Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

• Published in: Frontiers in veterinary science Vol. 11; p. 1450304
• Main Authors: Jeong, Jeongyun, Choi, Hyunji, Kim, Minjoo, Kim, Sung-Soo, Goh, Jinhyong, Hwang, Jeongyeon, Kim, Jaehwan, Cho, Hwan-Ho, Eom, Kidong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 23.09.2024
• Subjects: adenocarcinoma; canine; clinical radiomics models; multinomial logistic regression; random forest; support vector machine models; Veterinary Science; multinomial logistic regression; support vector machine models; lymphoma; spindle cell sarcoma; adenocarcinoma; canine; random forest; clinical radiomics models
• ISSN: 2297-1769; 2297-1769
• DOI: 10.3389/fvets.2024.1450304

Radiomics models have been widely exploited in oncology for the investigation of tumor classification, as well as for predicting tumor response to treatment and genomic sequence; however, their performance in veterinary gastrointestinal tumors remains unexplored. Here, we sought to investigate and compare the performance of radiomics models in various settings for differentiating among canine small intestinal adenocarcinoma, lymphoma, and spindle cell sarcoma. Forty-two small intestinal tumors were contoured using four different segmentation methods: pre- or post-contrast, each with or without the inclusion of intraluminal gas. The mesenteric lymph nodes of pre- and post-contrast images were also contoured. The bin settings included bin count and bin width of 16, 32, 64, 128, and 256. Multinomial logistic regression, random forest, and support vector machine models were used to construct radiomics models. Using features from both primary tumors and lymph nodes showed significantly better performance than modeling using only the radiomics features of primary tumors, which indicated that the inclusion of mesenteric lymph nodes aids model performance. The support vector machine model exhibited significantly superior performance compared with the multinomial logistic regression and random forest models. Combining radiologic findings with radiomics features improved performance compared to using only radiomics features, highlighting the importance of radiologic findings in model building. A support vector machine model consisting of radiologic findings, primary tumors, and lymph node radiomics features with bin count 16 in post-contrast images with the exclusion of intraluminal gas showed the best performance among the various models tested. In conclusion, this study suggests that mesenteric lymph node segmentation and radiological findings should be integrated to build a potent radiomics model capable of differentiating among small intestinal tumors.