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

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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 contrast-enhanced T1-weighted subtraction images show an example of texture analysis using different filter levels in a 67-year-old woman with a 36-mm invasive ductal carcinoma of the right breast: (A) conventional image without filter; (B) at a filter level of 0.5; (C) at a filter level of 1.5; (D) at a filter level of 2.
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Figure 1: Axial contrast-enhanced T1-weighted subtraction images show an example of texture analysis using different filter levels in a 67-year-old woman with a 36-mm invasive ductal carcinoma of the right breast: (A) conventional image without filter; (B) at a filter level of 0.5; (C) at a filter level of 1.5; (D) at a filter level of 2.

Mentions: After the ROIs were defined, a researcher with 7 years of experience in image analysis who was also blinded to the clinical and pathological results performed the texture analysis. We applied Gaussian spatial filters onto the images to focus on different pixel sizes. Filters 0.5 to 1.0 highlighted fine-texture features (2 and 4 pixels), filters 1.5 to 2.0 highlighted medium-texture features (6 and 10 pixels), and a filter of 2.5 highlighted coarse-texture features (12 pixels). The effect of filtration, by highlighting larger pixels, has been hypothesized to accentuate the contribution of the vasculature to texture features (Figure 1).15


Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis
Axial contrast-enhanced T1-weighted subtraction images show an example of texture analysis using different filter levels in a 67-year-old woman with a 36-mm invasive ductal carcinoma of the right breast: (A) conventional image without filter; (B) at a filter level of 0.5; (C) at a filter level of 1.5; (D) at a filter level of 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Axial contrast-enhanced T1-weighted subtraction images show an example of texture analysis using different filter levels in a 67-year-old woman with a 36-mm invasive ductal carcinoma of the right breast: (A) conventional image without filter; (B) at a filter level of 0.5; (C) at a filter level of 1.5; (D) at a filter level of 2.
Mentions: After the ROIs were defined, a researcher with 7 years of experience in image analysis who was also blinded to the clinical and pathological results performed the texture analysis. We applied Gaussian spatial filters onto the images to focus on different pixel sizes. Filters 0.5 to 1.0 highlighted fine-texture features (2 and 4 pixels), filters 1.5 to 2.0 highlighted medium-texture features (6 and 10 pixels), and a filter of 2.5 highlighted coarse-texture features (12 pixels). The effect of filtration, by highlighting larger pixels, has been hypothesized to accentuate the contribution of the vasculature to texture features (Figure 1).15

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