147P Racial bias in pretreatment MRI radiomics features to predict response to neoadjuvant systemic treatment in breast cancer: A multicenter study in China, Germany, and the US Article Swipe
André Pfob
,
Jia Liu
,
Christopher J. Sidey-Gibbons
,
Juliane Nees
,
Fabian Riedel
,
Benedikt Schaefgen
,
Riku Togawa
,
Junpeng Huang
,
Jörg Heil
,
B. Gao
,
Michael Golatta
,
Li Cai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.esmoop.2024.103134
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.esmoop.2024.103134
Machine learning with radiomics showed great potential to predict response to neoadjuvant systemic treatment (NAST) for breast cancer. However, performance across different ethnicities and potential racial bias remain unclear. We aimed to develop an intelligent algorithm using pretreatment MRI radiomics in addition to clinical variables, to validate their performance in ethnically diverse populations. We used institutional data of patients who underwent MRI before NAST. We developed a support vector machine algorithm based on German patients using pretreatment MRI radiomics features in addition to patient and tumor variables to predict pCR status (ypT0 and ypN0). We used N4 bias field correction to maintain the consistency of images acquired by different types of machines and settings. Model performance was validated on an American and Chinese dataset. Findings were compared to the histopathologic evaluation of the surgical specimen. The main outcome measure was the area under the curve (AUC). We included 656 patients in the German development set, 88.6% (581 of 656) were white people and 34.8% (228 of 656) achieved pCR. The model showed good performance in the development set (AUC: 0.81, 95%CI, 0.71-0.83). Validation in the American population sample (n = 100, 80% white people) showed non-inferior performance compared to the development (AUC: 0.75 vs. 0.81, p = 0.543), but performance in the Chinese population sample (n = 100, 0 white people) decreased (AUC: 0.61 vs. 0.81, p = 0.004). Also within the development set, training performance was descriptively lower for the patients with Asian (n= 59, AUC 0.73 (95% CI 0.56-0.88)), or African (n=16, AUC 0.71 (95% CI 0.44-0.95)) ethnicity. Racial bias exists in radiomics studies and should be assessed before the global application of AI-based imaging algorithms. Ethnic diversity is crucial to mitigate racial bias when developing such algorithms.
Related Topics
Concepts
Radiomics
Breast cancer
Medicine
China
Oncology
Systemic therapy
Neoadjuvant therapy
Internal medicine
Cancer
Radiology
Political science
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.esmoop.2024.103134
- OA Status
- gold
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4397006853
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4397006853Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.esmoop.2024.103134Digital Object Identifier
- Title
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147P Racial bias in pretreatment MRI radiomics features to predict response to neoadjuvant systemic treatment in breast cancer: A multicenter study in China, Germany, and the USWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-05-01Full publication date if available
- Authors
-
André Pfob, Jia Liu, Christopher J. Sidey-Gibbons, Juliane Nees, Fabian Riedel, Benedikt Schaefgen, Riku Togawa, Junpeng Huang, Jörg Heil, B. Gao, Michael Golatta, Li CaiList of authors in order
- Landing page
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https://doi.org/10.1016/j.esmoop.2024.103134Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.esmoop.2024.103134Direct OA link when available
- Concepts
-
Radiomics, Breast cancer, Medicine, China, Oncology, Systemic therapy, Neoadjuvant therapy, Internal medicine, Cancer, Radiology, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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