Orit Kaidar-Person, MD
Project Title
BRILLIANT study: BReast mri-based artificial InteLLIgence to identify high risk areas in residual breast tissue after mAstectomy and reconstruction
About the Investigator:
Dr. Orit Kaidar-Person began her medical studies at the Technion, Haifa, Israel as part of the Israeli
Defense Force (IDF) medical reserve for excellent high school students. She served as a doctor in the
IDF and received two excellence awards for her service. Thereafter, she trained in radiation oncology
at Rambam Medical Center in Haifa and received “the best oncology resident” award by the Israeli
Society for Clinical Oncology and Radiation Therapy (ISCORT), sponsored by The Israeli Cancer
Association. She is involved in the EORTC, founded, and chaired the EORTC-YROG. After completing
residency, she trained as a fellow at the radiotherapy department of UNC, Chapel Hill, NC, USA. Upon
her return to Israel, she resumed work at Rambam and participated in ECI Fellowship program at the
EORTC-H.Q. In 2019, she began PhD studies as a foreign student at the GROW School, Maastricht
University, NL. Her PhD thesis focuses on improving the outcomes after mastectomy. She is a member
of the core educational faculty of FALCON -ESTRO, the Breast ca faculty of ESTRO, and the editorial
board of Radiotherapy and Oncology. In 2019, she was appointed as a senior lecturer at the Technion.
Later that year, she moved to her current position at Sheba Medical Center as the Head of the breast
radiation unit and appointed as a senior lecturer by Tel Aviv University. She is currently involved in
several trials, including the CINDERELLA trial (NCT05196269, Project: 101057389 — CINDERELLA —
HORIZON-HLTH-2021-DISEASE-04). She has published over 150 peer reviewed papers and book
chapters, and co-edited two books, the most recent one about radiotherapy for breast cancer
(Springer). Her PhD work has led to the current AI project with Dr. Anaby and Prof. Sklair-Levy from
Sheba. Orit is married and a mother of three, she loves sport activities, and defines her perspective of
life as “long distance running”: stay patient and focused, make the best out of every challenge, and
keep on going endlessly.
About the Research:
Breast cancer is the most common cancer in women. In some cases, breast cancer patients undergo
removal of the breast (this type of surgery is called mastectomy), followed by radiotherapy (i.e.,
radiation) to eradicate any potential residual disease that cannot be detected on imaging (the spatial
resolution of imaging is limited to tumor foci of several mm in diameter, and can thereby not
demonstrate a small amount of tumor cells). Significant improvements in surgical techniques allow
now to reconstruct the breast (e.g., using silicone implants) at the same time of cancer surgery,
immediately after removal of the breast with the tumor in it. This combined breast removal and
reconstruction surgery has been shown to improve the patients’ quality of life and wellbeing by
waking up after cancer surgery with a “new breast”. However, to create the “new breast” the
surgeons need to preserve the patient’s breast skin with/without the breast nipple. In our previous
work, we showed that this type of breast reconstruction leaves behind various amounts of breast
tissue, which is partly associated to the expertise of the surgeon. These areas of residual breast tissue
may harbor residual tumor cells or be a source of a new breast cancer, leading to tumor recurrence in
the “new breast”. This event is devastating to the patients that had a mastectomy, not only because it
might lead to new cancer spread but also because it means that they will require additional surgery to
remove the “new breast”, often followed by additional anti-cancer therapy including hormonal
therapy, chemotherapy and/or radiation. In this work, we aim to develop imaging-based (Breast MRI)
artificial intelligence models, using the largest database in Israel from the Meirav Center at Sheba Tel
Hashomer. This model will assist to the identification of areas at high risk for cancer recurrence after
mastectomy and implant-based reconstruction.
This MRI-based model will highlight on the patient’s breast images which areas within the
reconstructed breast are at risk for cancer recurrence. This can thereby guide the selection and the
extent of further additional treatment such as surgery or radiotherapy to decrease the risks for cancer
recurrence. Our new technology will hereby improve outcomes of thousands of mastectomy breast
cancer patients worldwide by better identification of recurrence risks and improved selection for
subsequent treatments.
