The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study

Shuyi Lyu; Meiwu Zhang; Baisong Zhang; Libo Gao; Liu Yang; Susanna Guerrini; Eugene Ong; Yan Zhang

doi:10.21037/tcr-23-2122

The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study

Transl Cancer Res. 2024 Apr 30;13(4):1969-1979. doi: 10.21037/tcr-23-2122. Epub 2024 Apr 22.

Authors

Shuyi Lyu^{1

2}, Meiwu Zhang¹, Baisong Zhang¹, Libo Gao¹, Liu Yang¹, Susanna Guerrini³, Eugene Ong⁴, Yan Zhang^{1

2}

Affiliations

¹ Department of Ultrasound, Ningbo No. 2 Hospital, Ningbo, China.
² Department of Ultrasound, Zhenhai Hospital of Traditional Chinese Medicine, Ningbo, China.
³ Unit of Diagnostic Imaging, Department of Medical Sciences, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
⁴ Diagnostic Radiology, Mount Elizabeth Novena Hospital, Singapore, Singapore.

Abstract

Background: The consistency of Breast Imaging Reporting and Data System (BI-RADS) classification among experienced radiologists is different, which is difficult for inexperienced radiologists to master. This study aims to explore the value of computer-aided diagnosis (CAD) (AI-SONIC breast automatic detection system) in the BI-RADS training for residents.

Methods: A total of 12 residents who participated in the first year and the second year of standardized resident training in Ningbo No. 2 Hospital from May 2020 to May 2021 were randomly divided into 3 groups (Group 1, Group 2, Group 3) for BI-RADS training. They were asked to complete 2 tests and questionnaires at the beginning and end of the training. After the first test, the educational materials were given to the residents and reviewed during the breast imaging training month. Group 1 studied independently, Group 2 studied with CAD, and Group 3 was taught face-to-face by experts. The test scores and ultrasonographic descriptors of the residents were evaluated and compared with those of the radiology specialists. The trainees' confidence and recognition degree of CAD were investigated by questionnaire.

Results: There was no statistical significance in the scores of residents in the first test among the 3 groups (P=0.637). After training and learning, the scores of all 3 groups of residents were improved in the second test (P=0.006). Group 2 (52±7.30) and Group 3 (54±5.16) scored significantly higher than Group 1 (38±3.65). The consistency of ultrasonographic descriptors and final assessments between the residents and senior radiologists were improved (κ3 > κ2 > κ1), with κ2 and κ3 >0.4 (moderately consistent with experts), and κ1 =0.225 (fairly agreed with experts). The results of the questionnaire showed that the trainees had increased confidence in BI-RADS classification, especially Group 2 (1.5 to 3.5) and Group 3 (1.25 to 3.75). All trainees agreed that CAD was helpful for BI-RADS learning (Likert scale score: 4.75 out of 5) and were willing to use CAD as an aid (4.5, max. 5).

Conclusions: The AI-SONIC breast automatic detection system can help residents to quickly master BI-RADS, improve the consistency between residents and experts, and help to improve the confidence of residents in the classification of BI-RADS, which may have potential value in the BI-RADS training for radiology residents.

Trial registration: Chinese Clinical Trial Registry (ChiCTR2400081672).

Keywords: Breast Imaging Reporting and Data System (BI-RADS); Ultrasound (US); computer-aided diagnosis (CAD); resident; training.