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.

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.

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.

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.

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.

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.