Evaluation of the FUSION-X-US-II prototype to combine automated breast ultrasound and tomosynthesis

Schäfgen, B; Juskic, Marija; Radicke, Marcus; Hertel, Madeleine; Barr, Richard G.; Pfob, André; Togawa, Riku; Nees, Juliane; Au, Alexandra von; Fastner, Sarah; Harcos, Aba; Gomez, Christina; Stieber, Anne; Riedel, Fabian; Hennigs, André; Sohn, Christof; Heil, Joerg; Golatta, Michael

doi:10.1007/s00330-020-07573-3

Cited by 11 publications

(10 citation statements)

References 27 publications

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“…Further work is warranted to develop (more reliable) diagnostic models which combine imaging with comprehensive clinical and demographic data to fully represent the clinical reality (see commonly considered variables according to the National Comprehensive Cancer Network guideline for breast cancer screening [ 5 ]). Moreover, current research evaluates the feasibility of automated breast ultrasound and its combination with digital breast tomosynthesis which may further advance (automated) multi-modal breast image analysis in the future [ 30 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…Fusion-X-US is a new device under investigation, combining ABUS and tomosynthesis in one device, aiming to improve workflow in clinical practice. Preliminary results of a prospective study, analyzing 101 patients with this prototype, showed good breast coverage (80.0%) and diagnostic accuracy of ABUS (85.0%), as well as 97.1% lesion identification with the combined system [76].…”

Section: Future Perspectivesmentioning

confidence: 95%

Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

Allajbeu

Hickman

Payne

et al. 2021

Curr Breast Cancer Rep

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Purpose of Review Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives. Recent Findings ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics. Summary ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.

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“…In 18 out of 101 scans (17.8%), the image quality was described as being relatively similar to the HHUS, with the exception that no scans were ranked on a level of 5 (equal or higher quality than HHUS). Overall, the combined performance of the tomosynthesis and the ABUS resulted in a sensitivity rate of 97.1% and a specificity of 59.7% [23]. Image quality varied greatly depending on the individual breast shape.…”

Section: Comparison Of Dbt and Abusmentioning

confidence: 96%

“…ABUS had 4 false positive findings and the DBT had 15. According to the presented data, the ABUS showed a higher accuracy rate in comparison to the DBT ( Schäfgen et al, on the other hand, evaluated the performance of the FUSION-X-US-II prototype, which was developed to combine ABUS and DBT screening methods in a single device [23]. In 18 out of 101 scans (17.8%), the image quality was described as being relatively similar to the HHUS, with the exception that no scans were ranked on a level of 5 (equal or higher quality than HHUS).…”

Section: Comparison Of Dbt and Abusmentioning

confidence: 99%

Automated Breast Ultrasound

Žaja

Matijaš

2022

Radiol. vjesn. (Online)

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Due to the growing number of breast cancer patients, an early diagnosis is important in order to reduce the mortality rate of those affected. Methods such as mammography, DBT, MRI, HHUS or ABUS are used in the detection of breast cancer. The aim of this article is to review the literature showing the basic principle of ABUS and to point out its advantages and disadvantages in relation to conventional methods of breast imaging. ABUS is a relatively new ultrasound method that performs well on patients with dense breast tissue. It reduces operator dependence and provides valuable diagnostic information with multiplanar reconstructions. Using evidence from reliable researches, studies have demonstrated that ABUS has a higher diagnostic accuracy compared to mammography, which remains the primary modality for early diagnosis of breast cancer. Applying ABUS as an adjunct to mammography during the screening test has proven effective and further confirmed the importance of their application in clinical practice. The disadvantage of the combination of ABUS and mammography was that in a large number of studies the specificity was lower compared to mammography itself. Compared to DBT, ABUS has demonstrated to have a higher diagnostic performance, with the exception that it lacks the ability to effectively detect calcifications. Although MRI seem to outperform ABUS, ABUS devices offer a cost-effective and easy to use imaging system, making it the best alternative. The HHUS technique, on the other hand, was perceived by many studies as less painful, with a shorter operative time compared to ABUS. However, the sensitivity and specificity of this screening method continues to remain inferior to ABUS. The use of artificial intelligence is becoming widely used today. As a result, the CAD software has been developed to be applied in conjunction with ABUS in order to improve the detection rate of breast cancer as well as its accuracy. The use of CAD significantly reduced image reading time and improved the overall diagnostic accuracy of ABUS. According to all the presented data, the use of ABUS medical devices in clinical practice continues to grow in importance and with the further development of technology and medicine, its full integration into healthcare systems around the world is expected.

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Evaluation of the FUSION-X-US-II prototype to combine automated breast ultrasound and tomosynthesis

Cited by 11 publications

References 27 publications

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

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

Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

Automated Breast Ultrasound

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