Abstract
Purpose
To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category 4 (BI-RADS 4) lesions and to evaluate whether the combined diagnosis of these models could improve the diagnostic performance of radiologists.
Methods
A total of 319 female patients with 319 pathologically confirmed BI-RADS 4 lesions were randomly divided into training, validation, and testing sets in this retrospective study. The three models were established based on contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and T2-weighted imaging using the training and validation sets. The artificial intelligence (AI) combination score was calculated according to the results of three models. The diagnostic performances of four radiologists with and without AI assistance were compared with the AI combination score on the testing set. The area under the curve (AUC), sensitivity, specificity, accuracy, and weighted kappa value were calculated to assess the performance.
Results
The AI combination score yielded an excellent performance (AUC = 0.944) on the testing set. With AI assistance, the AUC for the diagnosis of junior radiologist 1 (JR1) increased from 0.833 to 0.885, and that for JR2 increased from 0.823 to 0.876. The AUCs of senior radiologist 1 (SR1) and SR2 slightly increased from 0.901 and 0.950 to 0.925 and 0.975 after AI assistance, respectively.
Conclusion
Combined diagnosis of multiparametric MRI-based deep learning models to differentiate TNBC from fibroadenoma magnetic resonance BI-RADS 4 lesions can achieve comparable performance to that of SRs and improve the diagnostic performance of JRs.
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Data availability
Data are available in the article. All other data can be provided upon reasonable request to the corresponding authors.
Code availability
The code of the proposed method can be provided upon reasonable request to the corresponding authors.
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This work was supported by the National Natural Science Foundation of China (grant no. 81771816).
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G-wL: Supervision, Writing, Reviewing and Editing, Conceptualization. Z-hX and H-hJ: Data curation. H-lY: Writing-Original draft preparation, Methodology. YJ: Formal analysis. All authors contributed to the article and approved the submitted version.
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This retrospective study was approved by The Institutional Review Board of Huadong Hospital, and the requirement for written informed consent was waived by The Institutional Review Board of Huadong Hospital.
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Yin, Hl., Jiang, Y., Xu, Z. et al. Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions. J Cancer Res Clin Oncol 149, 2575–2584 (2023). https://doi.org/10.1007/s00432-022-04142-7
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DOI: https://doi.org/10.1007/s00432-022-04142-7