Shovan K. Majumder

In vivo nonmelanoma skin cancer diagnosis using Raman microspectroscopy

Nonmelanoma skin cancers, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are the most common skin cancers, presenting nearly as many cases as all other cancers combined. The current gold‐standard for clinical diagnosis of these lesions is histopathologic examination, an invasive, time‐consuming procedure. There is thus considerable interest in developing a real‐time, automated, noninvasive tool for nonmelanoma skin cancer diagnosis. In this study, we explored the capability of Raman microspectroscopy to provide differential diagnosis of BCC, SCC, inflamed scar tissue, and normal tissue in vivo.

Measurement of optical transport properties of normal and malignant human breast tissue

We report measurement of optical transport parameters of normal and malignant (ductal carcinoma) human breast tissue. A spatially resolved steady-state diffuse reflectance technique was used for measurement of the reduced scattering coefficient (mu(s)?) and the absorption coefficient (mu(a)) of the tissue. The anisotropy parameter of scattering (g) was estimated by goniophotometric measurements of the scattering phase function. The values of mu(s)? and mu(a) for malignant breast tissue were observed to be larger than those for normal breast tissue over the wavelength region investigated (450-650 nm). Further, by using both the diffuse reflectance and the goniophotometric measurements, we estimated the Mie equivalent average radius of tissue scatterers to be larger in malignant tissue than in normal tissue.

Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy

This article reports results of an in vitro study involving 63 patients for the evaluation of the diagnostic potential of N2 laser excited autofluorescence spectroscopy of human breast tissues.

Raman microspectroscopy for skin cancer detection in vitro

We investigate the potential of near-infrared Raman microspectroscopy to differentiate between normal and malignant skin lesions. Thirty-nine skin tissue samples consisting of normal, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma from 39 patients were investigated. Raman spectra were recorded at the surface and at 20-microm intervals below the surface for each sample, down to a depth of at least 100 microm. Data reduction algorithms based on the nonlinear maximum representation and discrimination feature (MRDF) and discriminant algorithms using sparse multinomial logistic regression (SMLR) were developed for classification of the Raman spectra relative to histopathology. The tissue Raman spectra were classified into pathological states with a maximal overall sensitivity and specificity for disease of 100%. These results indicate the potential of using Raman microspectroscopy for skin cancer detection and provide a clear rationale for future clinical studies.

Autofluorescence and diffuse reflectance spectroscopy and spectral imaging for breast surgical margin analysis

Most women with early stage breast cancer have the option of breast conserving therapy, which involves a partial mastectomy for removal of the primary tumor, usually followed by radiotherapy. The presence of tumor at or near the margin is strongly correlated with the risk of local tumor recurrence, so there is a need for a non‐invasive, real‐time tool to evaluate margin status. This study examined the use of autofluorescence and diffuse reflectance spectroscopy and spectral imaging to evaluate margin status intraoperatively.

Spatially offset Raman spectroscopy of layered soft tissues

Raman spectroscopy has been widely used for cancer diagnosis, but conventional forms provide limited depth information. Spatially offset Raman spectroscopy (SORS) can solve the depth issue, but it has only been used to detect hard tissues such as bone. We explore the feasibility of using SORS to discriminate two layers of soft tissue. Measurements were taken with individual source and detector fibers at a number of spatial offsets from samples consisting of various thicknesses of normal human breast tissues overlying breast tumors. Results show that SORS can detect tumors beneath normal tissue, marking, to the best of our knowledge, the first application of SORS for discriminating two layers of soft tissue.

Support vector machine for optical diagnosis of cancer

We report the application of a support vector machine (SVM) for the development of diagnostic algorithms for optical diagnosis of cancer. Both linear and nonlinear SVMs have been investigated for this purpose. We develop a methodology that makes use of SVM for both feature extraction and classification jointly by integrating the newly developed recursive feature elimination (RFE) in the framework of SVM. This leads to significantly improved classification results compared to those obtained when an independent feature extractor such as principal component analysis (PCA) is used. The integrated SVM-RFE approach is also found to outperform the classification results yielded by traditional Fishers linear discriminant (FLD)-based algorithms. All the algorithms are developed using spectral data acquired in a clinical in vivo laser-induced fluorescence (LIF) spectroscopic study conducted on patients being screened for cancer of the oral cavity and normal volunteers. The best sensitivity and specificity values provided by the nonlinear SVM-RFE algorithm over the data sets investigated are 95 and 96% toward cancer for the training set data based on leave-one-out cross validation and 93 and 97% toward cancer for the independent validation set data. When tested on the spectral data of the uninvolved oral cavity sites from the patients it yielded a specificity of 85%.

Comparison of autofluorescence, diffuse reflectance, and Raman spectroscopy for breast tissue discrimination

For a given diagnostic problem, important considerations are the relative performances of the various optical biopsy techniques. A comparative evaluation of fluorescence, diffuse reflectance, combined fluorescence and diffuse reflectance, and Raman spectroscopy in discriminating different histopathologic categories of human breast tissues is reported. Optical spectra were acquired ex vivo from a total of 74 breast tissue samples belonging to 4 distinct histopathologic categories: invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS), fibroadenoma (FA), and normal breast tissue. A probability-based multivariate statistical algorithm capable of direct multiclass classification was developed to analyze the diagnostic content of the spectra measured from the same set of breast tissue sites with these different techniques. The algorithm uses the theory of nonlinear maximum representation and discrimination feature for feature extraction, and the theory of sparse multinomial logistic regression for classification. The results reveal that the performance of Raman spectroscopy is superior to that of all others in classifying the breast tissues into respective histopathologic categories. The best classification accuracy was observed to be approximately 99%, 94%, 98%, and 100% for IDC, DCIS, FA, and normal breast tissues, respectively, on the basis of leave-one-sample-out cross-validation, with an overall accuracy of approximately 99%.

Effect of hormonal variation on Raman spectra for cervical disease detection

OBJECTIVE The objective of the study was to characterize the variations in normal cervical spectra because of menopausal status and location within the menstrual cycle. Using the information obtained, the accuracy of Raman spectroscopy to diagnose low-grade squamous intraepithelial lesion (LGSIL) will be improved. STUDY DESIGN A total of 133 patients undergoing either colposcopy or Papanicolaou smear were recruited from either Vanderbilt University or Tri-State Womens Health. Raman spectra were collected from both normal and diseased areas. The data were processed and analyzed using a multiclass discrimination and classification algorithm to determine whether the spectra were correctly classified. RESULTS Stratifying the data by menopausal state resulted in correctly classifying LGSIL 97% of the time (from 74%). CONCLUSION This study brings Raman spectroscopy one step closer to clinical use by improving the sensitivity to differentiate LGSIL from normal.

Depth-resolved fluorescence measurement in a layered turbid medium by polarized fluorescence spectroscopy.

We show that, when a turbid medium with a layered fluorophore distribution is excited by linearly polarized light, measurement of angle-resolved polarized fluorescence can provide depth-resolved fluorescence measurements.