200 research outputs found

    Computer-assisted assessment of the Human Epidermal Growth Factor Receptor 2 immunohistochemical assay in imaged histologic sections using a membrane isolation algorithm and quantitative analysis of positive controls

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    <p>Abstract</p> <p>Background</p> <p>Breast cancers that overexpress the human epidermal growth factor receptor 2 (HER2) are eligible for effective biologically targeted therapies, such as trastuzumab. However, accurately determining HER2 overexpression, especially in immunohistochemically equivocal cases, remains a challenge. Manual analysis of HER2 expression is dependent on the assessment of membrane staining as well as comparisons with positive controls. In spite of the strides that have been made to standardize the assessment process, intra- and inter-observer discrepancies in scoring is not uncommon. In this manuscript we describe a pathologist assisted, computer-based continuous scoring approach for increasing the precision and reproducibility of assessing imaged breast tissue specimens.</p> <p>Methods</p> <p>Computer-assisted analysis on HER2 IHC is compared with manual scoring and fluorescence in situ hybridization results on a test set of 99 digitally imaged breast cancer cases enriched with equivocally scored (2+) cases. Image features are generated based on the staining profile of the positive control tissue and pixels delineated by a newly developed Membrane Isolation Algorithm. Evaluation of results was performed using Receiver Operator Characteristic (ROC) analysis.</p> <p>Results</p> <p>A computer-aided diagnostic approach has been developed using a membrane isolation algorithm and quantitative use of positive immunostaining controls. By incorporating internal positive controls into feature analysis a greater Area Under the Curve (AUC) in ROC analysis was achieved than feature analysis without positive controls. Evaluation of HER2 immunostaining that utilized membrane pixels, controls, and percent area stained showed significantly greater AUC than manual scoring, and significantly less false positive rate when used to evaluate immunohistochemically equivocal cases.</p> <p>Conclusion</p> <p>It has been shown that by incorporating both a membrane isolation algorithm and analysis of known positive controls a computer-assisted diagnostic algorithm was developed that can reproducibly score HER2 status in IHC stained clinical breast cancer specimens. For equivocal scoring cases, this approach performed better than standard manual evaluation as assessed by ROC analysis in our test samples. Finally, there exists potential for utilizing image-analysis techniques for improving HER2 scoring at the immunohistochemically equivocal range.</p

    Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays

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    <p>Abstract</p> <p>Introduction</p> <p>The human epidermal growth factor receptor 2 (HER2) is an established biomarker for management of patients with breast cancer. While conventional testing of HER2 protein expression is based on semi-quantitative visual scoring of the immunohistochemistry (IHC) result, efforts to reduce inter-observer variation and to produce continuous estimates of the IHC data are potentiated by digital image analysis technologies.</p> <p>Methods</p> <p>HER2 IHC was performed on the tissue microarrays (TMAs) of 195 patients with an early ductal carcinoma of the breast. Digital images of the IHC slides were obtained by Aperio ScanScope GL Slide Scanner. Membrane connectivity algorithm (HER2-CONNECTâ„¢, Visiopharm) was used for digital image analysis (DA). A pathologist evaluated the images on the screen twice (visual evaluations: VE1 and VE2). HER2 fluorescence <it>in situ </it>hybridization (FISH) was performed on the corresponding sections of the TMAs. The agreement between the IHC HER2 scores, obtained by VE1, VE2, and DA was tested for individual TMA spots and patient's maximum TMA spot values (VE1max, VE2max, DAmax). The latter were compared with the FISH data. Correlation of the continuous variable of the membrane connectivity estimate with the FISH data was tested.</p> <p>Results</p> <p>The pathologist intra-observer agreement (VE1 and VE2) on HER2 IHC score was almost perfect: kappa 0.91 (by spot) and 0.88 (by patient). The agreement between visual evaluation and digital image analysis was almost perfect at the spot level (kappa 0.86 and 0.87, with VE1 and VE2 respectively) and at the patient level (kappa 0.80 and 0.86, with VE1max and VE2max, respectively). The DA was more accurate than VE in detection of FISH-positive patients by recruiting 3 or 2 additional FISH-positive patients to the IHC score 2+ category from the IHC 0/1+ category by VE1max or VE2max, respectively. The DA continuous variable of the membrane connectivity correlated with the FISH data (HER2 and CEP17 copy numbers, and HER2/CEP17 ratio).</p> <p>Conclusion</p> <p>HER2 IHC digital image analysis based on membrane connectivity estimate was in almost perfect agreement with the visual evaluation of the pathologist and more accurate in detection of HER2 FISH-positive patients. Most immediate benefit of integrating the DA algorithm into the routine pathology HER2 testing may be obtained by alerting/reassuring pathologists of potentially misinterpreted IHC 0/1+ versus 2+ cases.</p

    A study of the molecular pathology of ductal carcinoma in situ and invasive ductal carcinoma of the breast.

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    The biological validity of the histopathological classification of ductal carcinoma in situ (DCIS) of the breast was evaluated in this study by correlating the three histopathological grades of DCIS to immunohistochemical expression of Ki67, p53, cerbB-2, markers of poor prognosis in invasive ductal carcinoma (IDC) and also to bcl2 and ER, markers of good prognosis in invasive breast cancer. DCIS grades correlated positively to Ki67, p53, cerbB-2 and negatively to bcl2 and ER, suggesting validity of the classification. The incidence of bax protein expression was determined immunohistochemically in DCIS and IDC. It did not correlate to histopathological grades of DCIS or IDC. The relationships of bax protein to the above mentioned biological markers were also determined in DCIS and IDC. Furthermore, the expression of bax, bcl2, Ki67, ER, p53 and cerbB-2 within DCIS grades was compared with the expression of these markers within IDC grades. The DCIS grades were determined subjectively as well as objectively by means of computer assisted image analysis with significant correlation found between subjective and objective measures. Image analysis was also used to determine percentage of positive cells per case for the nuclear stains (Ki67, ER, p53). Immunohistochemically positive p53 cases were analysed for p53 mutation by polymerase chain reaction (PCR) and subsequent DNA sequencing to compare the incidence of p53 mutation in DCIS to that of IDC. Biochemical changes within tissue may either initiate disease or occur as the result of the disease process and these changes can be studied by both Fourier transform infrared (FTIR) and FT-Raman spectroscopic techniques. FTIR and FT-Raman were employed to distinguish the DCIS and IDC grades. It has the potential to distinguish between DCIS grades, between IDC grades and also between DCIS and IDC as whole groups. The implications of the obtained data for the understanding of the molecular biology of DCIS of the breast and IDC are discussed and future investigations to further elucidate the molecular and cellular mechanisms involved are proposed

    Statistical methods for tissue array images - algorithmic scoring and co-training

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    Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm - Tissue Array Co-Occurrence Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists' input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high-dimensional setting when there is "sufficient" redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists' performance in terms of accuracy and repeatability.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS543 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org
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