Jakob Raundahl

A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer

OBJECTIVE We investigated whether breast cancer is predicted by a breast cancer risk mammographic texture resemblance (MTR) marker. METHODS A previously published case-control study included 495 women of which 245 were diagnosed with breast cancer. In baseline mammograms, 2-4 years prior to diagnosis, the following mammographic parameters were analysed for relation to breast cancer risk: (C) categorical parenchymal pattern scores; (R) radiologists percentage density, (P) computer-based percentage density; (H) computer-based breast cancer risk MTR marker; (E) computer-based hormone replacement treatment MTR marker; and (A) an aggregate of P and H. RESULTS Density scores, C, R, and P correlated (tau=0.3-0.6); no other pair of scores showed large (tau>0.2) correlation. For the parameters, the odds ratios of future incidence of breast cancer comparing highest to lowest categories (146 and 106 subject respectively) were C: 2.4(1.4-4.2), R: 2.4(1.4-4.1), P: 2.5(1.5-4.2), E: non-significant, H: 4.2(2.4-7.2), and A: 5.6(3.2-9.8). The AUC analysis showed a similarly increasing pattern (C: 0.58±0.02, R: 0.57±0.03, P: 0.60±0.03, H: 0.63±0.02, A: 0.66±0.02). The AUC of the aggregate marker (A) surpasses others significantly except H. HRT-MTR (E) did not significantly identify future cancers or correlate with any other marker. CONCLUSIONS Breast cancer risk MTR marker was independent of density scores and more predictive of risk. The hormone replacement treatment MTR marker did not identify patients at risk.

Automated Effect-Specific Mammographic Pattern Measures

We investigate the possibility to develop methodologies for assessing effect specific structural changes of the breast tissue using a general statistical machine learning framework. We present an approach of obtaining objective mammographic pattern measures quantifying a specific biological effect, such as hormone replacement therapy (HRT). We compare results using this approach to using standard density measures. We show that the proposed method can quantify both age related effects and effects caused by HRT. Age effects are significantly detected by our method where standard methodologies fail. The separation of HRT subpopulations using our approach is comparable to the best methodology, which is interactive.

Parallel assessment of the impact of different hormone replacement therapies on breast density by radiologist- and computer-based analyses of mammograms.

Objectives First, to compare the impact of nasally and orally dosed estradiol on breast density; second, to investigate the utility of computer-based automated approaches to the assessment of breast density with reference to traditional methods. Methods Digitized images from two 2-year, randomized, placebo-controlled trials formed the basis of the present post hoc analysis. Active treatments were 1 mg estradiol continuously combined with 0.125 mg trimegestone (oral hormone replacement therapy, HRT) or low-dose (150 or 300 µg estradiol) nasal estradiol cyclically combined with 200 mg micronized progesterone (nasal HRT). The effects on breast density were assessed by a radiologist, providing the BI-RADS® score and the interactive threshold, and by a computer-based approach, providing the measure of stripiness and the HRT-effect specific measure of breast density. Results In the oral HRT trial, active treatment induced a significant increase in breast density, which was consistent in all methods used (all p < 0.05). In contrast, none of the methods detected significant changes in women receiving nasal HRT. The sensitivity of automated methods to discriminate HRT- from placebo-treated women was equal or better than the sensitivity of methods performed by the radiologist. Conclusions The markedly different pharmacokinetic profile of nasal estrogen seems to be associated with better breast safety. Automated computer-based analysis of digitized mammograms provides a sensitive measure of changes in breast density induced by hormones and could serve as a useful tool in future clinical trials.

Breast density changes associated with postmenopausal hormone therapy: post hoc radiologist- and computer-based analyses

Objective:The aim of this study was to assess the impact of oral hormone therapy (HT) on breast density in postmenopausal women and to compare the use of computer-based automated approaches for the assessment of breast density with reference to traditional methods. Methods:Low-dose oral estrogen (1 mg) continuously combined with drospirenone (2 mg) was administered to postmenopausal women for up to 2 years (26 treatment cycles, 28 d/cycle) in a randomized, placebo-controlled trial. This post hoc analysis assessed the changes in breast density measured from digitized images by two radiologist-based approaches (Breast Imaging Reporting and Data System score and interactive threshold) and one computer-based technique (heterogeneity examination of radiographs). Correlations of temporal changes in breast density with changes in serum estradiol levels, biochemical markers of bone metabolism, and bone mineral density at the spine and femur were also assessed. Results:Breast density assessed by the radiologist-based approaches increased significantly from baseline in the HT group (P < 0.01), with significant divergence from placebo at 2 years (P < 0.01). Heterogeneity examination of radiograph score by computer-based technique was unchanged in the HT group and decreased significantly with placebo (P < 0.001) to produce a significant group divergence (P < 0.05). Changes in mammographic markers by radiologist- and computer-based approaches correlated with each other in the HT group (P < 0.01) but not in the placebo group. Conclusions:HT for 2 years in postmenopausal women significantly increased radiologist-assessed breast density compared with placebo, in addition to significant changes in estrogen levels, markers of bone metabolism, and bone mineral density. Computer-automated techniques may be comparable with and offer advantages over traditional methods.

Understanding hessian-based density scoring

Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density. No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. For serial, temporal analysis it is necessary to be able to detect more subtle changes. In previous work, a method for measuring the effect of HRT w.r.t. changes in biological density in the breast is described. The method provides structural information orthogonal to intensity-based methods. Hessian-based features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined. We have previously shown that this method can separate patients receiving HRT from patients receiving placebo. In this work, the focus is on deeper understanding of the methodology using tests on sets of artificial images of regular elongated structures.

Mammographic density measured as changes in tissue structure caused by HRT

Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density. No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. They may be sufficient in certain cases and for single measurements, but for serial, temporal analysis it is necessary to be able to detect more subtle changes and, in addition, to be more reproducible. In this work an automated method for measuring the effect of HRT w.r.t. changes in biological density in the breast is presented. This measure is a novel measure, which provides structural information orthogonal to intensity-based methods. Hessian eigenvalues at different scales are used as features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined. In the experiments, two sets of mammograms of 50 patients from a double blind, placebo controlled HRT experiment were used. The change in density for the HRT group, measured with the new method, was significantly higher (p = 0.0002) than the change in the control group.

Effect of projective viewpoint in detecting temporal density changes

An important question in mammographic image analysis is the importance of the projected view of the breast. Can temporal changes in density be detected equally well using either one of the commonly available views Medio-Lateral (ML) and Cranio-Caudal (CC) or a combination of the two? Two sets of mammograms of 50 patients in a double-blind, placebo controlled hormone replacement therapy (HRT) experiment were used. One set of ML and CC view from 1999 and one from 2001. HRT increases density which means that the degree of separation of the populations (one group receiving HRT and the other placebo) can be used as a measure of how much density change information is carried in a particular view or combination of views. Earlier results have shown a high correlation between CC and ML views leading to the conclusion that only one of them is needed for density assessment purposes. A similar high correlation coefficient was observed in this study (0.85), while the correlation between changes was a bit lower (0.71). Using both views to separate the patients receiving hormones from the ones receiving placebo increased the area under corresponding ROC curves from 0.76 ± 0.04 to 0.79 ± 0.04.

Quantifying effect-specific mammographic density

A methodology is introduced for the automated assessment of structural changes of breast tissue in mammograms. It employs a generic machine learning framework and provides objective breast density measures quantifying the specific biological effects of interest. In several illustrative experiments on data from a clinical trial, it is shown that the proposed method can quantify effects caused by hormone replacement therapy (HRT) at least as good as standard methods. Most interestingly, the separation of subpopulations using our approach is considerably better than the best alternative, which is interactive. Moreover, the automated method is capable of detecting age effects where standard methodologies completely fail.

Evaluation of four mammographic density measures on HRT data

Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with dense breasts have a four to six fold risk increase. There is currently no gold standard for automatic assessment of mammographic density. In previous work two different automated methods for measuring the effect of HRT w.r.t. changes in breast density have been presented. One is a percentage density based on an adaptive global threshold, and the other is an intensity invariant measure, which provides structural information orthogonal to intensity-based methods. In this article we investigate the ability to detect density changes induced by HRT for these measures and compare to a radiologists BI-RADS rating and interactive threshold percentage density. In the experiments, two sets of mammograms of 80 patients from a double blind, placebo controlled HRT experiment are used. The p-values for the statistical significance of the separation of density means, for the HRT group and the placebo group at end of study, are 0.2, 0.1, 0.02 and 0.02 for the automatic threshold, BI-RADS, the stripyness and the interactive threshold respectively.