title: Predicting lymphatic drainage patterns and primary tumour location in patients with breast cancer.
abstract: Detailed knowledge of the lymphatic drainage of the breast is limited. Lymphoscintigraphy is a technique used during breast cancer treatment to accurately map patterns of lymphatic drainage from the primary tumour to the draining lymph nodes. This study aimed to create a statistical model to analyse the spread of breast cancer and primary tumour location using a large lymphoscintigraphy database, and visualise the results with a novel computational model. This study was based on lymphoscintigraphy data from 2,304 breast cancer patients treated at the Royal Prince Alfred Hospital Medical Centre in Sydney, Australia. Bayesian inferential techniques were implemented to estimate the probabilities of lymphatic drainage from each region of the breast to each draining node field, to multiple node fields, and to determine probabilities of tumour prevalence in each breast region. A finite element model of the torso and discrete model of the draining node fields were created to visualise these data and a software tool was developed to display the results (                            www.abi.auckland.ac.nz/breast-cancer                                                     ). Results confirmed that lymphatic drainage is most likely to occur to the axillary node field, and that there is significant likelihood of drainage to the internal mammary node field. The likelihood of lymphatic drainage from the whole breast to the axillary, internal mammary, infraclavicular, supraclavicular and interpectoral node fields were 98.2, 35.3, 1.7, 3.1, and 0.7%, respectively; whilst the probability of lymphatic drainage to multiple node fields was estimated to be 36.4%. Additionally, primary tumours are most likely to develop in the upper regions of the breast. The models developed provide quantitative estimates of lymphatic drainage of the breast, giving important insights into understanding breast cancer metastasis and have the potential to benefit both clinicians and patients during breast cancer diagnosis and treatment.
authors.affiliation: Auckland Bioengineering Institute, The University of Auckland, Level 6, 70 Symonds Street, Auckland, New Zealand. eblu005@aucklanduni.ac.nz
authors.forename: Evan I
authors.initials: EI
authors.lastname: Blumgart
authors.forename: Roger F
authors.initials: RF
authors.lastname: Uren
authors.forename: Poul M F
authors.initials: PM
authors.lastname: Nielsen
authors.forename: Martyn P
authors.initials: MP
authors.lastname: Nash
authors.forename: Hayley M
authors.initials: HM
authors.lastname: Reynolds
mesh_terms: D001499:Bayes Theorem; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D042601:Lymphatic Vessels / Q000473:pathology*; D008954:Models, Biological