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. 2017 Oct 18;19(1):114.
doi: 10.1186/s13058-017-0906-6.

A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies

Chao Wang  1 , Adam R Brentnall  2 , Jack Cuzick  2 , Elaine F Harkness  3 , D Gareth Evans  4 , Susan Astley  3
Affiliations

A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies

Chao Wang et al. Breast Cancer Res. .

Abstract

Background: The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw full field digital mammograms (FFDM).

Method: A case-control study nested within a screening cohort (age 46-73 years) from Manchester UK was used to develop a texture feature risk score (264 cases diagnosed at the same time as mammogram of the contralateral breast, 787 controls) using the least absolute shrinkage and selection operator (LASSO) method for 112 features, and validated in a second case-control study from the same cohort but with cases diagnosed after the index mammogram (317 cases, 931 controls). Predictive ability was assessed using deviance and matched concordance index (mC). The ability to improve risk estimation beyond percent volumetric density (Volpara) was evaluated using conditional logistic regression.

Results: The strongest features identified in the training set were "sum average" based on the grey-level co-occurrence matrix at low image resolutions (original resolution 10.628 pixels per mm; downsized by factors of 16, 32 and 64), which had a better deviance and mC than volumetric PD. In the validation study, the risk score combining the three sum average features achieved a better deviance than volumetric PD (Δχ2 = 10.55 or 6.95 if logarithm PD) and a similar mC to volumetric PD (0.58 and 0.57, respectively). The risk score added independent information to volumetric PD (Δχ2 = 14.38, p = 0.0008).

Conclusion: Textural features based on digital mammograms improve risk assessment beyond volumetric percentage density. The features and risk score developed need further investigation in other settings.

Keywords: Breast cancer; Breast density; Digital mammogram; Risk prediction; Texture.

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Conflict of interest statement

Ethics approval and consent to participate

The PROCAS study was approved by Central Manchester Research Ethics Committee (reference: 09/H1008/81) and consent was obtained from study participants at the time of screening.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Matched concordance index (mC) for sum average at different image downsize factors, with bootstrap 95% confidence intervals (CI)
Fig. 2
Fig. 2
Comparison of mammograms (for presentation purpose processed images are shown) with two of the lowest (a) and highest (b) standardized risk scores. All mammograms have similar volumetric percent density (PD) around 10%. a Mammograms with low risk scores (-1.7 and -1.3, respectively). Volumetric PDs are 10.1% and 10.2%, respectively. b Mammograms with high risk scores (3.2 and 2.0, respectively). Volumetric PDs are 9.9% and 10.0%, respectively
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