Abstract
.Purpose: The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting.Approach: A case–control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography).Results: MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors (, 95%CI 0.92 to 1.46), nor after further adjustment for BI-RADS density (, 95%CI 0.76 to 1.10). There was weak evidence that MMTEXT was more predictive for cancers that were not detected by mammography (unadjusted for density: , 95%CI 0.99 to 2.15 versus 1.03, 95%CI 0.79 to 1.35, Phet 0.10; adjusted for density: , 95%CI 0.70 to 1.77 versus 0.76, 95%CI 0.55 to 1.05, Phet 0.21).Conclusions: MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation.
Highlights
Subgroup analysis by mode of cancer detection suggested potential merit of MMTEXT for cancers that were not detected by mammography [unadjusted for density: IQ-OR 1.46 (0.99 to 2.15) versus 1.03 (0.79 to 1.35), Phet 0.10, area under the curves (aAUC) 0.55 (95%CI 0.48 to 0.60); adjusted for density: IQ-OR 1.11 (0.70 to 1.77) versus 0.76 (0.55 to 1.05), Phet 0.21; Table 4]
There was weak evidence that MMTEXT was more predictive for cancers that were not detected mammography (IQ-OR 1.46, 95%CI 0.99 to 2.15); after adjustment for breast density, the predictive ability decreases (IQ-OR 1⁄4 1.11, 95%CI 0.70 to 1.77)
It is arguable that there is no better measure of mammographic density than visual assessment from an expert,[31] and in our previous analysis of mammographic density for risk assessment we found that BI-RADS density conferred slightly more predictive information than a volumetric method on the same data.[30]
Summary
Over the past few decades, there has been increasing interest in individual risk assessment for breast cancer.[1,2,3] Motivations for this include the identification of individuals at extremelyJournal of Medical ImagingJan∕Feb 2020 Vol 7(1)Wang et al.: External validation of a mammographic texture marker for breast cancer risk in a case–control. . .high risk who would be potential candidates for risk-reducing surgery or preventive therapy;[4] delineation of populations at moderately enhanced risk who might benefit from enhanced screening;[5] and more recently, identification of populations at sufficiently low risk as not to require screening or risk management.[6]Breast cancer has a relatively well established hormonal aetiology, in addition to a growing body of knowledge on genetic risk factors.[7,8] existing risk models have shown a degree of accuracy in prediction [the area under the receiver operating characteristic curve (AUC) ranges around 0.56 to 0.77 for different predictors], it is clear that there is room for improvement.[9,10] One area that offers hope for improved risk assessment is utilization of digital mammographic image features. Over the past few decades, there has been increasing interest in individual risk assessment for breast cancer.[1,2,3] Motivations for this include the identification of individuals at extremely. Wang et al.: External validation of a mammographic texture marker for breast cancer risk in a case–control. Breast cancer has a relatively well established hormonal aetiology, in addition to a growing body of knowledge on genetic risk factors.[7,8] existing risk models have shown a degree of accuracy in prediction [the area under the receiver operating characteristic curve (AUC) ranges around 0.56 to 0.77 for different predictors], it is clear that there is room for improvement.[9,10] One area that offers hope for improved risk assessment is utilization of digital mammographic image features. Mammographic density, which is broadly defined as the amount of radio-opaque tissue, is well known as an important independent risk factor for breast cancer.[11,12,13] Some previous research has tried to improve mammographic density risk assessment by looking at other image features of a mammogram, and computational advances in machine learning are starting to spur more work.[14,15,16,17] A limitation with much of the literature looking at textural or other features from mammograms has been reproducibility.[18]
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