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An evaluation of breast radiological...

Dollins, Camille Arianne.

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An evaluation of breast radiological density assessment by Quantra software as compared to visual classification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	An evaluation of breast radiological density assessment by Quantra software as compared to visual classification./
Author:	Dollins, Camille Arianne.
Description:	73 p.
Notes:	Source: Masters Abstracts International, Volume: 55-03.
Contained By:	Masters Abstracts International55-03(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10010520
ISBN:	9781339465340

An evaluation of breast radiological density assessment by Quantra software as compared to visual classification.
Dollins, Camille Arianne.

An evaluation of breast radiological density assessment by Quantra software as compared to visual classification. - 73 p.

Source: Masters Abstracts International, Volume: 55-03.

Thesis (M.S.)--University of Central Arkansas, 2015.

Breast density is a known risk factor in the development of breast cancer. Breast density is determined based on visual readings by radiologists using American Society of Radiology Bi-RADS density method, and therefore subjective. A way of measuring breast density numerically is computing the percent density using software. This thesis compares the qualitative readings by radiologists to quantitative readings by R2 Quantra Hologic computerized software of the same mammograms in order to establish an objective cutoff for categorizing breast density. Receiver Operating Characteristic (ROC) curves and logistic regression models are used to find the best cutoff Quantra density value to separate the low (D1/D2) density from the high density (D3/D4). Using a race indicator provided, the best cutoff values are also found for Quantra densities of African-American and Caucasian patients. Survival analysis is performed on simulated survival data to demonstrate how to find the best Quantra density cutoff value to distinguish between high risk and low risk groups. This analysis is done using Kaplan-Meier curves and Cox Proportional Hazards (PH) models.

ISBN: 9781339465340Subjects--Topical Terms:

517247
Statistics.

An evaluation of breast radiological density assessment by Quantra software as compared to visual classification.
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020 $a 9781339465340
035 $a (MiAaPQ)AAI10010520
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100 1 $a Dollins, Camille Arianne. $3 3191253
245 1 3 $a An evaluation of breast radiological density assessment by Quantra software as compared to visual classification.
300 $a 73 p.
500 $a Source: Masters Abstracts International, Volume: 55-03.
500 $a Adviser: R.B. Lenin.
502 $a Thesis (M.S.)--University of Central Arkansas, 2015.
520 $a Breast density is a known risk factor in the development of breast cancer. Breast density is determined based on visual readings by radiologists using American Society of Radiology Bi-RADS density method, and therefore subjective. A way of measuring breast density numerically is computing the percent density using software. This thesis compares the qualitative readings by radiologists to quantitative readings by R2 Quantra Hologic computerized software of the same mammograms in order to establish an objective cutoff for categorizing breast density. Receiver Operating Characteristic (ROC) curves and logistic regression models are used to find the best cutoff Quantra density value to separate the low (D1/D2) density from the high density (D3/D4). Using a race indicator provided, the best cutoff values are also found for Quantra densities of African-American and Caucasian patients. Survival analysis is performed on simulated survival data to demonstrate how to find the best Quantra density cutoff value to distinguish between high risk and low risk groups. This analysis is done using Kaplan-Meier curves and Cox Proportional Hazards (PH) models.
590 $a School code: 1605.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
650 4 $a Medical imaging. $3 3172799
690 $a 0463
690 $a 0308
690 $a 0574
710 2 $a University of Central Arkansas. $b Mathematics - Applied Mathematics. $3 3173013
773 0 $t Masters Abstracts International $g 55-03(E).
790 $a 1605
791 $a M.S.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10010520