Maciulevičius Martynas, Rupšytė Greta, Raišutis Renaldas, Tamošiūnas Mindaugas
Research Institute of Natural and Technological Sciences, Vytautas Magnus University, Kaunas, Lithuania.
Department of System Analysis, Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania.
Vet Q. 2025 Dec;45(1):1-19. doi: 10.1080/01652176.2025.2510486. Epub 2025 May 30.
Study advances current diagnostic efficiency of canine/feline (sub-)cutaneous tumors using machine learning and multimodal imaging data. White light (WL), fluorescence (FL) and ultrasound (US) imaging were combined into hybrid approaches to differentiate between malignant mastocytomas, soft tissue sarcomas and benign lipomas. Support Vector Machine and Ensemble classifiers were optimized sequential feature selection. US radio-frequency signals were quantitatively analyzed to derive the colormaps of six US estimates, corresponding to spectral and temporal domains of the acoustic field. This resulted in the quantification of 72 morphological features for US; as well as 24 and 12 - for WL and FL data, respectively. Resulting classification efficiency for mastocytoma and sarcoma using US data was >75%; US+FL - 75-80%; US+WL - 85-90% and US+OPTICS - 90-95%. ∼100% classification efficiency was achieved for the differentiation between benign and malignant tumors even using single WL feature for Ensemble classifier. US features, resulting in inferior classification efficiency, were competitive to superior optical, as they were selected during optimization to be added to or replace optical counterparts. Additional tissue differentiation was performed on z-stacks of US colormaps, obtained using 3D arrays of US radio-frequency signals. This resulted in ∼70% differentiation efficiency for mastocytoma and sarcoma as well as >95% for benign and malignant tissues. The obtained additional metric of classification efficiency provides complementary diagnostic support, which for Support Vector Machine can be expressed as: 90.3 ± 1.9% (US+WL)×71.2 ± 0.6% (US). This hybrid criterion adds robustness to diagnostic model and may be very beneficial to characterize heterogeneous tissues.
研究利用机器学习和多模态成像数据提高了犬/猫（亚）皮下肿瘤的当前诊断效率。将白光（WL）、荧光（FL）和超声（US）成像结合成混合方法，以区分恶性肥大细胞瘤、软组织肉瘤和良性脂肪瘤。支持向量机和集成分类器通过顺序特征选择进行了优化。对US射频信号进行定量分析，以得出六个US估计值的彩色图，对应于声场的频谱和时域。这导致对US的72个形态特征进行了量化；对于WL和FL数据，分别为24个和12个。使用US数据对肥大细胞瘤和肉瘤的分类效率>75%；US+FL为75 - 80%；US+WL为85 - 90%，US+OPTICS为90 - 95%。即使使用集成分类器的单个WL特征，良性和恶性肿瘤之间的区分也实现了约100%的分类效率。导致分类效率较低的US特征与 superior optical具有竞争力，因为它们在优化过程中被选择添加到光学对应特征中或替代光学对应特征。对使用US射频信号的3D阵列获得的US彩色图的z轴堆叠进行了额外的组织区分。这导致肥大细胞瘤和肉瘤的区分效率约为70%，良性和恶性组织的区分效率>95%。获得的额外分类效率指标提供了互补的诊断支持，对于支持向量机，其可以表示为：90.3±1.9%（US+WL）×71.2±0.6%（US）。这种混合标准增强了诊断模型的稳健性，可能对表征异质组织非常有益。
