High-risk breast lesions: A machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision

Radiology

Volumn 286, Issue 3, 2018, Pages 810-818

  Bahl, Manisha ^a   Barzilay, Regina ^b   Yedidia, Adam B ^b   Locascio, Nicholas J ^b   Yu, Lili ^b   Lehman, Constance D ^a  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b Department of Electrical Engineering and Computer Science   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGE; AGED; ARTICLE; ATYPICAL DUCTAL HYPERPLASIA; BREAST BIOPSY; BREAST CANCER; BREAST LESION; BREAST SURGERY; COHORT ANALYSIS; CONTROLLED STUDY; DIGITAL BREAST TOMOSYNTHESIS; DIGITAL MAMMOGRAPHY; FEMALE; FOLLOW UP; HIGH RISK PATIENT; HISTOLOGY; HUMAN; HUMAN TISSUE; LOW RISK PATIENT; MACHINE LEARNING; MAJOR CLINICAL STUDY; MAMMOGRAPHY; PRIORITY JOURNAL; RANDOM FOREST; BREAST TUMOR; CLINICAL DECISION MAKING; IMAGE GUIDED BIOPSY; INTRADUCTAL CARCINOMA; MIDDLE AGED; PATHOLOGY; PRECANCER; PROCEDURES; PROGNOSIS; RISK ASSESSMENT; UNNECESSARY PROCEDURE; VERY ELDERLY; WATCHFUL WAITING; YOUNG ADULT;

ADULT; AGED; AGED, 80 AND OVER; BREAST NEOPLASMS; CARCINOMA, INTRADUCTAL, NONINFILTRATING; CLINICAL DECISION-MAKING; FEMALE; FOLLOW-UP STUDIES; HUMANS; IMAGE-GUIDED BIOPSY; MACHINE LEARNING; MIDDLE AGED; PRECANCEROUS CONDITIONS; PROGNOSIS; RISK ASSESSMENT; UNNECESSARY PROCEDURES; WATCHFUL WAITING; YOUNG ADULT;

EID: 85042418889     PISSN: 00338419     EISSN: 15271315     Source Type: Journal    
DOI: 10.1148/radiol.2017170549     Document Type: Article

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