Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin’s biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed.

Diffuse reflectance spectroscopy (DRS) is an optical technology that uses light to non-invasively measure optical properties of biological tissue and has applications in the diagnosis of several cancers such as breast,¹ colorectal,² cervical,^3,4 oral,^5,6 lung,⁷ and skin.^{8 – 10} DRS instruments typically use an optical fiber probe to emit light onto tissue, where the light is scattered and absorbed. The light is then collected back into the fiber and is sent into a spectrometer to return reflectance values to a computer. A full spectrum of wavelengths is typically used to capture sufficient data for an accurate analysis of tissue composition. Example full-spectrum devices include Dermasensor (Dermasensor, Miami, Florida), a handheld device for performing reflectance measurements of skin, and Zenascope IM1 (Zenalux, Durham, North Carolina), a DRS system that measures biological endpoints of tissue for various diagnostic applications. These full-spectrum systems can be expensive due to the high cost of spectrometers, which can drive the cost of even the cheaper DRS systems to be around $2600 to $3800.¹¹ Thus, costs can potentially be reduced by utilizing a cheaper spectrometer that has a limited number of wavelengths.

A key challenge in melanoma diagnosis is the large number of unnecessary biopsies on benign nevi, which requires significant amounts of time and money. To reduce unnecessary biopsies while still accurately detecting melanoma lesions, we propose using Raman spectroscopy as a non-invasive, fast, and inexpensive method for generating a “second opinion” for lesions being considered for biopsy. We collected in vivo Raman spectral data in the clinical skin screening setting from 52 patients, including 53 pigmented lesions and 7 melanomas. All lesions underwent biopsies based on clinical evaluation. Principal component analysis and logistic regression models with leave one lesion out cross validation were applied to classify melanoma and pigmented lesions for biopsy recommendations. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.903 and a specificity of 58.5% at perfect sensitivity. The number needed to treat for melanoma could have been decreased from 8.6 (60/7) to 4.1 (29/7). This study in a clinical skin screening setting shows the potential of Raman spectroscopy for reducing unnecessary skin biopsies with in vivo Raman data and is a significant step toward the application of Raman spectroscopy for melanoma screening in the clinic.

The standard‐of‐care for nonmelanoma skin cancer (NMSC), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), is surgical excision of the tumor. To ensure removal of the entire tumor and minimize removal of healthy tissue, a tool for quick discrimination between cancerous and normal skin is needed. We propose using diffuse reflectance spectroscopy (DRS) to assess tumor margins. We collected two independent clinical datasets. Using a clinical study of 31 patients with 50 NMSC lesions, we trained models including DRS data normalization, a physiological model, parameter selection and logistic regression classifiers. We tested our models on a second clinical dataset of 56 patients with 68 NMSC lesions. Our test results showed the area under the ROC curve of 0.94 for BCC vs normal and 0.90 for SCC vs normal. When applying a threshold selected using the training dataset to the test dataset, for BCC vs normal, specificity is 89% and sensitivity is 87%; for SCC vs normal, specificity is 65% and sensitivity is 97%. This study indicates that DRS can be potentially used to map the tumor margin prior to surgery and monitor margins during the surgery on the surface of the skin.

Light transport through biological tissue has been shown to be sensitive to microstructural composition, including the orientation of cells, the constituents of the intra- and extracellular matrix, and the ratio of cell sizes.¹ This behavior can be characterized using optical properties such as the reduced scattering coefficient (${\mu }_s^{\prime }$), absorption coefficient (${\mu }_a$), and the phase function backscatter parameter ($\gamma$).² These optical properties have been found to be useful in many medical applications such as detecting burn wound severity,³ monitoring blood occlusions,^4,5 and aiding in cancer diagnostics.^{6 – 9}