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The Establishment of Transformer-Based Computer-Aided Diagnosis Model to Improve the Classification Consistency of BI-RADS-US3–5 Nodules Among Radiologists: A Multi-Center Study [post]

Hongtao Ji, Qiang Zhu, Teng Ma, Yun Cheng, Shuai Zhou, Wei Ren, Huilian Huang, Wen He, Haitao Ran, Litao Ruan, Yanli Guo, Jiawei Tian (+14 others)
2021 unpublished

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Background: Significant differences exist in classification outcomes for radiologists using ultrasonography-based breast imaging-reporting and data systems for diagnosing category 3–5 (BI-RADS-US 3–5) breast nodules, due to a lack of clear and distinguishing image features. As such, this study investigates the use of a transformer-based computer-aided diagnosis (CAD) model for improved BI-RADS-US 3–5 classification consistency.Methods: Five radiologists independently performed BI-RADS-US
more » ... ions on a breast ultrasonography image set collected from 20 hospitals in China. The data were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity, specificity, and accuracy were calculated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS-US classification results for the sampling test set, provided by CAD, to determine whether classification consistency (the kappa value),sensitivity, specificity, and accuracy had improved.Results: Classification accuracy for the CAD model applied to the test set was 95.7% for category 3 nodules, 97.6% for category 4A nodules, 95.60% for category 4B nodules, 94.2% for category 4C nodules, and 97.5% for category 5 nodules. Adjustments were made to 1,583 nodules, as 905 were classified to a higher category and 678 to a lower category in the sampling test set. As a result, the accuracy, sensitivity, and specificity of classification by each radiologist improved, with the consistency (kappa values) for all radiologists increasing to >0.60.Conclusions: The proposed transformer-based CAD model improved BI-RADS-US 3–5 nodule classification by individual radiologists and increased diagnostic consistency.
doi:10.21203/rs.3.rs-1179922/v1 fatcat:7nrgbvpuozftjdb74mz536px44
Open Access
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Hongtao Ji, Qiang Zhu, Teng Ma, Yun Cheng, Shuai Zhou, Wei Ren, Huilian Huang, Wen He, Haitao Ran, Litao Ruan, Yanli Guo, Jiawei Tian, Wu Chen, Luzeng Chen, Zhiyuan Wang, Qi Zhou, Lijuan Niu, Wei Zhang, Ruimin Yang, Qin Chen, Ruifang Zhang, Hui Wang, Li Li, Minghui Liu, Fang Nie, Aiyun Zhou. "The Establishment of Transformer-Based Computer-Aided Diagnosis Model to Improve the Classification Consistency of BI-RADS-US3–5 Nodules Among Radiologists: A Multi-Center Study." (2021)