Monte Carlo-based inverse model for calculating tissue optical properties Part II: Application to breast cancer diagnosis

Abstract

The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.

Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: a Monte-Carlo-model-based approach

“…35 The hemoglobin saturation showed a significant decrease in malignant tissues relative to that in nonmalignant (including both fibrous/benign and adipose) tissues (p<0.0001), which is likely due to the oxygen extraction of rapidly proliferating tumor cells. This property agreed with the findings from our previous studies, 17,18 and has also been reported in several previous studies using other techniques such as pO 2 measurements 36,37 and near-IR (NIR) diffuse optical spectroscopy. 33,38,39 Malignant breast tissues had an increased mean reduced scattering coefficient compared to that of nonmalignant breast tissues, which is also consistent with the findings from earlier studies published by Peters et al 14 and Ghosh et al 16 Three fluorescence components were extracted from the MCR analysis of the intrinsic fluorescence spectra.…”

“…This work is distinct from previous publications by our group 5,17,18 in that first, it reports on the application of a new fluorescence model to recover the intrinsic fluorescence and then extract the contributions of individual fluorescing components that may relate to intrinsic fluorophores in the breast; second, it compares the merits of the extracted fluorescence properties versus the tissue absorption and scattering properties for diagnosing breast cancer; and finally, it demonstrates the consistency of our model-based approach for the analysis of diagnostically useful information contained in fluorescence and diffuse reflectance spectra measured with two different instruments and probe geometries.…”

“…Details about the Monte-Carlo-based inverse model of diffuse reflectance can be found in previous publications. 17,18,27 The accuracy of the inverse model was verified using experimental tissue phantom studies. The results indicated that for phantoms with a wide range of absorption coefficients (0 to 20 cm −1 ) and reduced scattering coefficients (7 to 33 cm −1 ), optical properties could be extracted within an average error of 3% for phantoms containing hemoglobin and within 12% for phantoms containing nigrosin.…”

“…Both studies showed that malignant tissues have decreased hemoglobin saturation and increased mean reduced scattering coefficients compared to nonmalignant tissues. Classification based on machine learning using these absorption and scattering properties as inputs achieved an unbiased sensitivity and specificity of 82 and 92%, respectively, in the study by Palmer et al, 18 and 86 and 80%, respectively, in the study by Zhu et al 17 , for discriminating between malignant and nonmalignant breast tissues.…”

“…However, they did not report the classification accuracy for diagnosing breast cancer based on the parameters extracted from the model. More recently our group, in two independent studies 17,18 applied a Monte-Carlo-based inverse model for the extraction of absorption and scattering properties from the diffuse reflectance spectra of malignant and non-malignant breast tissues. Both studies showed that malignant tissues have decreased hemoglobin saturation and increased mean reduced scattering coefficients compared to nonmalignant tissues.…”