ArticleEuropean radiology2022
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 in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02638935 (Evaluation of Virtual Touch Tissue Imaging Quantification), which is not on this map. Cited by 12 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Evaluation of Virtual Touch Tissue Imaging Quantification (VTIQ - 2D-SWE) in the Assessment of BI-RADS® 3 and 4 Lesions: Can Patient Selection for Biopsy be Improved? - A Confirmatory Multi-Center-Study
Who cites it
12 citing papers in PubMed.
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.Breast cancer research and treatment · 2025Article
- Article
- Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.IEEE journal of biomedical and health informatics · 2025Article
- Development and validation of an intratumoral-peritumoral deep transfer learning fusion model for differentiating BI-RADS 3-4 breast nodules.Gland surgery · 2025Article
- The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer.Breast cancer (Dove Medical Press) · 2025Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification.Scientific reports · 2024Article
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
- Ultrasound-based multiregional radiomics analysis to differentiate breast masses.Quantitative imaging in medicine and surgery · 2023Article
- Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison.BMC medical research methodology · 2022Article
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29 authors.
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Abstract
objectivesAI-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.
methodsPatients 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).
resultsOf 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).
conclusionsThe 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.
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