Tatyana Zhukov

Enhanced imaging and accelerated photothermalysis of A549 human lung cancer cells by gold nanospheres

BACKGROUND & AIMS Gold nanoparticles are excellent photon-thermal energy converters. The purpose of this work was to investigate the influence of gold nanoparticles on the photothermalysis of A549 lung tumor cells. MATERIALS & METHODS A549 lung tumor cells were exposed to goat antihuman immunoglobulin (Ig)G-conjugated gold nanospheres (40 nm) and were then imaged under a dark-field microscope. The live cells were then subjected to photoirradiation using a 633-nm laser at different power levels. The viability of tumor cells under laser irradiation was monitored by confocal microscopy using a viability-assay kit. RESULTS & DISCUSSION The death rates of A549 lung tumor cells after gold nanoparticle exposure increased significantly under laser irradiation. The maximum initial cell death rate was observed at a laser power level of 3.75 mW, with the initial deactivation rate accelerated by a factor of 6.6 and a total loss of 92% of cell viability. CONCLUSION This work demonstrated potential applications of gold nanospheres as both imaging probes and enhancing agents for photothermal therapy of cancer.

A kernelised fuzzy-Support Vector Machine CAD system for the diagnosis of lung cancer from tissue images

This research describes a non-interactive process that applies several forms of computational intelligence to classifying biopsy lung tissue samples. Three types of lung cancer evaluated (squamous cell carcinoma, adenocarcinoma, and bronchioalveolar carcinoma) together account for 65?70% of diagnoses. Accuracy achieved supports hypothesis that an accurate predictive model is generated from training images, and performance achieved is an accurate baseline for the processs potential scaling to larger datasets. Feature vector performance is good or better than Thiran and Macqs in every case. Except bronchioalveolar carcinomas, each individual cancer classification task experienced improvement, with two groupings showing nearly 20% classification accuracy.

Antigen Retrieval Improves hnRNP A2/B1 Immunohistochemical Localization in Premalignant Lesions of the Lung

Monoclonal antibody 703D4 recognizes a 31 kD lung cancer associated antigen (p31) expressed both in resected lung tumor and in sputum cells shed from the bronchial epithelium in advance of clinical cancer [1, 2]. Originally developed by mouse immunization using a whole non-small cell lung cancer (NSCLC) tumor-cell extract, 703D4 recognizes a protein expressed by most NSCLC as well as small cell lung cancer (SCLC) cell lines [1,3]. Recently, the 703D4 antigen has been purified by a seven-step chromatographic procedure, guided by Western blotting [4]. Sequencing of the principal immunostaining protein identified a heterogeneous nuclear ribonucleoprotein-A2 (hnRNP-A2). A splice variant, hnRNP-B1, was identified as a minor copurifying immunoreactive protein. Table 1 presents a summary of the advances in understanding of p31 expression in pulmonary tissues.

Prognosis of stage I lung cancer patients through quantitative analysis of centrosomal features

Centrosome amplification leads to the loss of regulated chromosome segregation, aneuploidy, and chromosome instability and has the possibility to be a biomarker of cancer prognosis. To explore this feasibility, resected, stage I non-small cell lung cancer (NSCLC) tissues from six survivor and six fatal cases were immunostained and scanned. Regions of interest were selected to include one cell and its centrosomes. After segmentation, feature abstraction, and optimization, six nonredundant features were used for statistical analysis and classification. Two analytic methods showed that for each feature, centrosomes from survivors differed from centrosomes of fatalities, indicating sampling from different populations. The data were classified using linear discriminant analysis (LDA) and support vector machines (SVM) with 10-fold cross-validation. Classification accuracy was 74% by LDA and 79% by SVM, respectively, and further improved to 85% with bagging. Centrosome can be a biomarker for stage I NSCLC prognosis and has potential for clinical utility.

An adaptive image segmentation process for the classification of lung biopsy images

The purpose of this study was to develop a computer-based second opinion diagnostic tool that could read microscope images of lung tissue and classify the tissue sample as normal or cancerous. This problem can be broken down into three areas: segmentation, feature extraction and measurement, and classification. We introduce a kernel-based extension of fuzzy c-means to provide a coarse initial segmentation, with heuristically-based mechanisms to improve the accuracy of the segmentation. The segmented image is then processed to extract and quantify features. Finally, the measured features are used by a Support Vector Machine (SVM) to classify the tissue sample. The performance of this approach was tested using a database of 85 images collected at the Moffitt Cancer Center and Research Institute. These images represent a wide variety of normal lung tissue samples, as well as multiple types of lung cancer. When used with a subset of the data containing images from the normal and adenocarcinoma classes, we were able to correctly classify 78% of the images, with a ROC AZ of 0.758.

Combinational Feature Optimization for Classification of Lung Tissue Images

A novel approach to feature optimization for classification of lung carcinoma using tissue images is presented. The methodology uses a combination of three characteristics of computational features: F-measure, which is a representation of each feature towards classification, inter-correlation between features and pathology based information. The metadata provided from pathological parameters is used for mapping between computational features and biological information. Multiple regression analysis maps each category of features based on how pathology information is correlated with the size and location of cancer. Relatively the computational features represented the tumor size better than the location of the cancer. Based on the three criteria associated with the features, three sets of feature subsets with individual validation are evaluated to select the optimum feature subset. Based on the results from the three stages, the knowledgebase produces the best subset of features. An improvement of 5.5% was observed for normal Vs all abnormal cases with Az value of 0.731 and 74/114 correctly classified. The best Az value of 0.804 with 66/84 correct classification and improvement of 21.6% was observed for normal Vs adenocarcinoma.