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Distinction between phyllodes tumor and fibroadenoma in breast ultrasound using deep learning image analysis


Stoffel, Elina; Becker, Anton S; Wurnig, Moritz C; Marcon, Magda; Ghafoor, Soleen; Berger, Nicole; Boss, Andreas (2018). Distinction between phyllodes tumor and fibroadenoma in breast ultrasound using deep learning image analysis. European Journal of Radiology Open, 5:165-170.

Abstract

Purpose To evaluate the accuracy of a deep learning software (DLS) in the discrimination between phyllodes tumors (PT) and fibroadenomas (FA). Methods In this IRB-approved, retrospective, single-center study, we collected all ultrasound images of histologically secured PT (n = 11, 36 images) and a random control group with FA (n = 15, 50 images). The images were analyzed with a DLS designed for industrial grade image analysis, with 33 images withheld from training for validation purposes. The lesions were also interpreted by four radiologists. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, negative and positive predictive values were calculated at the optimal cut-off (Youden Index). Results The DLS was able to differentiate between PT and FA with good diagnostic accuracy (AUC = 0.73) and high negative predictive value (NPV = 100%). Radiologists showed comparable accuracy (AUC 0.60-0.77) at lower NPV (64-80%). When performing the readout together with the DLS recommendation, the radiologist's accuracy showed a non-significant tendency to improve (AUC 0.75-0.87, p = 0.07). Conclusion Deep learning based image analysis may be able to exclude PT with a high negative predictive value. Integration into the clinical workflow may enable radiologists to more confidently exclude PT, thereby reducing the number of unnecessary biopsies.

Abstract

Purpose To evaluate the accuracy of a deep learning software (DLS) in the discrimination between phyllodes tumors (PT) and fibroadenomas (FA). Methods In this IRB-approved, retrospective, single-center study, we collected all ultrasound images of histologically secured PT (n = 11, 36 images) and a random control group with FA (n = 15, 50 images). The images were analyzed with a DLS designed for industrial grade image analysis, with 33 images withheld from training for validation purposes. The lesions were also interpreted by four radiologists. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, negative and positive predictive values were calculated at the optimal cut-off (Youden Index). Results The DLS was able to differentiate between PT and FA with good diagnostic accuracy (AUC = 0.73) and high negative predictive value (NPV = 100%). Radiologists showed comparable accuracy (AUC 0.60-0.77) at lower NPV (64-80%). When performing the readout together with the DLS recommendation, the radiologist's accuracy showed a non-significant tendency to improve (AUC 0.75-0.87, p = 0.07). Conclusion Deep learning based image analysis may be able to exclude PT with a high negative predictive value. Integration into the clinical workflow may enable radiologists to more confidently exclude PT, thereby reducing the number of unnecessary biopsies.

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Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Diagnostic and Interventional Radiology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Radiology, Nuclear Medicine and Imaging
Language:English
Date:2018
Deposited On:01 Nov 2018 06:25
Last Modified:15 Apr 2020 21:42
Publisher:Elsevier
ISSN:2352-0477
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.ejro.2018.09.002
PubMed ID:30258856

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