» Articles » PMID: 27700229

Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes

Overview
Journal Radiology
Specialty Radiology
Date 2016 Oct 5
PMID 27700229
Citations 106
Authors
Affiliations
Soon will be listed here.
Abstract

Purpose To determine the relationship between tumor heterogeneity assessed by means of magnetic resonance (MR) imaging texture analysis and survival outcomes in patients with primary breast cancer. Materials and Methods Between January and August 2010, texture analysis of the entire primary breast tumor in 203 patients was performed with T2-weighted and contrast material-enhanced T1-weighted subtraction MR imaging for preoperative staging. Histogram-based uniformity and entropy were calculated. To dichotomize texture parameters for survival analysis, the 10-fold cross-validation method was used to determine cutoff points in the receiver operating characteristic curve analysis. The Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of texture parameters and morphologic or volumetric information obtained at MR imaging or clinical-pathologic variables with recurrence-free survival (RFS). Results There were 26 events, including 22 recurrences (10 local-regional and 12 distant) and four deaths, with a mean follow-up time of 56.2 months. In multivariate analysis, a higher N stage (RFS hazard ratio, 11.15 [N3 stage]; P = .002, Bonferroni-adjusted α = .0167), triple-negative subtype (RFS hazard ratio, 16.91; P < .001, Bonferroni-adjusted α = .0167), high risk of T1 entropy (less than the cutoff values [mean, 5.057; range, 5.022-5.167], RFS hazard ratio, 4.55; P = .018), and T2 entropy (equal to or higher than the cutoff values [mean, 6.013; range, 6.004-6.035], RFS hazard ratio = 9.84; P = .001) were associated with worse outcomes. Conclusion Patients with breast cancers that appeared more heterogeneous on T2-weighted images (higher entropy) and those that appeared less heterogeneous on contrast-enhanced T1-weighted subtraction images (lower entropy) exhibited poorer RFS. RSNA, 2016 Online supplemental material is available for this article.

Citing Articles

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.

Guo Y, Li T, Gong B, Hu Y, Wang S, Yang L Adv Sci (Weinh). 2024; 12(2):e2408069.

PMID: 39535476 PMC: 11727298. DOI: 10.1002/advs.202408069.


Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study.

Li B, Zhu J, Wang Y, Xu Y, Gao Z, Shi H Cancer Imaging. 2024; 24(1):103.

PMID: 39107799 PMC: 11302839. DOI: 10.1186/s40644-024-00744-1.


Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer.

Mohamed R, Panthi B, Adrada B, Boge M, Candelaria R, Chen H Sci Rep. 2024; 14(1):16073.

PMID: 38992094 PMC: 11239818. DOI: 10.1038/s41598-024-66220-9.


Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI.

Wagner S, Ewald C, Freitag D, Herrmann K, Koch A, Bauer J J Neurooncol. 2024; 169(2):257-267.

PMID: 38960965 PMC: 11341603. DOI: 10.1007/s11060-024-04725-z.


Radiomics-Based Machine Learning Classification Strategy for Characterization of Hepatocellular Carcinoma on Contrast-Enhanced Ultrasound in High-Risk Patients with LI-RADS Category M Nodules.

Li L, Liang X, Yu Y, Mao R, Han J, Peng C Indian J Radiol Imaging. 2024; 34(3):405-415.

PMID: 38912232 PMC: 11188750. DOI: 10.1055/s-0043-1777993.