A number of noninvasive fiber optic optical technologies are under development for real-time diagnosis of neoplasia. We investigate how the light scattering properties of cervical cells are affected by changes in nuclear morphology, DNA content, and chromatin texture, which occur during neoplastic progression. We used a Cyto-Savant computer-assisted image analysis system to acquire quantitative nuclear features measurements from 122 Feulgen-thionin-stained histopathologic sections of cervical tissue. A subset of the measured nuclear features was incorporated into a finite-difference time-domain (FDTD) model of cellular light scattering. The magnitude and angular distribution of scattered light was calculated for cervical cells as a function of pathologic grade. The nuclear atypia strongly affected light scattering properties. The increased size and elevated DNA content of nuclei in high-grade lesions caused the most significant changes in scattering intensity. The spatial dimensions of chromatin texture features and the amplitude of refractive index fluctuations within the nucleus impacted both the angular distribution of scattering angles and the total amount of scattered light. Cellular scattering is sensitive to changes in nuclear morphology that accompany neoplastic progression. Understanding the quantitative relationships between nuclear features and scattering properties will aid in the development of noninvasive optical technologies for detection of precancerous conditions. © 2003 Society of Photo-Optical Instrumentation Engineers.

Background: Results from preclinical studies have suggested that the organosulfur compound anethole dithiolethione (ADT) may be an effective chemopreventive agent for lung cancer. We conducted a phase IIb study to determine the effects of ADT in smokers with bronchial dysplasia. Methods: One hundred twelve current and former smokers with a smoking history of at least 30 pack-years and at least one site of bronchial dysplasia identified by an autofluorescence bronchoscopy-directed biopsy were randomly assigned to receive placebo or ADT at 25 mg orally thrice daily for 6 months. Each subject then underwent a follow-up bronchoscopy-directed biopsy. We used changes in histopathologic grade and nuclear morphometry index (MI) as the primary and secondary end point biomarkers, respectively. Chi-square tests with continuity correction were used to compare response rates on a lesion- and person-specific basis between the two study groups. All statistical tests were two-sided. Results: One hundred one subjects had a follow-up bronchoscopy. In the lesion-specific analysis, progression rate of pre-existing dysplastic lesions by two or more grades and/or the appearance of new lesions was statistically significantly lower in the ADT group (8%) than in the placebo group (17%) (P<.001; difference = 9%, 95% confidence interval [CI] = 4% to 15%). In the person-specific analysis, the disease progression rate was statistically significantly lower in the ADT group (32%) than in the placebo group (59%) (P = .013; difference = 27%, 95% CI = 6% to 48%). The two treatment groups did not differ statistically significantly in terms of nuclear MI. Among individuals with an abnormal nuclear MI before treatment (29 in the ADT group and 25 in the placebo group), the progression rate in the ADT group (41%) was substantially lower than that in the placebo group (60%), although the difference was not statistically significant (P = .28; difference = 19%, 95% CI = –11% to 49%). Adverse events were mostly minor gastrointestinal symptoms that resolved with dose reduction or discontinuation of the medication. Conclusion: Our results suggest that, in smokers, ADT is a potentially efficacious chemoprevention agent for lung cancer.

Oral cancer is one of the most commonly diagnosed cancers worldwide. Disease is often diagnosed at later stages, which is associated with a poor 5‐year survival rate and a high rate of local recurrence. MicroRNAs (miRNAs), a group of small, noncoding RNAs, can be isolated from blood serum samples and have demonstrated utility as biomarkers in multiple cancer types. The aim of this study was to examine the expression profiles of circulating miRNAs in the serum of patients with high‐risk oral lesions (HRLs; oral cancer or carcinoma in situ) and to explore their utility as potential oral cancer biomarkers. Global serum miRNA profiles were generated using quantitative PCR method from 1) patients diagnosed with HRLs and undergoing intent‐to‐cure surgical treatment (N = 30) and 2) a demographically matched, noncancer control group (N = 26). We next honed our list of serum miRNAs associated with disease by reducing the effects of interpatient variability; we compared serum miRNA profiles from samples taken both before and after tumor resections (N = 10). Based on these analyses, fifteen miRNAs were significantly upregulated and five were significantly downregulated based on presence of disease (minimum fold‐change >2 in at least 50% of samples, P < 0.05, permutation). Five of these miRNAs (miR‐16, let‐7b, miR‐338‐3p, miR‐223, and miR‐29a) yielded an area under the ROC curve (AUC) >0.8, suggesting utility as noninvasive biomarkers for detection of oral cancer or high‐grade lesions. Combining these serum miRNA profiles with other screening techniques could greatly improve the sensitivity in oral cancer detection.

The finite-difference time-domain (FDTD) method provides a flexible approach to studying the scattering that arises from arbitrarily inhomogeneous structures. We implemented a three-dimensional FDTD program code to model light scattering from biological cells. The perfectly matched layer (PML) boundary condition has been used to terminate the FDTD computational grid. We investigated differences in angle-dependent scattering properties of normal and dysplastic cervical cells. Specifically, the scattering patterns and phase functions have been computed for normal and dysplastic cervical cells at three different epithelial depths, namely, basal/parabasal, intermediate, and superficial. Construction of cervical cells within the FDTD computational grid is based on morphological and chromatin texture features obtained from quantitative histopathology. The results show that angle-dependent scattering characteristics are different not only for normal and dysplastic cells but also for cells at different epithelial depths. The calculated scattering cross-sections are significantly greater for dysplastic cells. The scattering cross-sections of cells at different depths indicate that scattering decreases in going from the superficial layer to the intermediate layer, but then increases in the basal/parabasal layer. This trend for epithelial cell scattering has also been observed in confocal images of ex vivo cervical tissue.

This paper addresses the problem of quantifying biomarkers in multi-stained tissues based on the color and spatial information of microscopy images of the tissue. A deep learning-based method that can automatically localize and quantify the regions expressing biomarker(s) in any selected area on a whole slide image is proposed. The deep learning network, which we refer to as Whole Image (WI)-Net, is a fully convolutional network whose input is the true RGB color image of a tissue and output is a map showing the locations of each biomarker. The WI-Net relies on a different network, Nuclei (N)-Net, which is a convolutional neural network that classifies each nucleus separately according to the biomarker(s) it expresses. In this study, images of immunohistochemistry (IHC)-stained slides were collected and used. Images of nuclei (4679 RGB images) were manually labeled based on the expressing biomarkers in each nucleus (as p16 positive, Ki-67 positive, p16 and Ki-67 positive, p16 and Ki-67 negative). The labeled nuclei images were used to train the N-Net (obtaining an accuracy of 92% in a test set). The trained N-Net was then extended to WI-Net that generated a map of all biomarkers in any selected sub-image of the whole slide image acquired by the scanner (instead of classifying every nucleus image). The results of our method compare well with the manual labeling by humans (average F-score of 0.96). In addition, we carried a layer-based immunohistochemical analysis of cervical epithelium, and showed that our method can be used by pathologists to differentiate between different grades of cervical intraepithelial neoplasia by quantitatively assessing the percentage of proliferating cells in the different layers of HPV positive lesions.