P2.11-14 Malignancy Associated Change and The LuCED® Test for Detection of Early Stage Lung Cancer

Abstract

Early detection remains the most reliable and effective strategy for curing lung cancer. Many approaches, however, are limited by poor sensitivity or specificity that increase health care costs and potentially risk patient health through unneeded procedures. The association between cell morphology and cancer has been established in the pathology literature. However, through the so-called field effect, cancer can introduce subtle morphological changes into non-cancerous cells that are proximal to the tumor site. The Cell-CT™ platform and LuCED® test represent a promising new method for detecting lung cancer with high (92%) sensitivity and (95%) specificity based on cytologically abnormal cells. In this research we investigate use of the Cell-CT to detect malignancy associated changes in normal cells near the cancer with a view towards enhancing LuCED test performance. We present a study of 3D morphological alterations in non-cancer cells obtained from sputum of healthy subjects and biopsy confirmed lung cancer patients. Three major cell types were analyzed from 235 patients: bronchial epithelial columnar, squamous intermediate, and mature macrophages. We used the Cell-CT™ platform to measure over 700 different structural biomarkers for each cell. The measurements were used to define prominent clusters of cells through a hierarchical process that were then used under supervised learning, with case status as ground truth, to create classifiers that optimally separated cells from cancer vs. normal cases. The table gives classifier development and performance characteristics:Tabled 1Cell TypeNumber of cells from cancer patientsNumber of cells from normal patientsCluster-based Supervised Learning – aROCSquamous Intermediate23166840.94Macrophages496050400.94Columnar cells322732340.92 Open table in a new tab Our results indicate that the Cell-CT can discriminate cell features that are too subtle to distinguish by a human. The study suggests that detection of cells with Malignancy Associated Changes may be used to further enhance the LuCED test’s performance beyond published levels.