ArticleActa cytologica2012
Semiautomated laser capture microdissection of lung adenocarcinoma cytology samples.
Article in Acta cytologica, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Laser capture microdissection.Nature reviews. Methods primers · 2025Article
- Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.Acta cytologica · 2021Review
- Simplified molecular classification of lung adenocarcinomas based on EGFR, KRAS, and TP53 mutations.BMC cancer · 2020Article
- Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.Journal of pathology informatics · 2018Article
- Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images.Laboratory investigation; a journal of technical methods and pathology · 2015Review
- SIVQ-LCM protocol for the ArcturusXT instrument.Journal of visualized experiments : JoVE · 2014Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectiveIn 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
designHematoxylin 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.
resultsWe 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.
conclusionIn 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.
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