Abstract
The diagnosis and treatment of non-melanoma skin cancer remain urgent problems. Histological examination of biopsy material—the gold standard of diagnosis—is an invasive procedure that requires a certain amount of time to perform. The ability to detect abnormal cells using fluorescence spectroscopy (FS) has been shown in many studies. This technique is rapidly expanding due to its safety, relative cost-effectiveness, and efficiency. However, skin lesion FS-based diagnosis is challenging due to a number of single overlapping spectra emitted by fluorescent molecules, making it difficult to distinguish changes in the overall spectrum and the molecular basis for it. We applied deep learning (DL) algorithms to quantitatively assess the ability of FS to differentiate between pathologies and normal skin. A total of 137 patients with various forms of primary and recurrent basal cell carcinoma (BCC) were observed by a multispectral laser-based device with a built-in neural network (NN) “DSL-1”. We measured the fluorescence spectra of suspected non-melanoma skin cancers and compared them with “normal” skin spectra. These spectra were input into DL algorithms to determine whether the skin is normal, pigmented normal, benign, or BCC. The preoperative differential AI-driven fluorescence diagnosis method correctly predicted the BCC lesions. We obtained an average sensitivity of 62% and average specificity of 83% in our experiments. Thus, the presented “DSL-1” diagnostic device can be a viable tool for the real-time diagnosis and guidance of non-melanoma skin cancer resection.
Highlights
75.0% to 97.0% of all malignant epithelial neoplasms of the skin, squamous cell carcinoma (SCC), which accounts for 5.0% up to 15.0%
The method described in this paper shows good statistical values: a specificity of 0.83 and sensitivity of 0.62
The clinical research is currently under implementation; it will become possible in future work on a large dataset from more substantial patient numbers to gain the best prediction and classification accuracy
Summary
Keratinocyte carcinomas (KC) or non-melanoma skin cancer is a group of malignant skin neoplasms (MSN), which includes basal cell carcinoma (BCC), which occupy from. 75.0% to 97.0% of all malignant epithelial neoplasms of the skin, squamous cell carcinoma (SCC), which accounts for 5.0% up to 15.0%. Rare skin appendage carcinomas (sebaceous and sweat glands, hair follicles) constitute less than 1.0% of all types of KC [1,2,3]. In the structure of the incidence of malignant diseases, KC in recent decades occupies from first to third place in most countries of the world [4]. Malvehy et al note that KC accounts for 80.0% and 20.0% of all types of MSN [5]. BCC is the most common skin neoplasia [6]
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