Optical spectral surveillance of breast tissue landscapes for detection of residual disease in breast tumor margins.

We demonstrate a strategy to "sense" the micro-morphology of a breast tumor margin over a wide field of view by creating quantitative hyperspectral maps of the tissue optical properties (absorption and scattering), where each voxel can be deconstructed to provide information on the underlying histology. Information about the underlying tissue histology is encoded in the quantitative spectral information (in the visible wavelength range), and residual carcinoma is detected as a shift in the histological landscape to one with less fat and higher glandular content. To demonstrate this strategy, fully intact, fresh lumpectomy specimens (n = 88) from 70 patients were imaged intra-operatively. The ability of spectral imaging to sense changes in histology over large imaging areas was determined using inter-patient mammographic breast density (MBD) variation in cancer-free tissues as a model system. We discovered that increased MBD was associated with higher baseline β-carotene concentrations (p = 0.066) and higher scattering coefficients (p = 0.007) as measured by spectral imaging, and a trend toward decreased adipocyte size and increased adipocyte density as measured by histological examination in BMI-matched patients. The ability of spectral imaging to detect cancer intra-operatively was demonstrated when MBD-specific breast characteristics were considered. Specifically, the ratio of β-carotene concentration to the light scattering coefficient can report on the relative amount of fat to glandular density at the tissue surface to determine positive margin status, when baseline differences in these parameters between patients with low and high MBD are taken into account by the appropriate selection of threshold values. When MBD was included as a variable a priori, the device was estimated to have a sensitivity of 74% and a specificity of 86% in detecting close or positive margins, regardless of tumor type. Superior performance was demonstrated in high MBD tissue, a population that typically has a higher percentage of involved margins

Number of false negative (FN) and false positive (FP) margins (stratified by margin histology and surgical margin status) and patients (stratified by MBD) calculated from the surgeon performance, as well as, the performance of the device.

<p>Number of false negative (FN) and false positive (FP) margins (stratified by margin histology and surgical margin status) and patients (stratified by MBD) calculated from the surgeon performance, as well as, the performance of the device.</p

Optical parameters reflect presence of residual disease.

<p>50x bicubic interpolated images of [β-carotene], <µ_s′>, and [β-carotene]/<µ_s′> from a A) negative and B) positive margin in 2 different patients with MBD-3. Sites with corresponding histopathology are highlighted with diagnoses of adipose (A), adipose plus fibroglandular (A+FG), and ductal carcinoma <i>in situ</i> (DCIS). C) eCDFs of the pixels from the representative images in panels A and B.</p

Summary of predictor variables selected by the conditional inference tree model, stratified by mammographic breast density (MBD).

<p>Summary of predictor variables selected by the conditional inference tree model, stratified by mammographic breast density (MBD).</p

Patient and tumor demographics.

<p>BMI – body mass index, IDC – invasive ductal carcinoma, DCIS – ductal carcinoma <i>in situ</i>.</p

Sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and classification accuracy (A) of the device and the surgeon.

<p>Performance within each MBD subgroup is given. *The surgeon’s performance is based on the primary specimen (and no additional shavings) taken during the first operation.</p

Relationship between optical parameters and benign breast composition with differences in breast density.

<p>A) Parameter maps from a low density margin; B) Parameter maps from a high density margin; blue indicates higher values of the corresponding variable. C) eCDFs of all measured sites from negative, neoadjuvant-naïve margins, separated by mammographic breast density. <i>P</i>-values were calculated with modified Kolmogorov-Smirnov statistics.</p

Schematic of our conceptual approach.

<p>Cartoon representing A) a “negative” tumor margin surface corresponding to a mix of normal tissues, and B) a “positive” tumor margin surface with areas of residual tumor (red) at the surface. The cumulative distribution functions in C) and D) are actual optical data from representative margins (sensitive to the relative amount of fat and fibroglandular tissue). In a negative margin (C), there is a distribution of values, which corresponds to the mixture of tissue types in benign (aka negative) margins. When malignancy is present in varying amounts (D), a shift in the optical contrast distribution is observed, due to the disruption in the tissue landscape caused by the increase in cancerous tissue and displacement of normal tissue.</p