Automated image analysis in histopathology: a valuable tool in medical diagnostics.
ABSTRACT Virtual pathology, the process of assessing digital images of histological slides, is gaining momentum in today's laboratory environment. Indeed, digital image acquisition systems are becoming commonplace, and associated image analysis solutions are viewed by most as the next critical step in automated histological analysis. Here, we document the advances in the technology, with reference to past and current techniques in histological assessment. In addition, the demand for these technologies is analyzed with major players profiled. As there are several image analysis software programs focusing on the quantification of immunohistochemical staining, particular attention is paid to this application in this review. Oncology has been a primary target area for these approaches, with example studies in this therapeutic area being covered here. Toxicology-based image analysis solutions are also profiled as these are steadily increasing in popularity, especially within the pharmaceutical industry. Reinforced by the phenomenal growth of the virtual pathology field, it is envisioned that the market for automated image analysis tools will greatly expand over the next 10 years.
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ABSTRACT: Prostate cancer (PCa) is the most common male cancer in the United States. A major challenge that remains is to predict the clinical outcome in managing PCa patients. The prolyl isomerase Pin1 has been shown to be overexpressed in some human cancer tissues and thought to be an important player in several oncogenic pathways. However, the relationship between Pin1 expression and clinical outcome of cancer patients has not been explored. In this study, we investigated the role of Pin1 in human PCa progression and its clinicopathological significance. Immunohistochemical assessment with affinity-purified polyclonal Pin1-specific antibodies was performed on formalin-fixed paraffin sections of tissue microarray composed of 580 radical prostatectomy specimens. As determined by visual semiquantitation and confirmed by automated image analysis quantitation, Pin1 expression was positively correlated with clinical stage. Furthermore, Cox survival analysis results indicated that patients with a higher Pin1 expression had a significantly higher probability of recurrence than their counterparts with low Pin1 expression, as defined by a serum prostate-specific antigen level of > or =0.4 ng/ml on two consecutive occasions after radical prostatectomy. In addition, patients with high Pin1 expression had almost 4 times the risk of having earlier recurrence than those with low Pin1 expression; patients with a very high level had 8.1 times the risk of an earlier recurrence than a low Pin1 expresser. Pin1 was also an excellent predictor of recurrence in the subset of patients with Gleason score 6 or 7 when analyzed separately: a patient with high Pin1 expression had 8.6 times the risk of having earlier recurrence than one with low Pin1 expression. Pin1 expression is as good as or better than currently used postoperatively available clinicopathological parameters and potentially could be used in the preoperative setting to assist in choice of treatment. Thus, this study suggests a role for Pin1 expression as a potentially excellent prognostic marker in PCa and suggests that Pin1 may also serve as a novel therapeutic target for PCa.Cancer Research 10/2003; 63(19):6244-51. · 8.65 Impact Factor
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ABSTRACT: Human tumor xenografts have been used extensively for rapid screening of the efficacy of anticancer drugs for the past 35 years. The selection of appropriate xenograft models for drug testing has been largely empirical and has not incorporated a similarity to the tumor type of origin at the molecular level. This study is the first comprehensive analysis of the transcriptome of a large set of pediatric xenografts, which are currently used for preclinical drug testing. Suitable models representing the tumor type of origin were identified. It was found that the characteristic expression patterns of the primary tumors were maintained in the corresponding xenografts for the majority of samples. Because a prerequisite for developing rationally designed drugs is that the target is expressed at the protein level, we developed tissue arrays from these xenografts and corroborated that high mRNA levels yielded high protein levels for two tested genes. The web database and availability of tissue arrays will allow for the rapid confirmation of the expression of potential targets at both the mRNA and the protein level for molecularly targeted agents. The database will facilitate the identification of tumor markers predictive of response to tested agents as well as the discovery of new molecular targets.Cancer Research 02/2007; 67(1):32-40. · 8.65 Impact Factor
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ABSTRACT: We assessed the accuracy of automated cell imaging systems when compared to manual evaluation of cytospin slides in determining the presence of cytokeratin-positive, disseminated breast cancer cells in bone marrow aspirates. A total of 298 cytospin slides of bone marrow aspirates were first evaluated by individual screening by one expert immunocytologist. Subsequently, all 298 slides were evaluated by the Automated Cell Imaging System (ACIS) by ChromaVisiontrade mark. Two separate analysis cycles were performed using ACIS. The results of the two ACIS analysis cycles were almost identical: in 293 out of 298 samples (98.3%), identical numbers of disseminated breast cancer cells were detected. In the remaining five samples (1.7%), the result of the two ACIS analysis cycles differed by only one tumor cell. By using the manual technique, 120 cytospin samples were found to be positive. ACIS was able to detect additional tumor cells in 64 cases. Not once did ACIS miss tumor cells when compared to the manual technique. Using ACIS, we were able to determine the bone marrow status of patients with nonmetastatic breast cancer faster, with greater accuracy, and with greater reproducibility than with the manual technique.Journal of Clinical Laboratory Analysis 02/2005; 19(3):115-9. · 1.36 Impact Factor