3 research outputs found
Immunization against GnRF in adult cattle: a prospective field study
No full textAbstract Background Suppression of cyclic activity in cattle is often desired in alpine farming and for feedlot cattle, not intended for breeding. A cattle specific anti-GnRF vaccine (Bopriva™) is registered for use in heifers and bulls in different countries. In adult cows vaccinated with Bopriva™, the median period until recurrence of class III follicles was 78 days from the day of the 2nd vaccination and reversibility could be proven, as out of 11 experimental cows 10 cows became pregnant at first, and one cow at second insemination. In the present study, 76 healthy, cyclic Eringer heifers and cows were vaccinated twice with Bopriva™ 3-7 weeks apart, to prevent estrus during alpine pasturing. Blood samples were taken for progesterone and GnRF antibody titer analysis on the day of inclusion (7–9 d before the first vaccination) and at the first vaccination. At the same time, gynaecological examinations were performed. When estrus occurred in the course of the alpine pasturing season, a gynaecological examination was done including analysis of a blood sample (progesterone, anti-GnRF antibody titer). Cows were followed for fertility out to 26 months post second vaccination. Results Median duration of estrus suppression was 191 days after the second vaccination (when the 2 vaccinations were given 28–35 days apart). From n = 13 cows showing signs of estrus on the alpine pasture, n = 7 could not be confirmed in estrus (serum progesterone value >2 ng/ml, no class III follicles seen using ultrasonography). Median duration between second vaccination and next calving was 496 days (25%/75% quartiles: 478/532 days). Conclusion Bopriva™ induced a reliable and reversible suppression of estrus for more than 3 months in over 90% of the cows
Normal Tissue Complication Probability (NTCP) Prediction Model for Osteoradionecrosis of the Mandible in Patients With Head and Neck Cancer After Radiation Therapy:Large-Scale Observational Cohort
No full textPurpose: Osteoradionecrosis (ORN) of the mandible represents a severe, debilitating complication of radiation therapy (RT) for head and neck cancer (HNC). At present, no normal tissue complication probability (NTCP) models for risk of ORN exist. The aim of this study was to develop a multivariable clinical/dose-based NTCP model for the prediction of ORN any grade (ORNI-IV) and grade IV (ORNIV) after RT (+/- chemotherapy) in patients with HNC.Methods and Materials: Included patients with HNC were treated with (chemo-)RT between 2005 and 2015. Mandible bone radiation dose-volume parameters and clinical variables (ie, age, sex, tumor site, pre-RT dental extractions, chemotherapy history, postoperative RT, and smoking status) were considered as potential predictors. The patient cohort was randomly divided into a training (70%) and independent test (30%) cohort. Bootstrapped forward variable selection was performed in the training cohort to select the predictors for the NTCP models. Final NTCP model(s) were validated on the holdback test subset.Results: Of 1259 included patients with HNC, 13.7% (n = 173 patients) developed any grade ORN (ORNI-IV primary endpoint) and 5% (n = 65) ORNIV (secondary endpoint). All dose and volume parameters of the mandible bone were significantly associated with the development of ORN in univariable models. Multivariable analyses identified D30% and pre-RT dental extraction as independent predictors for both ORNI-IV and ORNIV best-performing NTCP models with an area under the curve (AUC) of 0.78 (AUCvalidation = 0.75 [0.69-0.82]) and 0.81 (AUCvalidation = 0.82 [0.74-0.89]), respectively.Conclusions: This study presented NTCP models based on mandible bone D30% and pre-RT dental extraction that predict ORNI-IV and ORNIV (ie, needing invasive surgical intervention) after HNC RT. Our results suggest that less than 30% of the mandible should receive a dose of 35 Gy or more for an ORNI-IV risk lower than 5%. These NTCP models can improve ORN prevention and management by identifying patients at risk of ORN. (C) 2021 The Author(s). Published by Elsevier Inc.</p
Computed Tomography Radiomics-Based Cross-Sectional Detection of Mandibular Osteoradionecrosis in Head and Neck Cancer Survivors
No full textPurpose: This study aims to identify radiomic features from contrast-enhanced CT (CECT) scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer (HNC) patients treated with radiotherapy (RT).
Materials and methods: CECT images from 150 patients with confirmed ORN diagnosis (2008-2018) at MD Anderson Cancer Center (MDACC) were analyzed (80 % train, 20 % test). Radiomic features were extracted using PyRadiomics from manually segmented ORN regions and automated contralateral healthy mandible regions. Correlation analysis (r \u3e 0.95) reduced features for model training. A random Forest (RF) classifier with Recursive Feature Elimination identified discriminative features. Explainability was assessed using SHapley Additive exPlanations (SHAP) analysis on the 20 most important features identified by the trained RF classifier.
Results: Of the 1316 radiomic features extracted, 810 features were excluded for high collinearity. From a set of 506 pre-selected radiomic features, 67 were optimal for RF classification, yielding 88% accuracy and a ROC AUC of 0.96. The model well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue.
Conclusion: This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on detecting subclinical ORNJ regions to guide earlier interventions
