Project title: Advanced Robotics breasT ExaMination Intelligent System (ARTEMIS)

Funder: CRUK

Project summary:

Early detection has a significant impact on extending breast cancer survival and improving quality of life. Young women (35 years) with breast cancer account for 7% of all Breast cancers. Diagnostic delays are common and they present often at an advanced stage, with more aggressive and less responsive disease to hormonal therapy. They experience a poor outcome when compared to cancers of older patients. Screening mammography is not recommended due to the associated X-ray dose, with ultrasound being the first line examination, hampered by a high false positive rate. Hence, there is an unmet clinical need for early detection of breast cancer in young women, where 80% of all breast cancers present with a palpable lump. Equally, high-risk women (~6% of UK 40-73 year olds) may benefit from a safe, convenient, active surveillance of breast health programme for early cancer detection.

We propose a complementary approach for detecting the earliest stages of breast cancer in a paradigm shift combining a robotic device for smart sensing, imaging and palpation (SIP), with the possibility of collection and assay of nipple aspirate fluid (NAF) for breast health profiling. Our hypothesis is that multi-modal, timely breast examination will enable detection of the earliest perturbations in breast health and enable treatment with minimal impact on patients or even reverse disease state, thus reducing the need for mastectomy and burden on healthcare systems.

Project aims/objectives:

Our overarching goal is to increase accessibility to breast health monitoring for the wider female community through a validated, multi-modal robotic platform that enables frequency and capacity of breast examination to be increased. The specific objectives of this seed project are as follows:

• To explore end-user (doctor and patient) requirements through active clinician and patient involvement
• To understand the mechanics and characteristics of breast examination using a bespoke sensorised breast phantom model
• To develop innovative, patient-friendly robotic palpation approaches exploiting the latest advances in soft robotics
• To investigate suitable machine learning techniques for training the robotic palpation prototypes
• To study the feasibility, acceptability and suitability of NAF as an additional detection modality