Open AccessPrediction of Atypical Ductal Hyperplasia Upgrades Through a Machine Learning Approach to Reduce Unnecessary Surgical Excisions
Author(s) -
Lia X. Harrington,
Roberta M. diFlorioAlexander,
Katherine Trinh,
Todd Mackenzie,
Arief A. Suriawinata,
Saeed Hassanpour
Publication year - 2018
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.18.00083
Subject(s) - medicine , random forest , biopsy , malignancy , logistic regression , cohort , medical diagnosis , radiology , surgery , machine learning , pathology , computer science
Surgical excision is currently recommended for all occurrences of atypical ductal hyperplasia (ADH) found on core needle biopsies for malignancy diagnoses and treatment of lesions. The excision of all ADH lesions may lead to overtreatment, which results in invasive surgeries for benign lesions in many women. A machine learning method to predict ADH upgrade may help clinicians and patients decide whether combined active surveillance and hormonal therapy is a reasonable alternative to surgical excision.