Analytical Cellular Pathology

Analytical Cellular Pathology / 2012 / Article

Open Access

Volume 35 |Article ID 289625 |

Gerald Li, Martial Guillaud, Jean LeRiche, Annette McWilliams, Adi Gazdar, Stephen Lam, Calum MacAulay, "Automated Sputum Cytometry for Detection of Intraepithelial Neoplasias in the Lung", Analytical Cellular Pathology, vol. 35, Article ID 289625, 15 pages, 2012.

Automated Sputum Cytometry for Detection of Intraepithelial Neoplasias in the Lung

Received12 Jul 2011
Accepted06 Jan 2012


Background: Despite the benefits of early lung cancer detection, no effective strategy for early screening and treatment exists, partly due to a lack of effective surrogate biomarkers. Our novel sputum biomarker, the Combined Score (CS), uses automated image cytometric analysis of ploidy and nuclear morphology to detect subtle intraepithelial changes that often precede lung tumours.Methods: 2249 sputum samples from 1795 high-risk patients enrolled in ongoing chemoprevention trials were subjected to automated quantitative image cytometry after Feulgen-thionin staining. Samples from normal histopathology patients were compared against samples from carcinoma in situ (CIS) and cancer patients to train the CS.Results: CS correlates with several lung cancer risk factors, including histopathological grade, age, smoking status, and p53 and Ki67 immunostaining. At 50% specificity, CS detected 78% of all highest-risk subjects—those with CIS or worse plus those with moderate or severe dysplasia and abnormal nuclear morphology.Conclusion: CS is a powerful yet minimally invasive tool for rapid and inexpensive risk assessment for the presence of precancerous lung lesions, enabling enrichment of chemoprevention trials with highest-risk dysplasias. CS correlates with other biomarkers, so CS may find use as a surrogate biomarker for patient assessment and as an endpoint in chemoprevention clinical trials.

Copyright © 2012 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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