title: Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging.
abstract: Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.
authors.affiliation: Department of Biological Regulation, Weizmann Institute of Science.
authors.forename: Noam
authors.initials: N
authors.lastname: Nissan
authors.affiliation: Unit of Biological Services, Weizmann Institute of Science.
authors.forename: Edna
authors.initials: E
authors.lastname: Furman-Haran
authors.affiliation: Department of Diagnostic Imaging, Meir Medical Center.
authors.forename: Myra
authors.initials: M
authors.lastname: Feinberg-Shapiro
authors.affiliation: Department of Biological Regulation, Weizmann Institute of Science.
authors.forename: Dov
authors.initials: D
authors.lastname: Grobgeld
authors.affiliation: Department of Biological Regulation, Weizmann Institute of Science.
authors.forename: Erez
authors.initials: E
authors.lastname: Eyal
authors.affiliation: Pathology Department, Meir Medical Center.
authors.forename: Tania
authors.initials: T
authors.lastname: Zehavi
authors.affiliation: Department of Biological Regulation, Weizmann Institute of Science; hadassa.degani@weizmann.ac.il.
authors.forename: Hadassa
authors.initials: H
authors.lastname: Degani
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016880:Anisotropy; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008875:Middle Aged; D011788:Quality of Life; D012984:Software