2.1.

System Description

The LCI system and probe have been described previously and are shown schematically in Fig. 1 .¹² Briefly, the LCI system consisted of a nonreciprocal fiber optic Michelson interferometer. A broadband super luminescent diode (SLD) centered at $1310\mathrm{nm}$ with a full width at half maximum bandwidth of $50\mathrm{nm}$ (Optiphase, Inc., Van Nuys, California) was used as a light source. The axial resolution was $\sim 15\mu \mathrm{m}$ in air, or $11\mu \mathrm{m}$ in tissue $(n=1.4)$ . Light from the source was transmitted through the first output port of a circulator and an $80∕20$ splitter, which directed approximately $750\mu \mathrm{W}$ to the sample arm. LCI depth scans (A-lines) were obtained at a rate of $40\mathrm{Hz}$ and the path length in the reference arm was scanned by illuminating a retroreflector mounted on a galvanometer-driven lever arm (Model 6220, Cambridge Technology, Lexington, Massachusetts). Light from the sample and reference arms were recombined and directed toward a polarization beamsplitter and two photodetectors, enabling polarization diverse detection. Shot noise limited detection was achieved with a maximum signal-to-noise ratio (SNR) of $101\mathrm{dB}$ .

Fig. 1

Schematic of the LCI biopsy guidance instrument. A super luminescent diode (SLD) is sent through a circulator (Circ) and an 80/20 splitter. The reference arm optical delay line (ODL) consists of a retroreflector mounted on a galvanometer-driven lever arm. The LCI-FNA probe consists of a single mode fiber inserted through a hub connecting the syringe and the needle. An optical connector attaches the LCI-FNA probe to the sample arm of the interferometer.

The optical probe consisted of a single mode optical fiber inserted through the bore of a 23 gauge ( $\sim 570\text{-}\mu \mathrm{m}$ outside diameter) FNA needle. No focusing lens was used. The needle was attached to a regular syringe through a hub (Model 54501, Inrad, Northvale, New Jersey). The syringe was held within a FNA biopsy gun. The fiber probe was designed to be simple and therefore inexpensive. Because the fiber core aperture was always at the needle tip, there was no uncertainty regarding the probe location, and the interrogated tissue was directly in front of the needle location. Although the fiber probe was housed within a FNA needle, no tissue aspirates were collected during the measurements.

2.2.

Experimental Protocol

Excised surgical specimens were collected, stored in 10% phosphate buffered saline and data was collected at $37°\mathrm{C}$ within $24\mathrm{h}$ of collection. The needle and FNA gun were secured onto a vertical translation stage as shown in Fig. 2 . During imaging, samples were placed flat on a piece of corrugated cardboard within a Petri dish and positioned under the needle probe. The needle was lowered onto the sample until the fiber surface came into contact with the sample. Ten consecutive A-lines were collected at each site. Following imaging, the needle was raised, and the needle location was marked with India ink. The samples were then fixed in formalin. Histologic sections were obtained and stained with hematoxylin and eosin.

Fig. 2

FNA gun mounted with needle and positioned above sample.

Histology slides were read by a pathologist who was blinded to the LCI data. Slides were randomly ordered to avoid bias from reading samples from the same patient consecutively. Histology samples were grouped into two critical cases for this application—adipose and fibroglandular tissue types. Fibroglandular tissues included benign fibrous parenchyma, adenocarcinoma, and ductal carcinoma in situ (DCIS) tissue types. Only homogeneous samples classified as pure adipose or fibroglandular tissue were included for parameter extraction and algorithm development/classification. Samples with significant heterogeneity in the image field as defined by the pathologist or samples where no ink was visible on histology were excluded. Heterogeneous samples were defined as tissues where the ratio of major to minor tissue type was approximately less than 3:1 within $1\mathrm{mm}$ of the ink mark.

2.3.

Parameter Extraction

For each sample, 10 consecutive A-lines were acquired. Signal parameters were extracted for each A-line, and the mean value for each parameter was used to represent the sample. Each parameter was calculated using an automated MATLAB script without the need for additional user input other than the sample file. Prior to parameter extraction, the raw LCI interferogram data was converted to depth-dependent reflectivity profiles in the standard fashion.¹² The signal was transformed using discrete Fourier transform (DFT), bandpass filtered, frequency shifted to zero, and inverse transformed. The resulting linear intensity values were then converted to decibel scale by $20{\log }_{10}$ multiplication.

2.3.4.

Spatial frequency content

Scattering center distribution, representing the distance between scatterers, may be evaluated by analyzing the spatial frequency components of the signal. The power spectrum of the signal can be interpreted as the signal energy within spatial frequency windows, and the unique signature from different tissue types was recently described as a method for differentiating human breast tissue.²¹ The spatial frequency parameter was computed in the following manner. First, as with the computation of the standard deviation, the linear regression was conducted and the residual was used for subsequent processing. Next, the dc component was removed by mean subtraction. Data outside the start and end index were set to zero. The resulting signal was zero mean, with components that fluctuated with varying frequency content depending on tissue type. The DFT was then computed. The spatial frequency parameter was then defined by integrating the magnitude of the spatial frequency content over a particular window band. The window was defined by calculating the average DFTs for the entire training set and observing where the adipose and fibroglandular tissue samples differed. A zoomed portion of the mean DFTs for the training set are shown in Fig. 3 . The vertical lines represent the width of the integration window.

Fig. 3

Average power spectrum data from LCI depth scans over the entire training set that show a region of difference between adipose and fibroglandular tissues types. The integration window (green lines) represents the area over which the spatial frequency content parameter is generated. (Color online only.)

2.4.

Algorithm Model

A multivariate Gaussian model was used for classification. The data set was randomly split into training and validation sets. A pooled estimate of the covariance matrix was used for the training set. The result of the model was an equation for each class that defined the probability that any new set of parameters fell within that class. Prospective analysis was then performed on the validation set. Classification was carried out by extracting parameters for each test sample, calculating the probability of falling within a particular class, and then assigning classification based on the highest probability.

2.5.

Intrasample Variability

To test the intrasample variability of the device and algorithm, an additional experiment was conducted using another data set. The needle probe was lowered onto each sample and a 10–A-line acquisition was performed. The needle was then raised off of the sample using the vertical translation stage and relowered back onto the sample for an additional 10–A-line acquisition. This process was repeated 10 times so that each sample had 10 data sets of 10 A-lines each all from the same exact location. After the 10 measurement, the needle was raised and the sample was marked with India ink and sent for histology sectioning and staining. Each set of 10 A-lines was processed in the same manner as the earlier experiments so that a single set of extracted parameters characterized each set of A-lines. The samples were then classified using the multivariate Gaussian model. The result was a set of 10 classifications from the same sample at the same location. The intrasample variability was defined as the percentage of misclassified measurements within a particular sample.

2.6.

Statistical Analysis

The accuracy of LCI for classifying breast tissue type was assessed by comparing the predicted tissue type to the gold standard histopathologic classification. All data processing and parameter extraction were done within MATLAB. Each parameter is listed as $\mu \pm \sigma$ , where $\mu$ is the mean, and $\sigma$ is the standard deviation. The $p$ -values were calculated using a two-sided unpaired $t$ -test to determine if the difference in sample means between parameters were statistically significant, and 95% confidence intervals (CI) are also reported.

3.1.

Training Set

The results from the training set are listed in Table 1 . As the table demonstrates, each parameter has a significant $p$ -value. The average magnitude of the slope parameter was higher for fibroglandular tissue, which indicates a higher scattering coefficient for fibroglandular breast tissue compared with adipose tissue. The mean standard deviation was higher for adipose tissue as a result of the signal variation resulting from refractive index fluctuations. The spatial frequency parameter had more energy for the adipose samples within the integrated window band. There was no spatial frequency region where the fibroglandular tissue had higher energy as was seen at higher spatial frequencies in Zysk ²¹ This could be due to differences in axial resolution ( $10\mu \mathrm{m}$ versus $2\mu \mathrm{m}$ ) because the Fourier transform resolution highly depends on spatial sampling frequency.

Table 1

Training set statistics.

Another way to represent the training data is through the use of a scatter matrix as shown in Fig. 5 . The scatter matrix plots two-dimensional scatter plots between each set of parameters and can be used to observe correlations between classification parameters. It can be seen that there is little correlation between the slope and standard deviation as well as the slope and spatial frequency parameters. In addition, the slope–standard deviation and slope–spatial frequency scatter plots show that the adipose and fibroglandular data sets fall into separate regions, making classification based on these parameters possible. The scatter plot matrix also shows that the standard deviation and spatial frequency parameters are highly correlated. This is expected as both parameters are related to the scattering strength and scatterer distribution. The correlation is higher for the adipose $(R=0.938)$ than for the fibroglandular $(R=0.780)$ tissue samples.

Fig. 5

Scatter plot representation of the entire training set data that can be used to visualize relationships between parameters. There is a high degree of correlation between the standard deviation and the spatial frequency content parameters for both the adipose (red plus sign) and fibroglandular (blue cross) tissue types. Tumor tissues are labeled in green circles and were included in the fibroglandular tissue classification. (Color online only.)

3.2.

Validation Set

The results from the validation set using all three parameters for classification are listed in Table 2 . The classification parameters show the same trends as were seen in the training set data. The classification results are listed in Table 3 . During a FNA procedure, the collection of adipose tissue is seen as a nondiagnostic result. Therefore, the correct classification of adipose tissue can be viewed as a true negative (TN), and the correct classification of fibroglandular tissue can be viewed as a true positive (TP). In this way, the sensitivity, as defined by $\mathit{TP}∕(\mathit{TP}+\mathit{FN})$ $(\mathit{FN}=\text{false}\text{negative})$ is equivalent to the accuracy of detecting fibroglandular tissue. In addition, the specificity, as defined by $\mathit{TN}∕(\mathit{TN}+\mathit{FP})$ $(\mathit{FP}=\text{false}\text{positive})$ is equivalent to the accuracy of detecting adipose tissue. The sensitivity and specificity of the validation set were 98.1% (95% CI: 89.7 to 99.9) and 82.4% (95% CI: 65.5 to 93.2), respectively. The overall accuracy was defined as the total number of correctly classified tissue samples regardless of tissue type. With 86 (34 adipose, 52 fibroglandular) samples in the validation set, the overall accuracy was 91.9% (95% CI: 84.0 to 96.7). CIs were calculated using the normal approximation to the binomial distribution.²⁴ The one misclassified sample from the fibroglandular validation set was an adenocarcinoma case. The other 8 of 9 tumor cases were correctly classified as fibroglandular tissues.

Table 2

Validation set statistics.

Table 3

Classification results.

These results use all three classification parameters as previously described. To determine whether or not the three-parameter model was statistically better than simply using the slope and standard deviation parameters,¹² it was necessary to look at a truth table describing the differences between the two models. The overall classification results using only the slope and standard deviation parameters are shown in Table 3. The sensitivity and specificity were 80.8% (67.5 to 90.4) and 82.4% (65.5 to 93.2), respectively. Using only the two-parameter model, four tumor cases, all adenocarcinoma, were misclassified as adipose tissue. A truth table to quantify the differences between the two models is shown in Table 4 . In Table 4, a +/+ cell indicates that both the two-parameter and the three-parameter models correctly classified the sample. A +/− cell indicates that the two-parameter model correctly classified a sample, whereas, the three-parameter model misclassified a sample. Similarly, a −/+ cell indicates that the three-parameter model classified the sample correctly when the two-parameter model misclassified the sample, and a −/− cell indicates that both models misclassified the samples. The table shows that there are nine cases where the three-parameter model classified a fibroglandular sample correctly when the two-parameter model misclassified the sample, and no cases with the reverse scenario. The associated $p$ -value is calculated using McNemar’s test for correlated proportions and shows that there is a statistically significant difference between the two- and three-parameter models in terms of fibroglandular tissue classification. No statistical difference was observed for the adipose case.

Table 4

Model comparison.

Because the standard deviation parameter is calculated from the slope-subtracted LCI signal, errors in the slope calculation could result in an artificially high standard deviation measurement. To test the effect this would have on our classification, we simulated errors in the slope by randomly modifying the slope parameter +/− 5, 10, and 20% of its nominal value. The standard deviation and spatial frequency content parameters were then recalculated using the modified slope value. The resulting classification was compared to the nominal value result using McNemar’s test as previously described. There was no significant difference in the classification results for either the adipose or fibroglandular tissue type. For the maximum error of +/− 20%, the sensitivity was 96.2% (95% CI: 86.8 to 99.5) and the specificity was 76.5% (95% CI: 58.8 to 89.3).

3.3.

Intrasample Variability

Data to test intrasample variability were collected and analyzed from a separate set of 14 samples from 6 patients (6 adipose, 8 fibroglandular). The average intrasample variability was 18.3% (9.5 to 30.4) for adipose and 1.3% (0.03 to 6.8) for the fibroglandular tissue samples. The overall Cohen’s $\kappa$ statistic was 0.821 (0.725 to 0.981). The number of errors for each 10–A-line set was as follows: adipose [1 0 0 0 7 3] and fibroglandular [0 0 0 1 0 0 0 0]. The one outlier sample within the adipose data set ( $7∕10$ error rate) was due to low signal content, which tended to reduce the spatial frequency content parameter and shift the probability toward the fibroglandular tissue type. If the outlier were be removed, the adipose intrasample variability rate would become 8.0% (2.2 to 19.2) and the $\kappa$ value would become 0.918 (0.847 to 0.988).