Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma

Mengkun Chen; Xu Feng; Matthew C Fox; Jason S Reichenberg; Fabiana C P S Lopes; Katherine R Sebastian; Mia K Markey; James W Tunnell

doi:10.1117/1.JBO.27.6.065004

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma

J Biomed Opt. 2022 Jun;27(6):065004. doi: 10.1117/1.JBO.27.6.065004.

Authors

Mengkun Chen¹, Xu Feng¹, Matthew C Fox², Jason S Reichenberg², Fabiana C P S Lopes², Katherine R Sebastian², Mia K Markey^{1

3}, James W Tunnell¹

Affiliations

¹ The University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.
² The University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, United States.
³ The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, Texas, United States.

Abstract

Significance: Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues structures. Reflectance confocal microscopy (RCM) provides morphological and cytological details by which many features of epidermis and hair follicles can be readily identified. Combining RS with deep-learning-aided RCM has the potential to improve the diagnostic accuracy of RS in an automated fashion, without requiring additional input from the clinician.

Aim: The aim of this study is to improve the specificity of RS for detecting basal cell carcinoma (BCC) using an artificial neural network trained on RCM images to identify false positive normal skin structures (hair follicles and epidermis).

Approach: Our approach was to build a two-step classification model. In the first step, a Raman biophysical model that was used in prior work classified BCC tumors from normal tissue structures with high sensitivity. In the second step, 191 RCM images were collected from the same site as the Raman data and served as inputs for two ResNet50 networks. The networks selected the hair structure and epidermis images, respectively, within all images corresponding to the positive predictions of the Raman biophysical model with high specificity. The specificity of the BCC biophysical model was improved by moving the Raman spectra corresponding to these selected images from false positive to true negative.

Results: Deep-learning trained on RCM images removed 52% of false positive predictions from the Raman biophysical model result while maintaining a sensitivity of 100%. The specificity was improved from 84.2% using Raman spectra alone to 92.4% by integrating Raman spectra with RCM images.

Conclusions: Combining RS with deep-learning-aided RCM imaging is a promising tool for guiding tumor resection surgery.

Keywords: Raman spectroscopy; basal cell carcinoma; deep learning; reflectance confocal microscopy.

MeSH terms

Carcinoma, Basal Cell* / diagnostic imaging
Deep Learning*
Dermoscopy / methods
Humans
Microscopy, Confocal / methods
Skin Neoplasms* / pathology