The Value of Magnetic Resonance Imaging Peritumoral Radiomics in Predicting 21-gene Recurrence Score of ER+/HER2- Breast Cancer

Motivation: 21-gene assay is recommend to guide decision on the use of adjuvant chemotherapy. However, this test is expensive and time-consuming, so it is not widely used in clinic. Goal(s): To develop a radiomics model for predicting 21-gene recurrence score based on MRI intratumoral and peritumoral features. Approach: Prediction models in tumor and different peritumoral regions (2 mm, 4 mm, 6 mm, 8 mm, 10 mm) were established using machine learning method. Feature-fusion and logistics-regression methods were used to fuse information. Results: Combining 4-mm peritumoral model on T2WI (AUC = 0.66) with intratumoral and clinical-imaging features, the fusion model performed best (AUC = 0.75). Impact: To provide an alternative for patients who cannot afford Oncotype 21-gene assay and to reduce the medical costs for those who could afford it.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/3617
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

The Value of Magnetic Resonance Imaging Peritumoral Radiomics in Predicting 21-gene Recurrence Score of ER+/HER2- Breast Cancer

Weijun Peng, Yang Chen, Lizhi Xie
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

The Value of Magnetic Resonance Imaging Peritumoral Radiomics in Predicting 21-gene Recurrence Score of ER+/HER2- Breast Cancer

Weijun Peng, Yang Chen, Lizhi Xie
article en

Abstract

Motivation: 21-gene assay is recommend to guide decision on the use of adjuvant chemotherapy. However, this test is expensive and time-consuming, so it is not widely used in clinic. Goal(s): To develop a radiomics model for predicting 21-gene recurrence score based on MRI intratumoral and peritumoral features. Approach: Prediction models in tumor and different peritumoral regions (2 mm, 4 mm, 6 mm, 8 mm, 10 mm) were established using machine learning method. Feature-fusion and logistics-regression methods were used to fuse information. Results: Combining 4-mm peritumoral model on T2WI (AUC = 0.66) with intratumoral and clinical-imaging features, the fusion model performed best (AUC = 0.75). Impact: To provide an alternative for patients who cannot afford Oncotype 21-gene assay and to reduce the medical costs for those who could afford it.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Good health and well-being
Openalex Percentile: Top 62%
Radiomics and Machine Learning in Medical Imaging
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