Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience

Ji-Hye Choi, Bong Joo Kang, Ji Eun Baek, Hyun Sil Lee, Sung Hun Kim, Ji-Hye Choi, Bong Joo Kang, Ji Eun Baek, Hyun Sil Lee, Sung Hun Kim

Abstract

Purpose: The purpose of this study was to evaluate the usefulness of applying computer-aided diagnosis (CAD) to breast ultrasound (US), depending on the operator's experience with breast imaging.

Methods: Between October 2015 and January 2016, two experienced readers obtained and analyzed the grayscale US images of 200 cases according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. They additionally applied CAD (S-Detect) to analyze the lesions and made a diagnostic decision subjectively, based on grayscale US with CAD. For the same cases, two inexperienced readers analyzed the grayscale US images using the BI-RADS lexicon and categories, added CAD, and came to a subjective diagnostic conclusion. We then compared the diagnostic performance depending on the operator's experience with breast imaging.

Results: The sensitivity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 91.7%, 75%, 75%, and 66.7%, respectively. The specificity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 76.6%, 71.8%, 78.2%, and 76.1%, respectively. When diagnoses were made subjectively in combination with CAD, the specificity significantly improved (76.6% to 80.3%) without a change in the sensitivity (91.7%) in the experienced readers. After subjective combination with CAD, the sensitivity and specificity improved in the inexperienced readers (75% to 83.3% and 71.8% to 77.1%). In addition, the area under the curve improved for both the experienced and inexperienced readers (0.84 to 0.86 and 0.73 to 0.8) after the addition of CAD.

Conclusion: CAD is more useful for less experienced readers. Combining CAD with breast US led to improved specificity for both experienced and inexperienced readers.

Keywords: Breast neoplasms; Diagnosis, computer-assisted; Ultrasonography.

Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.. Flow chart and representative images…
Fig. 1.. Flow chart and representative images for this study.
A. This flow chart shows the analysis process of this study. B. This figure presents the classification and final assessment of the grayscale ultrasound (US) images. The descriptions in this figure were made by an experienced reader. C, D. These figures depict how a breast lesion was classified automatically by the S-Detect program and a final assessment was produced. Like the grayscale US, the representative image was analyzed after two or more regions of interest were indicated via the touchscreen. CAD, computer-aided diagnosis.
Fig. 2.. A 39-year-old woman who underwent…
Fig. 2.. A 39-year-old woman who underwent a breast ultrasound for screening.
A. The grayscale ultrasound image analyzed by an experienced reader shows an oval, microlobulated, hypoechoic mass at 9 o’clock in the right breast. The experienced and inexperienced readers concluded that the lesion was BI-RADS category 4A, with a low suspicion for malignancy. B. CAD (S-Detect) by an experienced reader reveals the same lesion, and the conclusion was “possibly benign.” An ultrasoundguided biopsy was performed, and this lesion was pathologically confirmed to be a fibrocystic change. BI-RADS, Breast Imaging Reporting and Data System; CAD, computer-aided diagnosis.
Fig. 3.. A 54-year-old woman with a…
Fig. 3.. A 54-year-old woman with a palpable lesion.
A. The grayscale ultrasound image analyzed by an inexperienced reader shows an oval, microlobulated, isoechoic lesion at 3 o’clock in the right breast. An inexperienced reader concluded that the lesion was BI-RADS category 3, a probable benign finding. An experienced reader concluded that the lesion was BI-RADS category 4A, with a low suspicion for malignancy. B. CAD (S-Detect) by an inexperienced reader reveals the same lesion, and the conclusion was “possibly malignant.” Based on the CAD findings, the inexperienced reader subjectively chose the CAD (S-Detect) result. An ultrasound-guided biopsy was performed, and this lesion was pathologically confirmed to be mucinous carcinoma. BI-RADS, Breast Imaging Reporting and Data System; CAD, computer-aided diagnosis.

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