TY - JOUR
T1 - Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype
AU - Net, Jose M.
AU - Whitman, Gary J.
AU - Morris, Elizabteh
AU - Brandt, Kathleen R.
AU - Burnside, Elizabeth S.
AU - Giger, Maryellen L.
AU - Ganott, M.
AU - Sutton, Elizabeth J.
AU - Zuley, Margarita L.
AU - Rao, Arvind
N1 - Funding Information:
Grant Support: EJS and EAM were funded in part through the NIH/NCI Grant, P30 CA008748. MLG was funded in part through the NIH/NCI Grant, U01CA195564. AR was supported by CCSG Bioinformatics Shared Resource, P30 CA01667, an Institutional Research Grant from the University of Texas MD Anderson Cancer Center (MD Anderson) and a Career Development Award from the MD Anderson Brain Tumor SPORE. ESB was supported by the NIH grant, K24CA194251.
Funding Information:
Grant Support: EJS and EAM were funded in part through the NIH / NCI Grant, P30 CA008748 . MLG was funded in part through the NIH / NCI Grant, U01CA195564 . AR was supported by CCSG Bioinformatics Shared Resource , P30 CA01667 , an Institutional Research Grant from the University of Texas MD Anderson Cancer Center (MD Anderson) and a Career Development Award from the MD Anderson Brain Tumor SPORE. ESB was supported by the NIH grant, K24CA194251 .
Funding Information:
Grant Support: EJS and EAM were funded in part through the NIH/NCI Grant, P30 CA008748. MLG was funded in part through the NIH/NCI Grant, U01CA195564. AR was supported by CCSG Bioinformatics Shared Resource, P30 CA01667, an Institutional Research Grant from the University of Texas MD Anderson Cancer Center (MD Anderson) and a Career Development Award from the MD Anderson Brain Tumor SPORE. ESB was supported by the NIH grant, K24CA194251. Grant Support: EJS and EAM were funded in part through the NIH/ NCI Grant, P30 CA008748. MLG was funded in part through the NIH/ NCI Grant, U01CA195564. AR was supported by CCSG Bioinformatics Shared Resource, P30 CA01667, an Institutional Research Grant from the University of Texas MD Anderson Cancer Center (MD Anderson) and a Career Development Award from the MD Anderson Brain Tumor SPORE. ESB was supported by the NIH grant, K24CA194251. This work was completed with the TCGA Breast Phenotype Research Group. Grant Support: EJS and EAM were funded in part through the NIH/ NCI Grant, P30 CA008748. MLG was funded in part through the NIH/ NCI Grant, U01CA195564. AR was supported by CCSG Bioinformatics Shared Resource, P30 CA01667, an Institutional Research Grant from the University of Texas MD Anderson Cancer Center (MD Anderson) and a Career Development Award from the MD Anderson Brain Tumor SPORE. ESB was supported by the NIH grant, K24CA194251.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - PURPOSE: The purpose of this study was to investigate if human-extracted MRI tumor phenotypes of breast cancer could predict receptor status and tumor molecular subtype using MRIs from The Cancer Genome Atlas project. MATERIALS AND METHODS: Our retrospective interpretation study utilized the analysis of HIPAA-compliant breast MRI data from The Cancer Imaging Archive. One hundred and seven preoperative breast MRIs of biopsy proven invasive breast cancers were analyzed by 3 fellowship-trained breast-imaging radiologists. Each study was scored according to the Breast Imaging Reporting and Data System lexicon for mass and nonmass features. The Spearman rank correlation was used for association analysis of continuous variables; the Kruskal-Wallis test was used for associating continuous outcomes with categorical variables. The Fisher-exact test was used to assess correlations between categorical image-derived features and receptor status. Prediction of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor, and molecular subtype were performed using random forest classifiers. RESULTS: ER+ tumors were associated with the absence of rim enhancement (P = 0.019, odds ratio [OR] 5.5), heterogeneous internal enhancement (P = 0.02, OR 6.5), peritumoral edema (P = 0.0001, OR 10.0), and axillary adenopathy (P = 0.04, OR 4.4). ER+ tumors were smaller than ER− tumors (23.7 mm vs 29.2 mm, P = 0.02, OR 8.2). All of these variables except the lack of axillary adenopathy were also associated with progesterone receptor+ status. Luminal A tumors (n = 57) were smaller compared to nonLuminal A (21.8 mm vs 27.5 mm, P = 0.035, OR 7.3) and lacked peritumoral edema (P = 0.001, OR 6.8). Basal like tumors were associated with heterogeneous internal enhancement (P = 0.05, OR 10.1), rim enhancement (P = 0.05, OR6.9), and perituomral edema (P = 0.0001, OR 13.8). CONCLUSIONS: Human extracted MRI tumor phenotypes may be able to differentiate those tumors with a more favorable clinical prognosis from their more aggressive counterparts.
AB - PURPOSE: The purpose of this study was to investigate if human-extracted MRI tumor phenotypes of breast cancer could predict receptor status and tumor molecular subtype using MRIs from The Cancer Genome Atlas project. MATERIALS AND METHODS: Our retrospective interpretation study utilized the analysis of HIPAA-compliant breast MRI data from The Cancer Imaging Archive. One hundred and seven preoperative breast MRIs of biopsy proven invasive breast cancers were analyzed by 3 fellowship-trained breast-imaging radiologists. Each study was scored according to the Breast Imaging Reporting and Data System lexicon for mass and nonmass features. The Spearman rank correlation was used for association analysis of continuous variables; the Kruskal-Wallis test was used for associating continuous outcomes with categorical variables. The Fisher-exact test was used to assess correlations between categorical image-derived features and receptor status. Prediction of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor, and molecular subtype were performed using random forest classifiers. RESULTS: ER+ tumors were associated with the absence of rim enhancement (P = 0.019, odds ratio [OR] 5.5), heterogeneous internal enhancement (P = 0.02, OR 6.5), peritumoral edema (P = 0.0001, OR 10.0), and axillary adenopathy (P = 0.04, OR 4.4). ER+ tumors were smaller than ER− tumors (23.7 mm vs 29.2 mm, P = 0.02, OR 8.2). All of these variables except the lack of axillary adenopathy were also associated with progesterone receptor+ status. Luminal A tumors (n = 57) were smaller compared to nonLuminal A (21.8 mm vs 27.5 mm, P = 0.035, OR 7.3) and lacked peritumoral edema (P = 0.001, OR 6.8). Basal like tumors were associated with heterogeneous internal enhancement (P = 0.05, OR 10.1), rim enhancement (P = 0.05, OR6.9), and perituomral edema (P = 0.0001, OR 13.8). CONCLUSIONS: Human extracted MRI tumor phenotypes may be able to differentiate those tumors with a more favorable clinical prognosis from their more aggressive counterparts.
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U2 - 10.1067/j.cpradiol.2018.08.003
DO - 10.1067/j.cpradiol.2018.08.003
M3 - Article
C2 - 30270031
AN - SCOPUS:85054158857
SN - 0363-0188
VL - 48
SP - 467
EP - 472
JO - Current Problems in Diagnostic Radiology
JF - Current Problems in Diagnostic Radiology
IS - 5
ER -