Hamyoon, Hessam and Chan, Wai Yee and Mohammadi, Afshin and Kuzan, Taha Yusuf and Mirza-Aghazadeh-Attari, Mohammad and Leong, Wai Ling and Altintoprak, Kuebra Murzoglu and Vijayananthan, Anushya and Rahmat, Kartini and Ab Mumin, Nazimah and Leong, Sook Sam and Ejtehadifar, Sajjad and Faeghi, Fariborz and Abolghasemi, Jamileh and Ciaccio, Edward J. and Acharya, U. Rajendra and Ardakani, Ali Abbasian (2022) Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts. EUROPEAN JOURNAL OF RADIOLOGY, 157. ISSN 1872-7727, DOI https://doi.org/10.1016/j.ejrad.2022.110591.
Full text not available from this repository.Abstract
Purpose: To develop and validate a machine learning (ML) model for the classification of breast lesions on ul-trasound images.Method: In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data. An expert radiologist and a radiology resident classified the lesions based on the BI-RADS lexicon. Thirteen morphometric features were selected from a contour of the lesion and underwent a three-step feature selection process. Five features were chosen to be fed into the model separately and combined with the imaging signs mentioned in the BI-RADS reference guide. A support vector classifier was trained and optimized.Results: The diagnostic profile of the model with various input data was compared to the expert radiologist and radiology resident. The agreement of each approach with histopathologic specimens was also determined. Based on BI-RADS and morphometric features, the model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.885, which is higher than the expert radiologist and radiology resident performances with AUC of 0.814 and 0.632, respectively in all cohorts. DeLong's test also showed that the AUC of the ML protocol was significantly different from that of the expert radiologist (Delta AUCs = 0.071, 95%CI: (0.056, 0.086), P = 0.005). Conclusions: These results support the possible role of morphometric features in enhancing the already well -excepted classification schemes.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Malaysia [FRGS/1/2019 SKK03/UM/01/1] |
| Uncontrolled Keywords: | Artificial intelligence; BI-RADS; Breast cancer; Machine learning; Ultrasound |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Divisions: | Faculty of Medicine > Biomedical Imaging Department |
| Depositing User: | Ms Koh Ai Peng |
| Date Deposited: | 25 Oct 2024 08:03 |
| Last Modified: | 25 Oct 2024 08:03 |
| URI: | http://eprints.um.edu.my/id/eprint/46176 |
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