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Surkov YI, Serebryakova IA, Kuzinova YK, Konopatskova OM, Safronov DV, Kapralov SV, Genina EA, Tuchin VV. Multimodal Method for Differentiating Various Clinical Forms of Basal Cell Carcinoma and Benign Neoplasms In Vivo. Diagnostics (Basel) 2024; 14:202. [PMID: 38248078 PMCID: PMC10814941 DOI: 10.3390/diagnostics14020202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/15/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024] Open
Abstract
Correct classification of skin lesions is a key step in skin cancer screening, which requires high accuracy and interpretability. This paper proposes a multimodal method for differentiating various clinical forms of basal cell carcinoma and benign neoplasms that includes machine learning. This study was conducted on 37 neoplasms, including benign neoplasms and five different clinical forms of basal cell carcinoma. The proposed multimodal screening method combines diffuse reflectance spectroscopy, optical coherence tomography and high-frequency ultrasound. Using diffuse reflectance spectroscopy, the coefficients of melanin pigmentation, erythema, hemoglobin content, and the slope coefficient of diffuse reflectance spectroscopy in the wavelength range 650-800 nm were determined. Statistical texture analysis of optical coherence tomography images was used to calculate first- and second-order statistical parameters. The analysis of ultrasound images assessed the shape of the tumor according to parameters such as area, perimeter, roundness and other characteristics. Based on the calculated parameters, a machine learning algorithm was developed to differentiate the various clinical forms of basal cell carcinoma. The proposed algorithm for classifying various forms of basal cell carcinoma and benign neoplasms provided a sensitivity of 70.6 ± 17.3%, specificity of 95.9 ± 2.5%, precision of 72.6 ± 14.2%, F1 score of 71.5 ± 15.6% and mean intersection over union of 57.6 ± 20.1%. Moreover, for differentiating basal cell carcinoma and benign neoplasms without taking into account the clinical form, the method achieved a sensitivity of 89.1 ± 8.0%, specificity of 95.1 ± 0.7%, F1 score of 89.3 ± 3.4% and mean intersection over union of 82.6 ± 10.8%.
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Affiliation(s)
- Yuriy I. Surkov
- Institution of Physics, Saratov State University, 410012 Saratov, Russia; (I.A.S.); (E.A.G.)
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia
- Laboratory of Biomedical Photoacoustic, Saratov State University, 410012 Saratov, Russia;
| | - Isabella A. Serebryakova
- Institution of Physics, Saratov State University, 410012 Saratov, Russia; (I.A.S.); (E.A.G.)
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia
| | - Yana K. Kuzinova
- Department of Faculty Surgery and Oncology, Saratov State Medical University, 410012 Saratov, Russia; (Y.K.K.); (D.V.S.); (S.V.K.)
| | - Olga M. Konopatskova
- Laboratory of Biomedical Photoacoustic, Saratov State University, 410012 Saratov, Russia;
- Department of Faculty Surgery and Oncology, Saratov State Medical University, 410012 Saratov, Russia; (Y.K.K.); (D.V.S.); (S.V.K.)
| | - Dmitriy V. Safronov
- Department of Faculty Surgery and Oncology, Saratov State Medical University, 410012 Saratov, Russia; (Y.K.K.); (D.V.S.); (S.V.K.)
| | - Sergey V. Kapralov
- Department of Faculty Surgery and Oncology, Saratov State Medical University, 410012 Saratov, Russia; (Y.K.K.); (D.V.S.); (S.V.K.)
| | - Elina A. Genina
- Institution of Physics, Saratov State University, 410012 Saratov, Russia; (I.A.S.); (E.A.G.)
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia
| | - Valery V. Tuchin
- Institution of Physics, Saratov State University, 410012 Saratov, Russia; (I.A.S.); (E.A.G.)
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia
- Laboratory of Biomedical Photoacoustic, Saratov State University, 410012 Saratov, Russia;
- Institute of Precision Mechanics and Control, FRC “Saratov Scientific Centre of the Russian Academy of Sciences”, 410028 Saratov, Russia
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