Computed tomography-derived structural analysis for the likelihood of pathologic fracture in canine antebrachial osteosarcoma.

Determining the risk of pathologic fracture in dogs with a primary bone tumor would aid in case selection for in-situ treatment options. Prior research found strong relationships between in vitro strength of canine antebrachii with primary bone tumors and CT-derived metrics. This study assesses the prognosis for pathologic fracture in dogs with distal radial bone tumors using CT-derived structural analysis metrics. CT images of the antebrachium in dogs with aggressive osseous lesions of the radius were used to calculate structural rigidity and failure forces, including axial rigidity (AR), craniocaudal bending rigidity (BR), torsional rigidity (TR), and failure forces for a slightly-curved/asymmetric beam (Fs) or a curved beam (Fc). Metrics were compared with the clinical outcome of radial fracture. Eight of 19 dogs with CT-derived metrics developed a radial fracture. The prognostic potential of the metrics to discriminate fractured and nonfractured bones was analyzed using receiver operating characteristic curves (area under the curve), stepwise logistic regression, and classification regression (CART) analyses. Fc was the most sensitive and specific metric for prognosing fracture occurrence (AUC = 0.864). When dog body weight (BW) was included, all five metrics had AUC > 0.705. Fc was the best predictor of fracture using stepwise logistic regression and CART analysis, followed by BR. An indication of fracture probability can be determined by normalizing Fc or BR with dog BW or by using the logistic regression equation of either metric with dog BW. Results warrant further analysis of a larger cohort to evaluate fracture likelihood in dogs with antebrachial bone neoplasia.

Assessment of tumor hypoxia in spontaneous canine tumors after treatment with OMX, a novel H-NOX oxygen carrier, with [18F]FMISO PET/CT.

BACKGROUND: Hypoxia is a detrimental factor in solid tumors, leading to aggressiveness and therapy resistance. OMX, a tunable oxygen carrier from the heme nitric oxide/oxygen-binding (H-NOX) protein family, has the potential to reduce tumor hypoxia. [18F]Fluoromisonidazole ([18F]FMISO) positron emission tomography (PET) is the most widely used and investigated method for non-invasive imaging of tumor hypoxia. In this study, we used [18F]FMISO PET/CT (computed tomography) to assess the effect of OMX on tumor hypoxia in spontaneous canine tumors. RESULTS: Thirteen canine patients with various tumors (n = 14) were randomly divided into blocks of two, with the treatment groups alternating between receiving intratumoral (IT) OMX injection (OMX IT group) and intravenous (IV) OMX injection (OMX IV group). Tumors were regarded as hypoxic if maximum tumor-to-muscle ratio (TMRmax) was greater than 1.4. In addition, hypoxic volume (HV) was defined as the region with tumor-to-muscle ratio greater than 1.4 on [18F]FMISO PET images. Hypoxia was detected in 6/7 tumors in the OMX IT group and 5/7 tumors in the OMX IV injection group. Although there was no significant difference in baseline hypoxia between the OMX IT and IV groups, the two groups showed different responses to OMX. In the OMX IV group, hypoxic tumors (n = 5) exhibited significant reductions in tumor hypoxia, as indicated by decreased TMRmax and HV in [18F]FMISO PET imaging after treatment. In contrast, hypoxic tumors in the OMX IT group (n = 6) displayed a significant increase in [18F]FMISO uptake and variable changes in TMRmax and HV. CONCLUSIONS: [18F]FMISO PET/CT imaging presents a promising non-invasive procedure for monitoring tumor hypoxia and assessing the efficacy of hypoxia-modulating therapies in canine patients. OMX has shown promising outcomes in reducing tumor hypoxia, especially when administered intravenously, as evident from reductions in both TMRmax and HV in [18F]FMISO PET imaging.

Diagnosis and classification of portosystemic shunts: a machine learning retrospective case-control study.

Diagnosis of portosystemic shunts (PSS) in dogs often requires multiple diagnostic tests, and available clinicopathologic tests have limitations in sensitivity and specificity. The objective of this study was to train and validate a machine learning model (MLM) that can accurately predict the presence of a PSS utilizing routinely collected demographic data and clinicopathologic features. Dogs diagnosed with PSS or control dogs tested for PSS but had the condition ruled out (non-PSS) were identified. Dogs were included if a complete blood count and serum chemistry panel were available from PSS diagnostic testing. Dogs with a PSS were subcategorized as having a single intrahepatic PSS, a single extrahepatic PSS, or multiple extrahepatic PSS. An extreme gradient boosting (XGboost) MLM was trained with data from 70% of the cases, and MLM performance was determined on the test set, comprising the remaining 30% of the case data. Two MLMs were created. The first was designed to predict the presence of any PSS (PSS MLM), and the second to predict the PSS subcategory (PSS SubCat MLM). The trained PSS MLM had a sensitivity of 94.3% (95% CI 90.1-96.8%) and specificity of 90.5% (95% CI 85.32-94.0%) for dogs in the test set. The area under the receiver operator characteristic curve (AUC) was 0.976 (95% CI; 0.964-0.989). The mean corpuscular hemoglobin, lymphocyte count, and serum globulin concentration were most important in prediction classification. The PSS SubCat MLM had an accuracy of 85.7% in determining the subtype of PSS of dogs in the test set, with variable sensitivity and specificity depending on PSS subtype. These MLMs have a high accuracy for diagnosing PSS; however, the prediction of PSS subclassification is less accurate. The MLMs can be used as a screening tool to increase or decrease the index of suspicion for PSS before confirmatory diagnostics such as advanced imaging are pursued.