Abstract
A hysteresis loop analysis (HLA) method for breast cancer diagnosis based on a non-invasive digital imaging elasto-tomography (DIET) screening system is evaluated using data from 3 clinical trial patients, comprising 2 healthy breasts and 4 breasts with cancer. The identified mechanical nominal stiffness with ∼2x higher values compared to healthy tissue stiffness localized the correct cancerous area (CA) or tumor location, matching the mammography detection for all 4 breasts with cancer. The difference in identified stiffness varies across different frequencies and individuals. However, the identified stiffness for all healthy breasts and/or health tissue regions are consistent across frequencies, avoiding false positive diagnosis. The overall approach can be implemented automatically without requiring a skilled operator, thus reducing the screening cost.
Zusammenfassung
Eine Hysterese-Loop-Analyse (HLA) -Methode zur Brustkrebsdiagnose basierend auf einem nicht-invasiven digitalen Bildgebungs-Elastom-Tomographie (DIET) -Screeningsystem wird unter Verwendung von Daten von 3 klinischen Versuchspatienten ausgewertet, die 2 gesunde Brüste und 4 Brüste mit Krebs umfassen. Die identifizierte mechanische Nominalsteifigkeit mit ∼ 2x höheren Werten im Vergleich zur gesunden Gewebesteifigkeit lokalisierte den korrekten Tumorbereich (CA) oder den Tumorort und stimmte mit der Mammographieerkennung für alle 4 Brüste mit Krebs überein. Der Unterschied in der identifizierten Steifigkeit variiert über verschiedene Frequenzen und Individuen. Die identifizierte Steifigkeit für alle gesunden Brust- und / oder Gesundheitsgewebsregionen ist jedoch über die Frequenzen hinweg konsistent, wodurch eine falsch positive Diagnose vermieden wird. Der Gesamtansatz kann automatisch implementiert werden, ohne dass ein erfahrener Bediener erforderlich ist, wodurch die Kosten für das Screening verringert werden.
Funding statement: This work was supported by a Postdoctoral Fellowship grant from NZ National Science Challenge, NZ MedTech Core, RSNZ, and by Callaghan Innovation.
About the authors
Dr Cong Zhou received his PhD degree from University of Canterbury in 2016. Dr Zhou is currently a post-doctoral research fellow working in Mechanical Engineering and Centre for Bioengineering at University of Canterbury. His main research fields include: Structural Dynamics, Structural Health Monitoring (SHM), Signal Processing, Biomedical System Modelling and Machine Learning Application.
Brent Hainsworth received his BE(hons) in mechanical engineering from the University of Canterbury in 2017. He is currently involved in an Industry 4.0 project for Christchurch Engine Centre as part his master’s degree in engineering management.
Max Sydney is a graduate of the University of Canterbury, where he completed his B.E in Mechanical Engineering in 2018. He is currently working in industry as a Research Engineer with Trimble Navigation, where he specialises in sensor systems and instrumentation.
Michael Lee is a Master of Engineering in Management student at the University of Canterbury. Before that he received a BSc in Physiology from the University of Otago and a BE(Hons) in Mechanical Engineering from the University of Canterbury. His background in Physiology led to his interest in Biomedical Engineering.
Zane Ormsby, BEng Hons, received a honours degree in Engineering from the University of Canterbury in 2015, specialising in Mechatronics Engineering.
Marcus Haggers, MEng Hons Oxon, received a masters degree in Engineering from the University of Oxford in 2004, specialising in Biomedical Engineering. Subsequently he gained a further masters degree from the University in Manchester in 2007.
Professor Chase received his B.S. from Case Western Reserve University in 1986 in Mechanical Engineering. His M.S. and PhD were obtained at Stanford University in 1991 and 1996. He spent 6 years working for General Motors and a further 5 years in Silicon Valley, including Xerox PARC, ReSound, Hughes Space and Communications, and Infineon Technologies, before the University of Canterbury in 2000. His research focuses on the intersection of engineering and clinical medicine, primarily in intensive care. Dr. Chase has published over 1200 journal and conference papers and 15 US and European patents. He founded 3 startup companies, and is a Fellow of the Royal Society of NZ (FRSNZ), the American Society of Mechanical Engineers (FASME) and IPENZ (FIPENZ).
Acknowledgment
The authors thank and acknowledge Canterbury Breast Care and the patients who volunteered for this trial.
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