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Insights into Imaging
Published in: Insights into Imaging 1/2023

Open Access 01-12-2023 | Breast MRI | Original Article

Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance

Authors: Kawtar Debbi, Paul Habert, Anaïs Grob, Anderson Loundou, Pascale Siles, Axel Bartoli, Alexis Jacquier

Published in: Insights into Imaging | Issue 1/2023

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Abstract

Background

Recent advanced in radiomics analysis could help to identify breast cancer among benign mammary masses. The aim was to create a radiomics signature using breast DCE-MRI extracted features to classify tumors and to compare the performances with the BI-RADS classification.

Material and methods

From September 2017 to December 2019 images, exams and records from consecutive patients with mammary masses on breast DCE-MRI and available histology from one center were retrospectively reviewed (79 patients, 97 masses). Exclusion criterion was malignant uncertainty. The tumors were split in a train-set (70%) and a test-set (30%). From 14 kinetics maps, 89 radiomics features were extracted, for a total of 1246 features per tumor. Feature selection was made using Boruta algorithm, to train a random forest algorithm on the train-set. BI-RADS classification was recorded from two radiologists.

Results

Seventy-seven patients were analyzed with 94 tumors, (71 malignant, 23 benign). Over 1246 features, 17 were selected from eight kinetic maps. On the test-set, the model reaches an AUC = 0.94 95 CI [0.85–1.00] and a specificity of 33% 95 CI [10–70]. There were 43/94 (46%) lesions BI-RADS4 (4a = 12/94 (13%); 4b = 9/94 (10%); and 4c = 22/94 (23%)). The BI-RADS score reached an AUC = 0.84 95 CI [0.73–0.95] and a specificity of 17% 95 CI [3–56]. There was no significant difference between the ROC curves for the model or the BI-RADS score (p = 0.19).

Conclusion

A radiomics signature from features extracted using breast DCE-MRI can reach an AUC of 0.94 on a test-set and could provide as good results as BI-RADS to classify mammary masses.
Appendix
Available only for authorised users
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Metadata
Title
Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance
Authors
Kawtar Debbi
Paul Habert
Anaïs Grob
Anderson Loundou
Pascale Siles
Axel Bartoli
Alexis Jacquier
Publication date
01-12-2023
Publisher
Springer Vienna
Keyword
Breast MRI
Published in
Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-023-01404-x

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