Objective: In the past decade molecular diagnostics has changed the clinical management of lung adenocarcinoma patients. Molecular diagnostics, however, is largely dependent on the quantity and quality of the tumor DNA that is retrieved from the tissue or cytology samples. Frequently, patients are diagnosed on cytology specimens where the tumor cells are scattered within the cell block, making selecting for tumor enrichment difficult. In the past we have used laser capture microdissection (LCM) to select for pure populations of tumor cells to increase the sensitivity of molecular assays. This study explores several methods for semiautomated computer-guided LCM. Study Design: Hematoxylin and eosin- or TTF-1-immunostained slides from a pleural effusion cell block with metastatic lung adenocarcinoma were used for LCM with either AutoScan or a recently described pattern-matching algorithm, spatially invariant vector quantization (SIVQ), to define morphologic predicates (vectors) to select cells of interest. Results: We retrieved pure populations of tumor cells using both algorithm-guided LCM approaches with slight variations in cellular retrievals. Both methods were semiautomated, requiring minimum technical supervision. Conclusion: In this study we demonstrate the first semiautomated, computer-guided LCM of a cytology specimen using SIVQ and AutoScan, a first step towards the long-term goal of integrating LCM into the clinical cytology-molecular workflow.

Keywords:
Semiautomated laser capture microdissection, Spatially invariant vector quantization, Lung adenocarcinoma, Cytology
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2012
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