Computer-aided techniques for chromogenic immunohistochemistry: Status and directions

Cataldo, Santa Di; Ficarra, Elisa; Macii, Enrico

doi:10.1016/j.compbiomed.2012.08.004

Cited by 55 publications

(36 citation statements)

References 100 publications

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“…It is gratifying that computer automatic analysis technology has a significant advantage in dealing with this kind of big data information. Recently, several studies have developed various computer aided analysis methods to study the tumor image features [20]. Accurate identification of TNs boundary is the important premise of computer analysis [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Computer-Based Image Studies on Tumor Nests Mathematical Features of Breast Cancer and Their Clinical Prognostic Value

Wang

Yuan

et al. 2013

PLoS ONE

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BackgroundThe expending and invasive features of tumor nests could reflect the malignant biological behaviors of breast invasive ductal carcinoma. Useful information on cancer invasiveness hidden within tumor nests could be extracted and analyzed by computer image processing and big data analysis.MethodsTissue microarrays from invasive ductal carcinoma (n = 202) were first stained with cytokeratin by immunohistochemical method to clearly demarcate the tumor nests. Then an expert-aided computer analysis system was developed to study the mathematical and geometrical features of the tumor nests. Computer recognition system and imaging analysis software extracted tumor nests information, and mathematical features of tumor nests were calculated. The relationship between tumor nests mathematical parameters and patients' 5-year disease free survival was studied.ResultsThere were 8 mathematical parameters extracted by expert-aided computer analysis system. Three mathematical parameters (number, circularity and total perimeter) with area under curve >0.5 and 4 mathematical parameters (average area, average perimeter, total area/total perimeter, average (area/perimeter)) with area under curve <0.5 in ROC analysis were combined into integrated parameter 1 and integrated parameter 2, respectively. Multivariate analysis showed that integrated parameter 1 (P = 0.040) was independent prognostic factor of patients' 5-year disease free survival. The hazard risk ratio of integrated parameter 1 was 1.454 (HR 95% CI [1.017–2.078]), higher than that of N stage (HR 1.396, 95% CI [1.125–1.733]) and hormone receptor status (HR 0.575, 95% CI [0.353–0.936]), but lower than that of histological grading (HR 3.370, 95% CI [1.125–5.364]) and T stage (HR 1.610, 95% CI [1.026 –2.527]).ConclusionsThis study indicated integrated parameter 1 of mathematical features (number, circularity and total perimeter) of tumor nests could be a useful parameter to predict the prognosis of early stage breast invasive ductal carcinoma.

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Section: Introductionmentioning

confidence: 99%

Computer-Based Image Studies on Tumor Nests Mathematical Features of Breast Cancer and Their Clinical Prognostic Value

Wang

Yuan

et al. 2013

PLoS ONE

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“…However, interpreting IHC images by manual assessment is highly experience based and subjective. Additionally, no unified judgment and a lack of reproducibility as well as the potentially drastic variations among inter- and intraobservers further limited this technique [10], [11]. Therefore, an accurate and repeatable quantitative method for IHC images becomes increasingly desirable in clinical histopathology.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis

Liu

Wang

Liu

et al. 2016

Translational Oncology

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Despite the extensive application of multispectral imaging (MSI) in biomedical multidisciplinary researches, there is a paucity of data available regarding the implication of MSI in tumor prognosis prediction. We compared the behaviors of multispectral (MS) and conventional red-green-blue (RGB) images on assessment of human epidermal growth factor receptor 2 (HER2) immunohistochemistry to explore their impact on outcome in patients with invasive breast cancer (BC). Tissue microarrays containing 240 BC patients were introduced to compare the performance of MS and RGB imaging methods on the quantitative assessment of HER2 status and the prognostic value of 5-year disease-free survival (5-DFS). Both the total and average signal optical density values of HER2 MS and RGB images were analyzed, and all patients were divided into two groups based on the different 5-DFS. The quantification of HER2 MS images was negatively correlated with 5-DFS in lymph node–negative and –positive patients (P < .05), but RGB images were not in lymph node–positive patients (P = .101). Multivariate analysis indicated that the hazard ratio (HR) of HER2 MS was higher than that of HER2 RGB (HR = 2.454; 95% confidence interval [CI], 1.636-3.681 vs HR = 2.060; 95% CI, 1.361-3.119). Additionally, area under curve (AUC) by receiver operating characteristic analysis for HER2 MS was greater than that for HER2 RGB (AUC = 0.649; 95% CI, 0.577-0.722 vs AUC = 0.596; 95% CI, 0.522-0.670) in predicting the risk for recurrence. More importantly, the quantification of HER2 MS images has higher prediction accuracy than that of HER2 RGB images (69.6% vs 65.0%) on 5-DFS. Our study suggested that better information on BC prognosis could be obtained from the quantification of HER2 MS images and MS images might perform better in predicting BC prognosis than conventional RGB images.

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“…Various aspects of quantification of protein biomarkers by IHC have been reviewed recently [10,12,17]. In the many studies published so far, reproducibility and capacity advantages of IHC image analysis were used to quantify protein and/or protein-expressing cell populations to predict clinical outcomes.…”

Section: Road Map For Comprehensive Ihcmentioning

confidence: 99%

Comprehensive Immunohistochemistry: Digital, Analytical and Integrated

et al. 2016

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Immunohistochemistry (IHC) is widely used in contemporary pathology as a diagnostic and, increasingly, as a prognostic and predictive tool. The main value of the method today comes from a sensitive and specific detection of a protein of interest in the context of tissue architecture and cell populations. One of the major limitations of conventional IHC is related to the fact that the results are usually obtained by visual qualitative or semiquantitative evaluation. While this is sufficient for diagnostic purposes, measurement of prognostic and predictive biomarkers requires better accuracy and reproducibility. Also, objective evaluation of the spatial heterogeneity of biomarker expression as well as the development of combined/integrated biomarkers are in great demand. On the other end of the scale, the rapid development of tissue proteomics accounting for 2D spatial aspects has led to a disruptive concept of next-generation IHC, promising high multiplexing and broad dynamic range quantitative/spatial data on tissue protein expression. This ‘evolutionary gap' between conventional and next-generation IHC can be filled by comprehensive IHC based on digital technologies (empowered by quantification and spatial and multiparametric analytics) and integrated into the pathology workflow and information systems. In this paper, we share our perspectives on a comprehensive IHC road map as a multistep development process.

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Computer-aided techniques for chromogenic immunohistochemistry: Status and directions

Cited by 55 publications

References 100 publications

Computer-Based Image Studies on Tumor Nests Mathematical Features of Breast Cancer and Their Clinical Prognostic Value

Computer-Based Image Studies on Tumor Nests Mathematical Features of Breast Cancer and Their Clinical Prognostic Value

A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis

Comprehensive Immunohistochemistry: Digital, Analytical and Integrated

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