Effectiveness of Using Texture Analysis in Evaluating Heterogeneity in Breast Tumor and in Predicting Tumor Aggressiveness in Breast Cancer Patients

Persistent Link:
http://hdl.handle.net/10150/603653
Title:
Effectiveness of Using Texture Analysis in Evaluating Heterogeneity in Breast Tumor and in Predicting Tumor Aggressiveness in Breast Cancer Patients
Author:
Hopp, Alix
Affiliation:
The University of Arizona College of Medicine - Phoenix
Issue Date:
25-Mar-2016
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the College of Medicine - Phoenix, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Collection Information:
This item is part of the College of Medicine - Phoenix Scholarly Projects 2016 collection. For more information, contact the Phoenix Biomedical Campus Library at pbc-library@email.arizona.edu.
Publisher:
The University of Arizona.
Abstract:
Objective and Hypothesis We hypothesize that tumor heterogeneity or tissue complexity, as measured by quantitative texture analysis (QTA) on mammogram, is a marker of tumor aggressiveness in breast cancer patients. Methods Tumor heterogeneity was assessed using QTA on digital mammograms of 64 patients with invasive ductal carcinoma (IDC). QTA generates six different values – Mean, standard deviation (SD), mean positive pixel value (MPPV), entropy, kurtosis, and skewness. Tumor aggressiveness was assessed using patients’ Oncotype DX® Recurrence Score (RS), a proven genomic assay score that correlates with the rate of remote breast cancer recurrence. RS and hormonal receptor status ‐ estrogen receptor (ER) and progesterone receptor (PR) ‐ were collected from pathology reports. Data were analyzed using statistical tools including Spearman rank correlation, linear regression, and logistic regression. Results Linear regression analysis showed that QTA parameter, SD, was a good predictor of RS (F=6.89, p=0.0108, R2=0.0870) at SSF=0.4. When PR status was included as a predictor, PR status and QTA parameter Skewness‐Diff, achieved linear model of greater fit (F=15.302, p<0.0001, R2=0.2988) at SSF=1. Among PR+ patients, Skewness‐Diff was a good linear predictor of RS (F=9.36, p=0.0034, R2=0.1320) at SSF=0.8. Logistic regression analysis showed that QTA parameters were good predictors of high risk RS probability, using different cutoffs of 30 and 25 for high risk RS; these QTA parameters were Entropy‐Diff for RS>30 (chi2=10.98, p=0.0009, AUC=0.8424, SE=0.0717) and Mean‐Total for RS>25 (chi2=9.98, p=0.0016, AUC=0.7437, SE=0.0612). When PR status was included, logistic models of higher log‐likelihood chi2 were found with SD‐Diff for RS>30 (chi2=18.69, p=0.0001, AUC=0.9409, SE=0.0322), and with Mean‐Total for RS>25 (chi2=25.56, p<0.0001, AUC=0.8443, SE=0.0591). For PR+ patients, good predictors were SD‐Diff for RS>30 (chi2=6.87, p=0.0087, AUC=0.9212, SE=0.0515), and MPP‐Diff and Skewness‐Diff for RS>25 (chi2=16.17, p=0.0003, AUC=0.9103, SE=0.0482). Significance Quantitative measurement of breast cancer tumor heterogeneity using QTA on digital mammograms may be used as predictors of RS and can potentially allow a non‐invasive and cost‐effective way to quickly assess the likelihood of RS and high risk RS.
MeSH Subjects:
Breast Neoplasms; Genetic Heterogeneity
Description:
A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine.
Mentor:
Korn, Ronald MD, PhD

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleEffectiveness of Using Texture Analysis in Evaluating Heterogeneity in Breast Tumor and in Predicting Tumor Aggressiveness in Breast Cancer Patientsen_US
dc.contributor.authorHopp, Alixen
dc.contributor.departmentThe University of Arizona College of Medicine - Phoenixen
dc.date.issued2016-03-25en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the College of Medicine - Phoenix, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.collectioninformationThis item is part of the College of Medicine - Phoenix Scholarly Projects 2016 collection. For more information, contact the Phoenix Biomedical Campus Library at pbc-library@email.arizona.edu.en_US
dc.publisherThe University of Arizona.en
dc.description.abstractObjective and Hypothesis We hypothesize that tumor heterogeneity or tissue complexity, as measured by quantitative texture analysis (QTA) on mammogram, is a marker of tumor aggressiveness in breast cancer patients. Methods Tumor heterogeneity was assessed using QTA on digital mammograms of 64 patients with invasive ductal carcinoma (IDC). QTA generates six different values – Mean, standard deviation (SD), mean positive pixel value (MPPV), entropy, kurtosis, and skewness. Tumor aggressiveness was assessed using patients’ Oncotype DX® Recurrence Score (RS), a proven genomic assay score that correlates with the rate of remote breast cancer recurrence. RS and hormonal receptor status ‐ estrogen receptor (ER) and progesterone receptor (PR) ‐ were collected from pathology reports. Data were analyzed using statistical tools including Spearman rank correlation, linear regression, and logistic regression. Results Linear regression analysis showed that QTA parameter, SD, was a good predictor of RS (F=6.89, p=0.0108, R2=0.0870) at SSF=0.4. When PR status was included as a predictor, PR status and QTA parameter Skewness‐Diff, achieved linear model of greater fit (F=15.302, p<0.0001, R2=0.2988) at SSF=1. Among PR+ patients, Skewness‐Diff was a good linear predictor of RS (F=9.36, p=0.0034, R2=0.1320) at SSF=0.8. Logistic regression analysis showed that QTA parameters were good predictors of high risk RS probability, using different cutoffs of 30 and 25 for high risk RS; these QTA parameters were Entropy‐Diff for RS>30 (chi2=10.98, p=0.0009, AUC=0.8424, SE=0.0717) and Mean‐Total for RS>25 (chi2=9.98, p=0.0016, AUC=0.7437, SE=0.0612). When PR status was included, logistic models of higher log‐likelihood chi2 were found with SD‐Diff for RS>30 (chi2=18.69, p=0.0001, AUC=0.9409, SE=0.0322), and with Mean‐Total for RS>25 (chi2=25.56, p<0.0001, AUC=0.8443, SE=0.0591). For PR+ patients, good predictors were SD‐Diff for RS>30 (chi2=6.87, p=0.0087, AUC=0.9212, SE=0.0515), and MPP‐Diff and Skewness‐Diff for RS>25 (chi2=16.17, p=0.0003, AUC=0.9103, SE=0.0482). Significance Quantitative measurement of breast cancer tumor heterogeneity using QTA on digital mammograms may be used as predictors of RS and can potentially allow a non‐invasive and cost‐effective way to quickly assess the likelihood of RS and high risk RS.en
dc.typeThesisen
dc.subject.meshBreast Neoplasmsen
dc.subject.meshGenetic Heterogeneityen
dc.descriptionA Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine.en
dc.contributor.mentorKorn, Ronald MD, PhDen
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