Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment

Li, Hui; Giger, Maryellen L.; Olopade, Olufunmilayo I.; Chinander, Michael R.

doi:10.1007/s10278-007-9093-9

Cited by 53 publications

(64 citation statements)

References 31 publications

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“…The breasts were taken from women diagnosed with a lesion in the other breast and the evaluated cases can therefore be considered high risk, indicating that the average breast density may be higher than in a normal population of women. 9 The average ͗␤͘ recon is lowered for all but one case. In this case, the reconstructed breast displays a high density breast with a large amount of breast structure and high contrast in many slices ͑͗␤͘ recon = 3.21͒, whereas the projections show large areas of tissue with low contrast.…”

Section: Discussionmentioning

confidence: 95%

Comparison of power spectra for tomosynthesis projections and reconstructed images

2009

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Burgess et al. ͓Med. Phys. 28, 419-437 ͑2001͔͒ showed that the power spectrum of mammographic breast background follows a power law and that lesion detectability is affected by the power-law exponent ␤ which measures the amount of structure in the background. Following the study of Burgess et al., the authors measured and compared the power-law exponent of mammographic backgrounds in tomosynthesis projections and reconstructed slices to investigate the effect of tomosynthesis imaging on background structure. Our data set consisted of 55 patient cases. For each case, regions of interest ͑ROIs͒ were extracted from both projection images and reconstructed slices. The periodogram of each ROI was computed by taking the squared modulus of the Fourier transform of the ROI. The power-law exponent was determined for each periodogram and averaged across all ROIs extracted from all projections or reconstructed slices for each patient data set. For the projections, the mean ␤ averaged across the 55 cases was 3.06 ͑standard deviation of 0.21͒, while it was 2.87 ͑0.24͒ for the corresponding reconstructions. The difference in ␤ for a given patient between the projection ROIs and the reconstructed ROIs averaged across the 55 cases was 0.194, which was statistically significant ͑p Ͻ 0.001͒. The 95% CI for the difference between the mean value of ␤ for the projections and reconstructions was ͓0.170, 0.218͔. The results are consistent with the observation that the amount of breast structure in the tomosynthesis slice is reduced compared to projection mammography and that this may lead to improved lesion detectability.

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Section: Discussionmentioning

confidence: 95%

Comparison of power spectra for tomosynthesis projections and reconstructed images

2009

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“…[9][10][11][12][13] Investigators have also studied breast parenchymal patterns as characterized by computerized texture analysis on digitized screen-film mammograms and full-field digital mammograms (FFDMs). [14][15][16][17][18][19][20][21] Results indicate that women at high risk of developing breast cancer tended to have mammographic parenchymal patterns that were coarse and low in contrast. [16][17][18][19][20][21] The purpose of this current study was to investigate the additional value of parenchymal pattern characteristics to breast percent density (PD) in characterizing and distinguishing between women at high risk for breast cancer and low-risk controls.…”

Section: Introductionmentioning

confidence: 98%

“…[14][15][16][17][18][19][20][21] Results indicate that women at high risk of developing breast cancer tended to have mammographic parenchymal patterns that were coarse and low in contrast. [16][17][18][19][20][21] The purpose of this current study was to investigate the additional value of parenchymal pattern characteristics to breast percent density (PD) in characterizing and distinguishing between women at high risk for breast cancer and low-risk controls. The image-based parenchymal phenotypes of coarseness and contrast, as well as other texture features, were calculated from digital radiographic texture analysis (RTA) applied to FFDMs.…”

Section: Introductionmentioning

confidence: 98%

Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing betweenBRCA1/2cases, unilateral cancer cases, and controls

Giger

et al. 2014

J. Med. Imag

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Abstract. We statistically compare the contributions of parenchymal phenotypes to mammographic density in distinguishing between high-risk cases and low-risk controls. The age-matched evaluation included computerized mammographic assessment of breast percent density (PD) and parenchymal patterns (phenotypes of coarseness and contrast) from radiographic texture analysis (RTA) of the full-field digital mammograms from 456 cases: 53 women with BRCA1/2 gene mutations, 75 with unilateral cancer, and 328 at low risk of developing breast cancer. Image-based phenotypes of parenchymal pattern coarseness and contrast were each found to significantly discriminate between the groups; however, PD did not. From ROC analysis, PD alone yielded area under the fitted ROC curve (AUC) values of 0.53 (SE ¼ 0.05) and 0.57 (SE ¼ 0.04) in the classification task between BRCA1/2 gene-mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. In a round-robin evaluation with Bayesian artificial neural network (BANN) analysis, RTA yielded AUC values of 0.81 (95% confidence interval [0.71, 0.89]) and 0.70 (95% confidence interval [0.63, 0.77]) between the BRCA1/2 gene-mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results show that high-risk and low-risk women have different mammographic parenchymal patterns with significantly higher discrimination resulting from characteristics of the parenchymal patterns than just the breast PD.

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“…The parenchyma texture coarseness feature (MaxEdge Gradient) 18 39 with a p-value of 0.035 after multiple test correction (Table I; Figs. 5 and 6).…”

Section: A Association Study For Entire Datasetmentioning

confidence: 99%

Pilot study demonstrating potential association between breast cancer image‐based risk phenotypes and genomic biomarkers

Giger

Sun

et al. 2014

Medical Physics

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Purpose: In this pilot study, the authors examined associations between image-based phenotypes and genomic biomarkers. The potential genetic contribution of UGT2B genes to interindividual variation in breast density and mammographic parenchymal patterns is demonstrated by performing an association study between image-based phenotypes and genomic biomarkers [single-nucleotide polymorphism (SNP) genotypes]. Methods: This candidate-gene approach study included 179 subjects for whom both mammograms and blood DNA samples had been obtained. The full-field digital mammograms were acquired using a GE Senographe 2000D FFDM system (12-bit; 0.1 mm-pixel size). Regions-of-interest, 256 × 256 pixels in size, selected from the central breast region behind the nipple underwent computerized image analysis to yield image-based phenotypes of mammographic density and parenchymal texture patterns. SNP genotyping was performed using a Sequenom MassArray System. One hundred twenty three SNPs with minor allele frequency above 5% were genotyped for the UGT2B gene clusters, and used in the study. The association between the image-based phenotypes and genomic biomarkers was assessed with the Pearson correlation coefficient via the PLINK software, and included permutation and correction for multiple SNP comparisons. Results: From the phenotype-genotype association analysis, a parenchyma texture coarseness feature was found to be correlated with SNP rs451632 after multiple test correction for the multiple SNPs (p = 0.022). The power law β, which is used to characterize the frequency component of texture patterns, was found to be correlated with SNP rs4148298 (p = 0.035). Conclusions:The authors' results indicate that UGT2B gene variation may contribute to interindividual variation in mammographic parenchymal patterns and breast density. Understanding the relationship between image-based phenotypes and genomic biomarkers may help understand the biologic mechanism for image-based biomarkers and yield a future role in personalized medicine.

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Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment

Cited by 53 publications

References 31 publications

Comparison of power spectra for tomosynthesis projections and reconstructed images

Comparison of power spectra for tomosynthesis projections and reconstructed images

Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing betweenBRCA1/2cases, unilateral cancer cases, and controls

Pilot study demonstrating potential association between breast cancer image‐based risk phenotypes and genomic biomarkers

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