Detection of basal cell carcinoma by machine learning-assisted ex vivo confocal laser scanning microscopy.

Abstract

Background: Ex vivo confocal laser scanning microscopy (EVCM) is an emerging imaging modality that enables near real-time histology of whole tissue samples. However, the adoption of EVCM into clinical routine is partly limited because the recognition of modality-specific diagnostic features requires specialized training. Therefore, we aimed to build a machine learning algorithm for the detection of basal cell carcinoma (BCC) in images acquired using EVCM and, in turn, facilitate the examiner's decision-making process.

Methods: In this proof-of-concept study, histologically confirmed BCC fresh tissue samples were used to generate 50 EVCM images to train and assess a convolutional neural network architecture (MobileNet-V1) via tenfold cross-validation.

Results: Overall sensitivity and specificity of the model for detecting BCC and tumor-free regions on EVCM images compared to expert evaluation were 0.88 and 0.85, respectively. We constructed receiver operator characteristic and precision-recall curves from the aggregated tenfold cross-validation to assess the model's performance; the area under the curve was 0.94 and 0.87, respectively. Subsequently, the performance of one of the selected machine learning models was assessed with 19 new EVCM images of tumor-containing (n = 10) and 9 tumor-free (n = 9) skin tissue. A sensitivity of 0.83 and a specificity of 0.92 were achieved for the BCC group. The specificity for the tumor-free control group was 0.98.

Conclusion: The deep learning model developed in our study holds the potential to assist the diagnostic decision-making process and diminish the training time of novices by depicting tumor-positive regions in EVCM images.