Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique

• Published in: Lasers in surgery and medicine Vol. 38; no. 7; pp. 714 - 724
• Main Authors: Zhu, Changfang, Palmer, Gregory M., Breslin, Tara M., Harter, Josephine, Ramanujam, Nirmala
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01-08-2006
• Subjects: absorption; Adipose Tissue - pathology; beta Carotene - analysis; Breast - pathology; Breast Neoplasms - diagnosis; Breast Neoplasms - pathology; breast tissues; Carcinoma in Situ - diagnosis; Carcinoma in Situ - pathology; Carcinoma, Ductal, Breast - diagnosis; Carcinoma, Ductal, Breast - pathology; Carcinoma, Lobular - diagnosis; Carcinoma, Lobular - pathology; diffuse reflectance; Female; Fibrocystic Breast Disease - diagnosis; Fibrocystic Breast Disease - pathology; Hemoglobins - analysis; Humans; Image Processing, Computer-Assisted - methods; Least-Squares Analysis; Monte Carlo inverse model; Monte Carlo Method; Neoplasms, Fibrous Tissue - diagnosis; Neoplasms, Fibrous Tissue - pathology; optical properties; optical spectroscopy; scattering; Spectrophotometry, Ultraviolet - statistics & numerical data; spectrum; spectrum, breast tissues
• ISSN: 0196-8092; 1096-9101
• DOI: 10.1002/lsm.20356

Background and Objective
We explored the use of diffuse reflectance spectroscopy in the ultraviolet‐visible (UV‐VIS) spectrum for the diagnosis of breast cancer. A physical model (Monte Carlo inverse model) and an empirical model (partial least squares analysis) based approach, were compared for extracting diagnostic features from the diffuse reflectance spectra.
Study Design/Methods
The physical model and the empirical model were employed to extract features from diffuse reflectance spectra measured from freshly excised breast tissues. A subset of extracted features obtained using each method showed statistically significant differences between malignant and non‐malignant breast tissues. These features were separately input to a support vector machine (SVM) algorithm to classify each tissue sample as malignant or non‐malignant.
Results and Conclusions
The features extracted from the Monte Carlo based analysis were hemoglobin saturation, total hemoglobin concentration, beta‐carotene concentration and the mean (wavelength averaged) reduced scattering coefficient. Beta‐carotene concentration was positively correlated and the mean reduced scattering coefficient was negatively correlated with percent adipose tissue content in normal breast tissues. In addition, there was a statistically significant decrease in the beta‐carotene concentration and hemoglobin saturation, and a statistically significant increase in the mean reduced scattering coefficient in malignant tissues compared to non‐malignant tissues. The features extracted from the partial least squares analysis were a set of principal components. A subset of principal components showed that the diffuse reflectance spectra of malignant breast tissues displayed an increased intensity over wavelength range of 440–510 nm and a decreased intensity over wavelength range of 510–600 nm, relative to that of non‐malignant breast tissues. The diagnostic performance of the classification algorithms based on both feature extraction techniques yielded similar sensitivities and specificities of approximately 80% for discriminating between malignant and non‐malignant breast tissues. While both methods yielded similar classification accuracies, the model based approach provided insight into the physiological and structural features that discriminate between malignant and non‐malignant breast tissues. Lasers Surg. Med. © 2006 Wiley‐Liss, Inc.