on a two-grade scale. QuantraTM correctly classified 35.7%, 91.2%, 88.6%, and 50.3% of BI-RADS® 1, 2, 3, and 4 respectively. It also demonstrated 91.3% sensitivity and 83.6% specificity in reproducing BI-RADS® on a two-grade scale (1–2 vs. 3–4). CONCLUSION: QuantraTM has limited performance in reproducing BI-RADS® rating on a four-grade scale, however, highly reproduces BI-RADS® assessment on a two-grade scale.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ernest U. Ekpo, Claudia Mello-Thoms, Mary Rickard, Patrick C. Brennan, and Mark F. McEntee "Quantra reproduces BI-RADS assessment on a two-point scale", Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 978702 (24 March 2016); https://doi.org/10.1117/12.2205391
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KEYWORDS
Model-based design

Tissues

Breast cancer

Mammography

Cancer

Digital breast tomosynthesis

Magnetic resonance imaging

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