2.1.

Subjects and Sample Collection

This study included patients in three groups: 1. patients with severe dysplasia, carcinoma in situ (CIS) and squamous cell carcinoma (SCC) lesions (denoted abnormal set), 2. patients with normal mucosa (denoted normal set), and 3. patients with confounding lesions (denoted confounder set). The abnormal set involved participants in the ongoing NIH/NIDCR-funded oral cancer prediction longitudinal (OCPL) study at the British Columbia Cancer Agency in Vancouver, Canada.⁸ Normal control samples and confounders came from community clinics of the Oral Mucosal Disease Program at the University of British Columbia. All samples were collected between November 2004 and November 2008 through the application of a stiff brush rubbed against the lesion in the oral cavity (brushing). The study was approved by the Institutional Review Board of the BC Cancer Agency and University of British Columbia. Informed consent was obtained from all subjects. A total of 369 cytological samples (from 369 individuals) were analyzed: (1) 148 samples from pathology-proven sites of SCC, CIS or severe dysplasia (denoted abnormal set); (2) 77 samples from sites with inflammation, infection, or trauma, either biopsy-proven or clinically confirmed as such by an Oral Pathology Specialist in lesions at a three-month follow-up examination (denoted confounder set); and (3) 144 samples from normal sites (denoted normal set).

In a conventional dental practice, the prevalence of abnormal lesions is quite low ($\sim 0.5\%$) in most settings,⁹ the clinician performing the brushing may not always recognize the area of most severe change and may consequently not brush exactly the same area an experienced oral specialist would target. In the cervical screening programs the sample is frequently (5% to 20%) not taken from the targeted area at most risk of being transformed.^10,11 Thus, to more realistically represent these conditions, approximately 1 cm of clinically normal oral surface around the lesion as well as the lesion itself was intentionally brushed to reduce the dependency of the system on perfectly targeted brushing samples.

Exfoliated cells were collected by brushing the mucosal surface with a soft interdental brush (Innovatek cytology brush 4201-CB8B, The Stevens Company), which had been manually curved at the end prior to use. After 10 to 15 strokes of the brush, the collected cells and the brush were put into a 1.5 ml cryovial containing 800 ul of Preservcyte (Hologic Inc.) and stored at 4 ºC until analysis.

2.2.

QTP Imaging System for Quantitative Cytology

The oral brushings were cytospun down onto slides and stained with a modified Feulgen-Thionin reaction,¹² which quantitatively stains DNA such that the light absorbed at each location in the nucleus is directly proportional to the amount of DNA at that location. Stained slides were scanned using a modified version of the Cyto-Savant automated quantitative system (Integrative Oncology, BC Cancer Agency) currently used for cervical and sputum screening.¹³ The software used in the Cyto-Savant system is specifically designed for fully automatic slide scanning and collection of correctly segmented and focused images of all objects on the slide (stained nuclei and debris).^13,14 Essentially this is a fully automated microscopy system, which is capable of loading individual slides from a slide box, automatically finding and focusing individual objects on the slide, acquiring images of the objects found, and segmenting objects into nuclei-like objects. The system calculates $\sim 100+$ features for each object, passes these features through a multi-level binary classification tree to recognize and differentiate well-segmented, in focus single epithelial cells from overlapping cells, cell clusters, debris, white blood cells, etc. to finally end up with a collection of epithelial cell images and data for each slide. The imaging system performance characteristics follow the recommendation of the European Society of Analytical Cellular Pathology for ploidy analysis.¹⁵ A strict quality control process involving measurements of standard targets, power measurements, and illumination stability evaluated using statistical quality control processes is implemented to ensure the linearity and repeatability of the system for each analysis.¹⁶

The system used an illumination wavelength ($600\pm 5\mathrm{nm}$) corresponding to the absorption peak of the Thionin stain. The effective pixel sampling within the plane of the sample was 0.34 μm and the effective pixel sampling area was $0.116$ μm². Each object (cell or debris) scanned on a slide had 110 features calculated and stored. Each object was subjected to a cell recognition algorithm (originally trained for cervical cell recognition)¹⁷ to differentiate cells from debris with a subsequent quick review by an experienced cytotechnologist to ensure that only intact well-focused single cells were used in all subsequent analyses. The review typically takes less than 5 min to perform per slide as the automated algorithms are $\sim 99\%$ accurate.¹⁷ Various subsets of these cell and slide data were used to generate the algorithms described in this study.

2.3.

Selection of Training Set

Samples were divided into both training and test sets. The training set consisted of a random selection of 120 of the 144 normal samples and 109 of the 148 abnormal samples in addition to all 77 of the confounder samples. The remaining 24 normal samples and 39 abnormal samples were set aside as the test set to evaluate the performance of the generated algorithms.

A random selection of $\sim 10,000$ cells from the over 175,000 cells making up all the cells in only the normal and abnormal samples in the training set was used to train the cell by cell classifiers and to determine the thresholds for the frequency of cells displaying characteristics associated with the abnormal samples. These thresholds were then applied to all the cells in both the training and test samples.

2.4.

Data Analysis

The system measures two major aspects of all the cells it detects, the amount of DNA within a cell’s nucleus and how that DNA is distributed within the nucleus.¹⁸ A cell’s DNA index is a measure of the amount of chromosomal material (total DNA) within the cell: a value of 1 indicates the normal complement of DNA (46 chromosomes in a G0/G1 cell, or 2.9 billion base pairs); a value of 2 indicates twice the usual amount of DNA or 5.8 billion base pairs. It should be noted that a value of 1 does not necessarily indicate a normal complement of chromosomes, only the presence of $\sim 3$ billion base pairs. Also the uncertainty in the measurement ($<5\%$, or $\sim 150$ Mega bases in an imaging cytometry system meeting the guidelines of the ESACP)¹⁵ means that almost a full chromosome could be missing without being detected. Cell data in the training set were divided into boxes within discrete DNA index ranges: less than 0.9, 0.9 to 1.1, 1.1 to 1.35, 1.35 to 1.6, 1.6 to 1.95, 1.95 to 2.15, and 2.15 and higher. These DNA content ranges are a superset of those used by Bradley et al.¹⁹ and are similar to those used in earlier studies for the classification and grouping of cells in cervical smears and sputum samples.^20,21

The second aspect measured by the system involved features related to how the DNA was distributed within the nucleus, frequently referred to as nuclear texture analysis. These measures capture genetic and epigenetic alterations, which are not manifest as large base pair gains or losses but result in changes in the spatial arrangement and packaging of chromosomal strands within the nucleus. To recognize which and how large these measured spatial arrangement changes are needed to differentiate between normal and abnormal cells, for each DNA index range a separate cell by cell discriminate function analysis (DFA) was performed. The DFAs so generated were used to differentiate cells in normal samples from cells from abnormal samples in that DNA index range (see supplemental material, Appendix A). These DNA index boxes and DFAs were applied to all samples, and a calculation was made of the frequencies of cells in each category defined by the combination of DNA quantity and DFA. In this fashion 17 cell categories were defined (see Fig. 1).

Fig. 1

Definition of 17 cell categories: (a) Flow diagram of nuclei classification by DNA index thresholds and discriminate function analysis (DFA); and (b) application of this process to the nuclei in a CIS sample. In (a) all cells get divided into one of seven sets depending upon the amount of DNA detected in each cell (seven arrows leading away from all cells at top of figure). The cells in each of these sets gets further subdivided into two to three groups by a linear discriminate function unique to that set. See supplemental material Appendix A for a detailed description of the cell classification process. In (b) this process is applied to all the cells in a CIS sample, i.e. the cell classification process of initially separating cells into seven sets based upon their DNA amount, followed by the application of a DF specific to the individual DNA amount sets is applied so that each cell is assigned a DF score. The scatter plot of the cells in the CIS sample as a function of DNA amount and DF score is shown.

2.5.

Statistics

The overall approach used to differentiate normal samples from abnormal samples was to use the normal set to define ranges that represent normal limits; samples with characteristics outside these values were classified as “not” normal samples. The thresholds on the frequency of cells in these categories were used to separate normal samples from abnormal samples in the training set. $P$ values for the separation between the categories in the training sets were determined for all cell categories and scatter plots were made of the cell category frequencies that had the largest separation. The appropriate cutoffs for sample classification were heuristically evaluated from these graphs. Once these differentiating thresholds were set, the detection algorithms were run on all the samples. All statistical calculations, nonparametric Mann-Whitney U tests and Komogorov-Smirnov tests and scatter plots were produced using Statistica version 8.0 (StatSoft Inc., Tulsa, Oklahoma). A $P$ value of less than 0.05 was considered to be statistically significant and a value of less than 0.005 to be highly significant.

6.1.

Comparison with Other Oral Cytology Assessment Approaches

Genetic instability is a central hallmark of cancer.^22–25 Mutations, LOH, and aneuploidy can all play a role in the uncontrolled growth of cancer. Detecting these changes to the genome is one important way to follow the progression of cancer and to catch clinical cancer (cancer and its precursor lesions, which are at sufficient risk of becoming malignant that they are treated clinically, usually surgically) early. Aneuploidy occurs when a cell has an abnormal chromosomal content or number. This can arise from gain or loss of whole chromosomes or by gross alteration of one or more chromosomes through deletions, translocations, end to end fusions, or other events.^26,27 Polysomes of chromosomes 7, 9, and 17 increase as oral epithelial tissue progresses from low risk lesions (hyperplasia) to high risk (severe dysplasia).^28,29 There is a long history for the use of DNA amount measurements as detectors of malignancy. Several research groups have attempted to quantify the large scale genetic instability (aneuploidy) that develops with oral tumorgenesis as a means to assess cancer risk. Using cells dissociated from formalin-fixed paraffin-embedded tissue from 45 primary oral cancer, Diwakar et al. looked at not only the ploidy status of the oral tumors, but the consistency of the ploidy classification across the tumor.³⁰ Their work demonstrated that a definition of nondiploid would correctly identify 93.3% (42 out of the 45) of oral cancers. Researchers from Dűsseldorf have combined clinical cytological examination and DNA image cytometry of disaggregated tissue to differentiate high risk oral lesions (severe dysplasia, CIS, cancer) from normal tissue in one study³¹ and in a separate study to assess the risk of cancer in confounding lichen planus lesions.³² Using this combined cytological/cytometric method, they reported a very encouraging sensitivity of 100% and a specificity of 97.4% for high risk lesions that were either DNA-aneuploidy, or cytologically suspicious.³¹ Furthermore, only 2 of 56 lichen planus were positive, and these were shown to have an SCC component in complete agreement with their histological findings. While this approach showed high sensitivity and specificity, the heavily manual aspects (pathology expertise) limits the high throughput needed to be clinically relevant. In both of these studies, oral epithelial cells were obtained by targeted brushing, then stained, and graded by a pathologist. The DNA level was then quantified on those samples deemed visually suspect using computer-based image cytometry. In addition when Pektas et al. tried to duplicate this approach, using a manual cytometric DNA image analysis system coupled to expert clinical cytological assessment, only 16.7% of malignant oral lesion samples collected by cytobrushing were classified as aneuploid,³³ indicating that the sensitivity of this form of combined analysis can be quite variable.

In contrast, our high throughput automated image processing system requires very little human interaction, making it possible for a single operator to assess many samples daily while maintaining a performance similar to that reported by other groups that required input from skilled clinical specialists to perform the analysis.

6.2.

Use of DNA Organization to Assist in Sample Classification

To maximize the diagnostic information available beyond that available in the DNA index from the cytological samples, algorithms were developed that differentiated between normal cycling cells and cells altered as part of the neoplastic process as described previously. This approach, which is based upon the direct detection of genetic and epigenetic alterations in the DNA within nuclei, has been accomplished through the use of measures of nuclear DNA organization as recorded through image texture features. Specifically, these algorithms attempted to glean such further discriminating ability from the measured cells by calculating linear discriminate functions (LDF) for each DNA range to differentiate between cycling and aneuploid cells. Similar to most epithelial tissue, the oral cavity has a high enough tissue renewal rate that the presence of cells in non G0/G1 states is not a rare event. As part of the neoplastic development process, cells can acquire inheritable alterations, which alter their DNA content such that it is discernibly more than that of G2/M cells, making them easily identified as abnormal cells. It is the altered cells without such frank DNA changes that are challenging to recognize. If one can differentiate between cycling cells and true aneuploid (abnormal) cells increased sample classification is possible. Our LDFs made use of five to seven features selected from the over 100 available; generally the selected features contained one or two nuclear shape features with the rest being texture features that quantify the appearance of the DNA within the nuclei. Specifically these features describe DNA organization deregulation (hyperchromasia) associated with molecular and genetic alterations involved with the neoplastic process at the cellular level.

This is the first time that nuclear hyperchromatism as quantified by DNA texture measures has been integrated into the interpretation of oral cytology. Others have shown that increased proportion of heterochromatin condensation is a high-risk nuclear characteristic for cancer.²⁸ Our previous quantitative histology work showed that the quantification of the many facets of heterochromatin condensation and hyperchromatism is predictive of cancer development and is strongly associated with genetic level alterations within oral tissue.^5–7,34 Thus it was not unexpected that the training process for the LDFs resulted in the selection of features that are known to be measures of nuclear hyperchromasia.

This approach can differentiate between oral brushing samples from normal and abnormal sites even when the sampling is not directly targeted to only the area that an experienced oral specialist would select. It is robust to even include cells from the immediate surrounding tissue, such as those a clinician who is infrequently exposed to oral lesions might select. For brushing cytology samples collected only from the area selected by an experienced oral specialist and hence not diluted by the surrounding normal cells one would reasonably expect the system to perform even better.

The performance of the final algorithm while validated on the normal and abnormal test sets has not been validated on a confounder test set. This work needs to be reproduced in an independent test set. Such studies would be strengthened if they occurred in dental practices with dental health professionals as screeners. An efficient way to evaluate this next step would be to use QC in high-risk community settings, where cancer and dysplasia rates are known to be elevated, such as in communities characterized by high frequencies of habit usage and low socioeconomic status. Confounders are also high in such settings making cancer detection more difficult but making such a setting an excellent place for validation. An ongoing study is running in this setting to further evaluate the value of QC. Initial results on a subset of 20 cases of these confounding cases (confounder test set) achieved essentially the same results as shown in Table 2 for Algorithm 9.

6.3.

Combining Macroscopic and Microscopic Imaging

An important aspect of this study is that it demonstrates that the layering of clinical evaluation of the oral cavity using wide-field white light and/or FV with image cytometry of approximately targeted brushings can differentiate reactive and inflammatory conditions from true at-risk tissue (Fig. 2). In a clinical practice setting one initially uses a wide-field examination (conventional white light and/or fluorescence visualization) followed by a point sampling methodology (currently biopsy). The technology introduced here is a bridge in that it is not an invasive biopsy procedure but does acquire some cellular material for interrogation. The morbidity associated with brushing is minimal. As such it will be better tolerated in the dental practice setting and is eminently amenable to practice as a high throughput low-cost platform. This is exactly how this same platform is being used for cervical screening in resource-limited settings such as China³⁵ where it has been used to screen more than 400,000 women.

A separate but related benefit associated with QC is that it is readily scalable so it can handle a potential increase in referral of confounding lesions such as might occur with the introduction of a new wide field screening methodology such as FV. A stratification of the results presented here by FV status showed no difference in the ability of the QC to detect at risk tissue appropriately in the absence or presence of fluorescence loss ($P=1.0$). These data support the potential utility of the coupled use of FV and automated image cytometry for the detection of oral cancer. As such it is not intended to replace biopsy for the obvious neoplastic high-risk tissue. We envisage its primary usage in allowing dentists to query if the ambiguous lesion is at-risk. The data suggest that QC could reduce by more than 85% the number of lesions required to go forward to further assessment by biopsy.

In summary, opportunistic screening that takes place within a routine dental check-up is widely considered the most cost-effective screening strategy. However, correctly identifying and classifying oral lesions, especially when inflamed, is difficult even for trained pathologists.³⁶ Our data suggest that a high-throughput automated cytological image analysis system, based on targeted brushing of suspect lesions, could be an efficient and effective second step in a comprehensive screening program, directing the high risk patient populations to appropriate care while not necessitating a large number of biopsies. Such an approach could facilitate the widespread use of opportunistic screening to effectively manage oral cancer.