Best Practice Guideline – Empfehlungen der DEGUM zur Durchführung und Beurteilung der Mammasonografie

Vogel-Minea, Claudia Maria; Bader, W.; Blohmer, Jens-Uwe; Duda, Volker; Eichler, Christian; Fallenberg, Eva Maria; Farrokh, André; Golatta, Michael; Gruber, Ines; Hackelöer, Bernhard-Joachim; Heil, Jörg; Madjar, H.; Marzotko, Ellen; Merz, Eberhard; Müller-Schimpfle, Markus; Mundinger, A; Öhlinger, R.; Peisker, U.; Schäfer, Fritz K. W.; Schulz-Wendtland, R.; Solbach, Christine; Warm, Mathias; Watermann, Dirk; Wojcinski, Sebastian; Dudwiesus, Heiko; Hahn, Markus

doi:10.1055/a-2020-9904

Cited by 5 publications

(4 citation statements)

References 89 publications

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“…Together with the technical challenges of optical imaging technologies [34], the lack of standardized training programs for PAI has been recognized and is currently a focus of the clinical adoption theme of the International Photoacoustic Standardisation Consortium (http://www.ipasc.science) [35]. As compared to US [36], such efforts may lead to better implementation in clinical training and best practice recommendations.…”

Section: Discussionmentioning

confidence: 99%

Teachability of multispectral optoacoustic tomography

Li,

Gröhl,

Haney

et al. 2024

Journal of Biophotonics

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To date, the appropriate training required for the reproducible operation of multispectral optoacoustic tomography (MSOT) is poorly discussed. Therefore, the aim of this study was to assess the teachability of MSOT imaging. Five operators (two experienced and three inexperienced) performed repositioning imaging experiments. The inexperienced received the following introductions: personal supervision, video meeting, or printed introduction. The task was to image the exact same position on the calf muscle for seven times on five volunteers in two rounds of investigations. In the first session, operators used ultrasound guidance during measurements while using only photoacoustic data in the second session. The performance comparison was carried out with full‐reference image quality measures to quantitatively assess the difference between repeated scans. The study demonstrates that given a personal supervision and hybrid ultrasound real‐time imaging in MSOT measurements, inexperienced operators are able to achieve the same level as experienced operators in terms of repositioning accuracy.

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Section: Discussionmentioning

confidence: 99%

Teachability of multispectral optoacoustic tomography

Li,

Gröhl,

Haney

et al. 2024

Journal of Biophotonics

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“…In breast sonography, 3D imaging allows tumors to be displayed in coronal planes, which makes it possible to differentiate between benign and malignant findings in many cases [21,22]. Significant progress has also been made in the field of elastography in the female breast [23,24].…”

Section: Eberhard Merzmentioning

confidence: 99%

“…Bei der Mammasonografie gestattet die 3D-Diagnostik die Darstellung von Tumoren im Koronarschnitt, wodurch eine Differenzierung zwischen benignen und malignen Befunden in vielen Fällen möglich ist [21,22]. Deutliche Fortschritte konnten auch auf dem Gebiet der Elastografie bei der weiblichen Brust [23,24]erzielt werden.…”

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Merz

2023

Ultraschall Med

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“…Furthermore, assessing the complex vascular patterns and quantitative measurements of breast lesions with Doppler US can be clinically challenging. This complexity hinders the establishment of guidelines or recommendations for the optimal classification of vascularity patterns [9,11,12]. Artificial intelligence (AI) using deep learning-based neural networks has recently emerged to incorporate trainable data sets and potentially perform various imaging tasks in medical data [11] .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis

Eun,

Lee,

Park

et al. 2024

Ultraschall Med

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Purpose To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. Materials and Methods We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0–100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). Results The AUROC of the developed three AI algorithms (0.955–0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892–0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963–0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001). Conclusion AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.

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Best Practice Guideline – Empfehlungen der DEGUM zur Durchführung und Beurteilung der Mammasonografie

Cited by 5 publications

References 89 publications

Teachability of multispectral optoacoustic tomography

Teachability of multispectral optoacoustic tomography

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Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis

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