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A multimodal method for skin cancer diagnosis based on Dermatoscopy (DS) image analysis, Hyperspectral Imaging (HSI), Raman/Autofluorescense analysis (RS/AF) and Optical Coherence Tomography (OCT) is presented. A universal multi-class classifier for OCT is described. The diagnostic accuracy near 90% (depended from tissue type) has been demonstrated for in vivo and ex vivo cases.
A multimodal unit for diagnosis of cutaneous malignant neoplasms has been proposed. It include following pure optical modalities: Raman Spectroscopy (NIR, 785 nm) in combination with Autofluorescence analysis (485 and 785 nm), Optical Coherence Tomography (840±45 nm) and Hyperspectral Imaging (visible range, 450-750 nm, 2.3 nm spectral resolution). The method is also supplemented with a Multispectral Digital Dermatoscopy, which is a perfect solution for preliminary screening and diagnosis of pathological areas by general physician.
The own developed DS tool is based on the Basler acA1920-25uc camera (RGB, 12 bit/px, 1920*1080), its resolution on the surface of the skin is about 13 μm/px. The device body has been designed and printed on a 3D printer. Tissue lighting is carried out by various LEDs: UV 365 nm, White 4000 K (polarized and unpolarized LEDs); Blue 465-485 nm; Green 520-535 nm; Red 620-630 nm LEDs.
The results of several in vivo and ex vivo series of experiments are presented. In vivo studies were performed in Samara Oncology Clinical Center. A total of 220 patients were examined with Malignant Melanoma (MM), Basal Cell Carcinoma (BCC) and other benign and malignant tumors. 232 tissue samples surgically resected from patients were examined ex vivo. All samples diagnoses were confirmed by histology reports after resection.
A final accuracy of 97.3% was obtained using RS/AF analysis. Moreover, for case of Malignant/Benign in vivo case, it reached up to 100% with low-cost Ocean Optics QE65 Pro spectrograph and PLS analysis. A final separation of classes for OCT ex vivo leads to a high (95%) accuracy for two-classes cases and 75% for the four classes case. Combination of 2D and 3D OCT data give us an opportunity to receive common information about tumor (geometric and morphological characteristics) from data cubes and use more powerful algorithms for counting features (fractal, textural, geometric) on separated scans. These categories of features provide closer connection with ABCDE criteria (asymmetry, borders irregularity, color, diameter, evolution). Final accuracies 90% for in vivo HSI is very similar to in vivo DS accuracy, which reaches 93%. The optical density spectral feature has been used as a main diagnostic characteristic for HSI and topology texture and color analysis for RGB DS.
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