The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysis Article Swipe
André Pfob
,
Chris Sidey‐Gibbons
,
R. Graham Barr
,
Volker Duda
,
Zaher Alwafai
,
Corinne Balleyguier
,
Dirk‐André Clevert
,
Sarah Fastner
,
Christina Gomez
,
Manuela Gonçalo
,
Ines Gruber
,
Markus Hahn
,
André Hennigs
,
Panagiotis Kapetas
,
Sheng-Chieh Lu
,
Juliane Nees
,
Ralf Ohlinger
,
Fabian Riedel
,
Matthieu Rutten
,
Benedikt Schaefgen
,
Maximilian Schuessler
,
Anne Stieber
,
Riku Togawa
,
Mitsuhiro Tozaki
,
Sebastian Wojcinski
,
Cai Xu
,
Geraldine Rauch
,
Joerg Heil
,
Michael Golatta
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s00330-021-08519-z
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s00330-021-08519-z
Objectives AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms. Methods Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC). Results Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set ( n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05). Conclusions The performance of humans and AI-based algorithms improves with multi-modal information. Key Points • The performance of humans and AI-based algorithms improves with multi-modal information. • Multimodal AI-based algorithms do not necessarily outperform expert humans. • Unimodal AI-based algorithms do not represent optimal performance to classify breast masses.
Related Topics
Concepts
Ultrasound
Medicine
Neuroradiology
Breast ultrasound
Breast cancer
Breast imaging
Algorithm
Radiology
Medical diagnosis
Modalities
Machine learning
Medical physics
Artificial intelligence
Mammography
Cancer
Computer science
Internal medicine
Social science
Psychiatry
Neurology
Sociology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00330-021-08519-z
- https://link.springer.com/content/pdf/10.1007/s00330-021-08519-z.pdf
- OA Status
- hybrid
- Cited By
- 23
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213279130
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213279130Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s00330-021-08519-zDigital Object Identifier
- Title
-
The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-17Full publication date if available
- Authors
-
André Pfob, Chris Sidey‐Gibbons, R. Graham Barr, Volker Duda, Zaher Alwafai, Corinne Balleyguier, Dirk‐André Clevert, Sarah Fastner, Christina Gomez, Manuela Gonçalo, Ines Gruber, Markus Hahn, André Hennigs, Panagiotis Kapetas, Sheng-Chieh Lu, Juliane Nees, Ralf Ohlinger, Fabian Riedel, Matthieu Rutten, Benedikt Schaefgen, Maximilian Schuessler, Anne Stieber, Riku Togawa, Mitsuhiro Tozaki, Sebastian Wojcinski, Cai Xu, Geraldine Rauch, Joerg Heil, Michael GolattaList of authors in order
- Landing page
-
https://doi.org/10.1007/s00330-021-08519-zPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s00330-021-08519-z.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s00330-021-08519-z.pdfDirect OA link when available
- Concepts
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Ultrasound, Medicine, Neuroradiology, Breast ultrasound, Breast cancer, Breast imaging, Algorithm, Radiology, Medical diagnosis, Modalities, Machine learning, Medical physics, Artificial intelligence, Mammography, Cancer, Computer science, Internal medicine, Social science, Psychiatry, Neurology, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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23Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 9, 2023: 6, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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