Automated quantification of MART1-verified Ki67 indices by digital image analysis in melanocytic lesions.

CONTEXT: The proliferation marker Ki67 is an important diagnostic and prognostic aid in surgical pathology. However, manual quantification in a counting frame to accurately establish the proliferation rate (Ki67 index) is cumbersome and time-consuming. Instead, digital image analysis of Ki67/MART1 double stains may provide fast and novel index computations for entire tumor sections. OBJECTIVES: To design and compare image analysis protocols that compute Ki67 indices of Ki67/MART1 double stains, to compare automated indices with previously published manual indices, and to compare the total number of proliferating cells (mimicking a Ki67 single stain) with the number of MART1-verified proliferating cells. DESIGN: Whole slide images were captured from 48 melanomas and 77 nevi stained with an immunohistochemical cocktail against Ki67 and MART1. Ki67 indices were determined by digital image analysis and different equations based on number or area. RESULTS: The differences between mean indices of melanomas and nevi were significant (P < .001) in all index computations. Number-based image analysis of lesions with more than 250 melanocytic cells misclassified 1 of 42 melanomas and 4 of 53 nevi, numbers comparable with manual counting. Automated indices were significantly higher than manual indices, as were indices of mimicked Ki67 single stains compared with MART1-verified Ki67 indices (P < .001). CONCLUSIONS: Ki67 indices established by digital image analysis of Ki67/MART1 double stains demonstrated excellent abilities to discriminate melanomas from nevi with diagnostic performances equal to manually performed indices. Testing different definitions of the automated MART1-verified Ki67 index, no single definition stood out; thus, a variety of definitions may be used.

Practical considerations of image analysis and quantification of signal transduction IHC staining.

The dramatic increase in computer processing power in combination with the availability of high-quality digital cameras during the last 10 years has fertilized the grounds for quantitative microscopy based on digital image analysis. With the present introduction of robust scanners for whole slide imaging in both research and routine, the benefits of automation and objectivity in the analysis of tissue sections will be even more obvious. For in situ studies of signal transduction, the combination of tissue microarrays, immunohistochemistry, digital imaging, and quantitative image analysis will be central operations. However, immunohistochemistry is a multistep procedure including a lot of technical pitfalls leading to intra- and interlaboratory variability of its outcome. The resulting variations in staining intensity and disruption of original morphology are an extra challenge for the image analysis software, which therefore preferably should be dedicated to the detection and quantification of histomorphometrical end points.

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.

Low-dose transdermal estradiol induces breast density and heterogeneity changes comparable to those of raloxifene.

UNLABELLED: The aim of this study was to investigate whether transdermal low-dose estradiol treatment induces changes in mammographic density or heterogeneity compared with raloxifene, whether if these changes relate to changes in bone formation/resorption markers, and whether these findings indicate elevation of breast cancer risk by treatment. METHODS: Digitized mammograms of 2 x 135 completers of a 2-year, randomized trial formed the base of the present analysis. Active treatments were transdermal estradiol releasing 0.014 mg estradiol (E2)/week and orally administered raloxifene hydrochloride 60 mg/day, respectively. Influence of the therapies on breast density was assessed with categorical scores Breast Imaging Reporting and Data System, area percentage density, and computer-based (E2-specific) heterogeneity examination of radiographs. These where related to physical and systemic markers. RESULTS: At baseline, no mammography scoring methodology or other marker could separate the two treatment groups of transdermal estradiol and raloxifene. No treatment induced significant density changes measured by Breast Imaging Reporting and Data System. Both treatments made the area percentage density increase and the estradiol significantly. Both treatments induced significant changes in E2-specific heterogeneity scoring (E2-specific heterogeneity examination of radiograph), and the raloxifene treatment induced a significantly higher change. At baseline, the mammographic markers showed negative correlation with body mass index and positive correlation with serum type I collagen crosslinks C-telopeptide. The changes in mammographic markers did not essentially exhibit correlations to changes in bone markers in either treatment group. CONCLUSIONS: Low-dose transdermal estradiol and raloxifene induced comparable changes in breast density and heterogeneity. Baseline correlations may be explained through relations to obesity. The current study does not yield evidence against the hypothesis that "neither raloxifene nor low dose transdermal estradiol treatment increases the breast cancer 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.

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.