Abstract
Quantitative analysis of computed tomography (CT) radiomic features is an indirect measure of tumor heterogeneity, which has been associated with prognosis in human lung carcinoma. Canine lung tumors share similar features to human lung tumors and serve as a model in which to investigate the utility of radiomic features in differentiating tumor type and prognostication. The purpose of this study was to correlate first-order radiomic features from canine pulmonary tumors to histopathologic characteristics and outcome. Disease-free survival, overall survival time and tumor-specific survival were calculated as days from the date of CT scan. Sixty-seven tumors from 65 dogs were evaluated. Fifty-six tumors were classified as primary pulmonary adenocarcinomas and 11 were non-adenocarcinomas. All dogs were treated with surgical resection; 14 dogs received adjuvant chemotherapy. Second opinion histopathology in 63 tumors confirmed the histologic diagnosis in all dogs and further characterized 53 adenocarcinomas. The median overall survival time was longer (p = 0.004) for adenocarcinomas (339d) compared to non-adenocarcinomas (55d). There was wide variation in first-order radiomic statistics across tumors. Mean Hounsfield units (HU) ratio (p = 0.042) and median mean HU ratio (p = 0.042) were higher in adenocarcinomas than in non-adenocarcinomas. For dogs with adenocarcinoma, completeness of excision was associated with overall survival (p<0.001) while higher mitotic index (p = 0.007) and histologic score (p = 0.037) were associated with shorter disease-free survival. CT-derived tumor variables prognostic for outcome included volume, maximum axial diameter, and four radiomic features: integral total, integral total mean ratio, total HU, and max mean HU ratio. Tumor volume was also significantly associated with tumor invasion (p = 0.044). Further study of radiomic features in canine lung tumors is warranted as a method to non-invasively interrogate CT images for potential predictive and prognostic utility.
Highlights
Lung cancer is the most commonly diagnosed cancer and is a leading cause of human cancer death worldwide [1,2,3]
The presence of clinical signs was defined as any sign that could reportedly be secondary to a lung tumor, including cough, tachypnea, dyspnea, lethargy, exercise intolerance, anorexia, hyporexia, weight loss, and discomfort [16, 17, 19]
Cytologic interpretation was largely concordant with the histologic classification of adenocarcinoma or non-adenocarcinoma in 25/31 (81%) tumors (Table 1)
Summary
Lung cancer is the most commonly diagnosed cancer and is a leading cause of human cancer death worldwide [1,2,3]. There are two major categories of human lung cancer, namely small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). The latter is most common and can be further subdivided into adenocarcinoma and squamous cell carcinoma. Obtaining a lung biopsy is invasive, associated with a risk of minor and major complications, expensive, and requires time for histopathologic review [3, 9]. The identification of a semi-automated, quantitative imaging method by which lung tumors could be non-invasively and accurately classified may provide important predictive information to positively impact clinical practice [10, 11]
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