A new method for lung cancer prognosis via centrosome image feature analysis.

OBJECTIVE: To predict survival of resected stage I non-small cell lung cancer (NSCLC) patients through quantitative analysis and classification of centrosome features. STUDY DESIGN: Disordered centrosome amplification leads to the loss of regulated chromosome segregation, aneuploidy and chromosome instability and may be a biomarker of cancer prognosis. Resected, stage I NSCLC tissues from survivor and fatal cases were immunostained with gamma-tubulin and scanned by confocal microscopy. Regions of interest were selected to include 1 cell and at least 1 centrosome. Four hundred forty-six regions were imaged, including 903 centrosomes whose features were extracted and measured. After segmentation, 12 centrosome features were measured. After optimization, 6 non-redundant features were selected for statistical analysis and classification. RESULTS: Two statistical methods showed that for each feature, centrosomes from survivors differed significantly from centrosomes of fatalities. Centrosomes were classified into survival or fatal outcomes by centrosome features using linear discriminant analysis, support vector machines (SVMs) and further optimized using SVMs with bagging. Ten-fold cross-validation was applied. Classification accuracies were 74%, 79% and 85%, respectively. CONCLUSION: Centrosome features can be a prognostic biomarker for resected stage I NSCLC and may indicate patients who would benefit from additional adjuvant therapy.

Quantificational and statistical analysis of the differences in centrosomal features of untreated lung cancer cells and normal cells.

OBJECTIVE: To distinguish untreated lung cancer cells from normal cells through quantitative analysis and statistical inference of centrosomal features extracted from cell images. STUDY DESIGN: Recent research indicates that human cancer cell development is accompanied by centrosomal abnormalities. For quantitative analysis of centrosome abnormalities, high-resolution images of normal and untreated cancer lung cells were acquired. After the images were preprocessed and segmented, 11 features were extracted. Correlations among the features were calculated to remove redundant features. Ten nonredundant features were selected for further analysis. The mean values of 10 centrosome features were compared between cancer and normal cells by the two-sample t-test; distributions of the 10 features of cancer and normal centrosomes were compared by the two-sample Kolmogorov-Smirnov test. RESULTS: Both tests reject the null hypothesis; the means and distributions of features coincide for normal and cancer cells. The 10 centrosome features separate normal from cancer cells at the 5% significance level and show strong evidence that all 10 features exhibit major differences between normal and cancer cells. CONCLUSION: Centrosomes from untreated cancer and normal bronchial epithelial cells can be distinguished through objective measurement and quantitative analysis, suggesting a new approach for lung cancer detection, early diagnosis and prognosis.

Computerized analysis of cellular features and biomarkers for cytologic diagnosis of early lung cancer.

OBJECTIVE: To present a set of novel computerized analysis algorithms to construct a computer-aided cytologic diagnosis (CACD) system to differentiate lung cancer biomarkers and identify cancer cells in the tissue-based specimen images. STUDY DESIGN: Molecular methods, including application of cancer-specific markers, may prove to be complementary to cytology diagnosis, especially when they are combined with CACD system for biomarker assessment. We trained a novel CACD system to recognize expression of the cancer biomarkers histone H2AX in lung cancer cells and then tested the accuracy of this system to distinguish resected lung cancer from preneoplastic and normal tissues. The major characteristics of CACD algorithms is to adapt detection parameters according to cellular image contents. Our newly developed wavelet transform is able to adaptively select different resolution and orientation features based on image content requirements. RESULTS: Visual, statistical and quantitative results as CACD performance evaluation are presented in this paper. CONCLUSION: The presented algorithms and CACD system for cellular feature enhancement, segmentation and classification are very important in distinguishing benign and malignant lesions.

Protein microarrays and quantum dot probes for early cancer detection.

We describe here a novel approach for detection of cancer markers using quantum dot protein microarrays. Both relatively new technologies; quantum dots and protein microarrays, offer very unique features that together allow detection of cancer markers in biological specimens (serum, plasma, body fluids) at pg/ml concentration. Quantum dots offer remarkable photostability and brightness. They do not exhibit photobleaching common to organic fluorophores. Moreover, the high emission amplitude for QDs results in a marked improvement in the signal to noise ratio of the final image. Protein microarrays allow highly parallel quantitation of specific proteins in a rapid, low-cost and low sample volume format. Furthermore the multiplexed assay enables detection of many proteins at once in one sample, making it a powerful tool for biomarker analysis and early cancer diagnostics. In a series of multiplexing experiments we investigated ability of the platform to detect six different cytokines in protein solution. We were able to detect TNF-alpha, IL-8, IL-6, MIP-1beta, IL-13 and IL-1beta down to picomolar concentration, demonstrating high sensitivity of the investigated detection system. We have also constructed and investigated two different models of quantum dot probes. One by conjugation of nanocrystals to antibody specific to the selected marker--IL-10, and the second by use of streptavidin coated quantum dots and biotinylated detector antibody. Comparison of those two models showed better performance of streptavidin QD-biotinylated detector antibody model. Data quantitated using custom designed computer program (CDAS) show that proposed methodology allows monitoring of changes in biomarker concentration in physiological range.

Computer-aided mass detection based on ipsilateral multiview mammograms.

RATIONALE AND OBJECTIVES: Recent reports on advances in computer-aided detection (CAD) indicate that current schemes miss early-stage breast cancers and result in a relatively large false-positive detection rate in order to achieve a high sensitivity rate for mass detection. This paper is inspired by the interpretation procedure from mammographers. The abnormal diagnosis can be derived from multiple views but is not available through single-view image analysis. MATERIALS AND METHODS: A new multiview CAD system for early-stage breast cancer detection, which is based on modifying the optimized CAD algorithms from our prior single-view CAD system for constructing an adaptive ipsilateral multiview concurrent CAD system, is presented in this paper. The selection and design for the training and testing ipsilateral multiview mammogram databases are described here. RESULTS: The performance evaluation of the developed ipsilateral multiview CAD system using free-response receiver operating characteristic analysis and computerized receiver operating characteristic experiments are presented. The results indicated that the proposed multiview CAD system is significantly superior to the single-view CAD systems based on statistically standard P-values. CONCLUSION: This paper addresses a very important and timely project. It is related to two main problems regarding the development of breast cancer detection and diagnosis: early-stage detection and diagnosis of breast cancer with digital mammogram, and overall improvement of CAD system performance for clinical implementation. In order to improve the efficacy, accuracy, and efficiency of the current CAD scheme, an entirely new class of CAD method is required. This paper is unique in that a comprehensive and state-of-the-art approach is proposed for the CAD scheme of digital mammography. From the design aspect of the CAD scheme, the proposed ipsilateral multiview CAD method is innovative and quite different from current single-view CAD methods.

Ipsilateral-mammogram computer-aided detection of breast cancer.

In this paper, an ipsilateral multi-view computer-aided detection (CAD) scheme is presented for mass detection in digital mammograms by exploiting correlative information of suspicious lesions between mammograms of the same breast. After nonlinear tree-structured filtering for image noise suppression, two wavelet-based methods, directional wavelet transform and tree-structured wavelet transform for image enhancement, and adaptive fuzzy C-means algorithm for segmentation are employed on each mammograms of the same breast, respectively, concurrent analysis is developed for iterative analysis of ipsilateral multi-view mammograms by inter-projective feature matching analysis. A supervised artificial neural network is developed as a classifier, in which the back-propagation algorithm combined with Kalman filtering is used as training algorithm, and free-response receiver operating characteristic analysis is used to test the performance of the developed unilateral CAD system. Performance comparison has been conducted between the final ipsilateral multi-view CAD system and our previously developed single-mammogram-based CAD system. The study results demonstrate the advantages of ipsilateral multi-view CAD method combined with concurrent analysis over current single-view CAD system on false positive reduction.