Breast Pathology
Overview
Section Head: Fouad Boulos, MD
The Breast Pathology section has been home for breast pathology luminaries such as Dr. Robert W. McDivitt, co-author of the second edition of the AFIP “Tumors of the Breast” fascicle; Dr. Craig Allred, who established the scoring system for estrogen and progesterone receptors; and Dr. Horacio Maluf, who was first to describe solid papillary carcinoma in the breast and its cutaneous counterpart endocrine mucin-producing sweat gland carcinoma. Dr. Fouad Boulos, an expert in breast and soft tissue pathology, currently leads the service.
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Clinicians
The talented team behind groundbreaking discoveries in this subspecialty.
Milestones
The current breast pathology faculty includes nationally and internationally recognized academic pathologists with decades of cumulative experience in the diagnosis and management of common and uncommon breast diseases. Their careers have been and continue to be decorated with honors and recognitions in the fields of education (Drs. Erika Crouch and Ian Hagemann), innovation (Dr. Lulu Sun, who was named an ASCP 40 under 40 honoree in 2023), outstanding service (Drs. Jon Ritter and Richard Cote, who are continually named Castle Connolly Top Doctors), among others. All faculty members have been invited lecturers at major pathology meetings and pathology societies and have served on numerous committees and boards.
The breast section runs an active consultation service under the direction of Dr. Boulos, who inherited his mentors’ David Page and Jean Simpson’s consult service following the latter’s retirement in 2021. The consult service receives numerous uniquely challenging cases from pathologists across the United States and delivers comprehensive reports and state-of-the-art ancillary testing with turnaround times of 24 hours or less for most cases. It also offers direct communication with the consulting pathologists.
Current research in the section focuses, among other things, on the integration of digital pathology, multiplex staining platforms, and artificial intelligence to better predict the risk of breast cancer in women with benign, atypical, and premalignant lesions of the breast.
