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Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis

View Article: PubMed Central - PubMed

ABSTRACT

There is no study that investigates the potential correlation between the heterogeneity obtained from texture analysis of medical images and the heterogeneity observed from histopathological findings. We investigated whether texture analysis of magnetic resonance images correlates with histopathological findings.

Seventy-five patients with estrogen receptor positive invasive ductal carcinoma who underwent preoperative breast magnetic resonance imaging (MRI) were included. Tumor entropy and uniformity were determined on T2- and contrast-enhanced T1-weighted subtraction images under different filter levels. Two pathologists evaluated the detailed histopathological findings of the tumors including tumor cellularity, dominant stroma type, central scar, histologic grade, extensive intraductal component (EIC), and lymphovascular invasion. Entropy and uniformity values on both T2- and contrast-enhanced T1-weighted subtraction images were compared with detailed pathological findings.

In a multivariate analysis, entropy significantly increased only on unfiltered T2-weighted images (P = 0.013). Tumor cellularity and predominant stroma did not affect the uniformity or entropy on both T2- and contrast-enhanced T1-weighted subtraction images. High histologic grades showed increased uniformity and decreased entropy on contrast-enhanced T1-weighted subtraction images, whereas the opposite tendency was observed on T2-weighted images. Invasive ductal carcinoma with an EIC or lymphovascular invasion only affected the contrast-enhanced T1-weighted subtraction images, through increased uniformity and decreased entropy. The best uniformity results were recorded on T2- and contrast-enhanced T1-weighted subtraction images at a filter level of 0.5. Entropy showed the best results at a filter level of 0.5 on contrast-enhanced T1-weighted subtraction images. However, on T2-weighted images, an ideal model was achieved on unfiltered images.

MRI texture analysis correlated with pathological tumor heterogeneity.

No MeSH data available.


Related in: MedlinePlus

Axial magnetic resonance images show an example of texture analysis in a 50-year-old woman with an invasive ductal carcinoma of the right breast. Pathology revealed histologic grade 3 tumor exhibiting 90% cellularity, EIC, and lymphovascular invasion. (A) Axial contrast-enhanced T1-weighted subtraction image shows an 18-mm irregular mass. (B) Histogram obtained from entire tumor on contrast-enhanced T1-weighted subtraction image presents uniformity and entropy. (C) T2-weighted image at the same level as (A). (D) Histogram obtained from entire tumor on the T2-weighted image exhibits uniformity and entropy. EIC = extensive intraductal component.
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Figure 2: Axial magnetic resonance images show an example of texture analysis in a 50-year-old woman with an invasive ductal carcinoma of the right breast. Pathology revealed histologic grade 3 tumor exhibiting 90% cellularity, EIC, and lymphovascular invasion. (A) Axial contrast-enhanced T1-weighted subtraction image shows an 18-mm irregular mass. (B) Histogram obtained from entire tumor on contrast-enhanced T1-weighted subtraction image presents uniformity and entropy. (C) T2-weighted image at the same level as (A). (D) Histogram obtained from entire tumor on the T2-weighted image exhibits uniformity and entropy. EIC = extensive intraductal component.

Mentions: where 2s represents the number of discrete values (in this study, s = 8), I is the intensity of the original T1- or T2-weighted MR images, min (ROI) is the minimum intensity value within ROI, and max (ROI) is the maximum intensity value within ROI. Heterogeneity within the ROI was quantified with and without image filtration, calculating entropy (irregularity) and uniformity (distribution of gray level).8 Entropy is a measure of texture irregularity, whereas uniformity reflects how close the image is to a uniform distribution of the gray levels: higher entropy and lower uniformity represent greater heterogeneity (Figures 2 and 3).8 Entropy and uniformity were defined using the following equations: 


Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis
Axial magnetic resonance images show an example of texture analysis in a 50-year-old woman with an invasive ductal carcinoma of the right breast. Pathology revealed histologic grade 3 tumor exhibiting 90% cellularity, EIC, and lymphovascular invasion. (A) Axial contrast-enhanced T1-weighted subtraction image shows an 18-mm irregular mass. (B) Histogram obtained from entire tumor on contrast-enhanced T1-weighted subtraction image presents uniformity and entropy. (C) T2-weighted image at the same level as (A). (D) Histogram obtained from entire tumor on the T2-weighted image exhibits uniformity and entropy. EIC = extensive intraductal component.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4998252&req=5

Figure 2: Axial magnetic resonance images show an example of texture analysis in a 50-year-old woman with an invasive ductal carcinoma of the right breast. Pathology revealed histologic grade 3 tumor exhibiting 90% cellularity, EIC, and lymphovascular invasion. (A) Axial contrast-enhanced T1-weighted subtraction image shows an 18-mm irregular mass. (B) Histogram obtained from entire tumor on contrast-enhanced T1-weighted subtraction image presents uniformity and entropy. (C) T2-weighted image at the same level as (A). (D) Histogram obtained from entire tumor on the T2-weighted image exhibits uniformity and entropy. EIC = extensive intraductal component.
Mentions: where 2s represents the number of discrete values (in this study, s = 8), I is the intensity of the original T1- or T2-weighted MR images, min (ROI) is the minimum intensity value within ROI, and max (ROI) is the maximum intensity value within ROI. Heterogeneity within the ROI was quantified with and without image filtration, calculating entropy (irregularity) and uniformity (distribution of gray level).8 Entropy is a measure of texture irregularity, whereas uniformity reflects how close the image is to a uniform distribution of the gray levels: higher entropy and lower uniformity represent greater heterogeneity (Figures 2 and 3).8 Entropy and uniformity were defined using the following equations: 

View Article: PubMed Central - PubMed

ABSTRACT

There is no study that investigates the potential correlation between the heterogeneity obtained from texture analysis of medical images and the heterogeneity observed from histopathological findings. We investigated whether texture analysis of magnetic resonance images correlates with histopathological findings.

Seventy-five patients with estrogen receptor positive invasive ductal carcinoma who underwent preoperative breast magnetic resonance imaging (MRI) were included. Tumor entropy and uniformity were determined on T2- and contrast-enhanced T1-weighted subtraction images under different filter levels. Two pathologists evaluated the detailed histopathological findings of the tumors including tumor cellularity, dominant stroma type, central scar, histologic grade, extensive intraductal component (EIC), and lymphovascular invasion. Entropy and uniformity values on both T2- and contrast-enhanced T1-weighted subtraction images were compared with detailed pathological findings.

In a multivariate analysis, entropy significantly increased only on unfiltered T2-weighted images (P = 0.013). Tumor cellularity and predominant stroma did not affect the uniformity or entropy on both T2- and contrast-enhanced T1-weighted subtraction images. High histologic grades showed increased uniformity and decreased entropy on contrast-enhanced T1-weighted subtraction images, whereas the opposite tendency was observed on T2-weighted images. Invasive ductal carcinoma with an EIC or lymphovascular invasion only affected the contrast-enhanced T1-weighted subtraction images, through increased uniformity and decreased entropy. The best uniformity results were recorded on T2- and contrast-enhanced T1-weighted subtraction images at a filter level of 0.5. Entropy showed the best results at a filter level of 0.5 on contrast-enhanced T1-weighted subtraction images. However, on T2-weighted images, an ideal model was achieved on unfiltered images.

MRI texture analysis correlated with pathological tumor heterogeneity.

No MeSH data available.


Related in: MedlinePlus