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.
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 diag...
| Publicado en: | European Radiology Vol. 32; no. 6; pp. 4101 - 4116 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157006583&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157006583 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2022 vid: 32 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157006583 157006583 NLM35175381 157006583 10.1007/s00330-021-08519-z NLM35175381 157006583 ppf: 4101 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 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. aug: au: Pfob, André Sidey-Gibbons, Chris Barr, Richard G. Duda, Volker Alwafai, Zaher Balleyguier, Corinne Clevert, Dirk-André Fastner, Sarah Gomez, Christina Goncalo, Manuela Gruber, Ines Hahn, Markus Hennigs, André Kapetas, Panagiotis Lu, Sheng-Chieh Nees, Juliane Ohlinger, Ralf Riedel, Fabian Rutten, Matthieu Schaefgen, Benedikt affil: University Breast Unit, Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany sug: subj: Breast Neoplasms Breast Neoplasms Pathology Artificial Intelligence Female Algorithms Breast Diagnostic Imaging Human Breast Pathology Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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