Tamošiūnas Mindaugas, Maciulevičius Martynas, Maļiks Romans, Dupļevska Diāna, Viškere Daira, Matīse-van Houtana Ilze, Kadiķis Roberts, Cugmas Blaž, Raišutis Renaldas
Institute of Atomic Physics and Spectroscopy, University of Latvia, Rīga, Latvia.
Ultrasound Research Institute, Kaunas University of Technology, Kaunas, Lithuania.
Vet Q. 2025 Dec;45(1):1-17. doi: 10.1080/01652176.2025.2486771. Epub 2025 Apr 9.
This study introduces Raman imaging technique for diagnosing skin cancer in veterinary oncology patients (dogs and cats). Initially, Raman spectral bands (with specificity to certain molecular structures and functional groups) were identified in formalin-fixed samples of mast cell tumors and soft tissue sarcomas, obtained through routine veterinary biopsy submissions. Then, a custom-built Raman macro-imaging system featuring an intensified CCD camera (iXon Ultra 888, Andor, UK), tunable narrow-band Semrock (USA) optical filter compartment was used to map the spectral features at 1437 cm and 1655 cm in tissue. This approach enabled wide-field (cm), rapid (within seconds), and safe (< 400 mW/cm) imaging conditions, supporting accurate diagnosis of tissue state. The findings indicate that machine learning classifiers - particularly support vector machine (SVM) and decision tree (DT) - effectively distinguished between soft tissue sarcoma, mastocytoma and benign tissues using Raman spectral band imaging data. Additionally, combining Raman macro-imaging with residual near-infrared (NIR) autofluorescence as a bimodal imaging technique enhanced diagnostic performance, reaching 85 - 95% in accuracy, sensitivity, specificity, and precision - even with a single spectral band (1437 cm or 1655 cm). In conclusion, the proposed bi-modal imaging is a pioneering method for veterinary oncology science, offering to improve the diagnostic accuracy of malignant tumors.
本研究介绍了拉曼成像技术在兽医肿瘤学患者（狗和猫）皮肤癌诊断中的应用。最初，通过常规兽医活检送检获得的肥大细胞瘤和软组织肉瘤的福尔马林固定样本中，识别出了拉曼光谱带（对某些分子结构和官能团具有特异性）。然后，使用配备增强型电荷耦合器件相机（iXon Ultra 888，安道尔，英国）、可调谐窄带Semrock（美国）光学滤光片组件的定制拉曼宏观成像系统，对组织中1437 cm和1655 cm处的光谱特征进行映射。这种方法实现了宽视野（厘米级）、快速（数秒内）和安全（<400 mW/cm）的成像条件，有助于准确诊断组织状态。研究结果表明，机器学习分类器——特别是支持向量机（SVM）和决策树（DT）——利用拉曼光谱带成像数据有效地区分了软组织肉瘤、肥大细胞瘤和良性组织。此外，将拉曼宏观成像与残余近红外（NIR）自发荧光相结合作为一种双峰成像技术，提高了诊断性能，即使在使用单个光谱带（1437 cm或1655 cm）时，准确率、灵敏度、特异性和精确率也能达到85 - 95%。总之所提出的双峰成像技术是兽医肿瘤学领域的一种开创性方法，有望提高恶性肿瘤的诊断准确性。
