Dynamic contrast-enhanced computed tomography perfusion parameters of canine suspected brain tumors at baseline and during radiotherapy might be different depending on tumor location but not associated with survival.

Introduction: Treatment of brain tumors in dogs can be associated with significant morbidity and reliable prognostic factors are lacking. Dynamic contrast-enhanced computed tomography (DCECT) can be used to assess tumor perfusion. The objectives of this study were to assess perfusion parameters and change in size of suspected brain tumors before and during radiotherapy (RT) depending on their location and find a potential correlation with survival. Methods: Seventeen client-owned dogs with suspected brain tumors were prospectively recruited. All dogs had a baseline DCECT to assess mass size, blood volume (BV), blood flow (BF), and transit time (TT). Twelve dogs had a repeat DCECT after 12 Gy of megavoltage RT. Survival times were calculated. Results: Intra-axial masses had lower BF (p = 0.005) and BV (p < 0.001) than extra-axial masses but not than pituitary masses. Pituitary masses had lower BF (p = 0.001) and BV (p = 0.004) than extra-axial masses. The volume of the mass was positively associated with TT (p = 0.001) but not with BF and BV. Intra-axial masses showed a more marked decrease in size than extra-axial and pituitary masses during RT (p = 0.022 for length, p = 0.05 for height). Extra-axial masses showed a greater decrease in BF (p = 0.011) and BV (p = 0.012) during RT than pituitary masses and intra-axial masses. Heavier dogs had a shorter survival time (p = 0.011). Perfusion parameters were not correlated with survival. Conclusion: DCECT perfusion parameters and change in size of brain masses during RT might be different based on the location of the mass.

Dynamic contrast-enhanced computed tomography in dogs with nasal tumors.

BACKGROUND: Treatment of nasal tumors in dogs is associated with high morbidity and reliable prognostic factors are lacking. Dynamic contrast-enhanced computed tomography (DCECT) can be used to assess tumor perfusion. OBJECTIVES: To assess perfusion parameters of nasal tumors (correlating with tumor type) before and during radiotherapy (RT) and find potential correlation with survival. ANIMALS: Twenty-four client-owned dogs with nasal tumors, including 16 epithelial tumors and 8 sarcomas. METHODS: Prospective cross-sectional study. All dogs had baseline DCECT to assess fractional vascular volume (BV), blood flow (BF), and transit time (TT). Thirteen dogs had repeat DCECT after 12 Gy of megavoltage RT. Survival times were calculated. RESULTS: Median BV was 17.83 mL/100 g (range, 3.63-66.02), median BF was 122.63 mL/100 g/minute (range, 23.65-279.99), and median TT was 8.91 seconds (range, 4.57-14.23). Sarcomas had a significantly lower BF than adenocarcinomas (P = .002), carcinomas (P = .01), and other carcinomas (P = .001), and significantly lower BV than adenocarcinomas (P = .03) and other carcinomas (P = .004). Significant associations were found between epithelial tumors and sarcoma for change in tumor volume (P = .01), width (P = .004), and length (P = .02) in that epithelial tumors decreased in volume whereas sarcomas increased in volume. Perfusion parameters were not correlated with survival. CONCLUSIONS AND CLINICAL IMPORTANCE: Nasal sarcomas have lower BV and BF than nasal carcinomas, and sarcomas have a lower size reduction than carcinomas early on during RT. Baseline results and changes in perfusion parameters may not be correlated with survival.

Dynamic contrast-enhanced computed tomography in 11 dogs with orofacial tumors.

OBJECTIVE: Treatment of orofacial tumors in dogs is associated with high morbidity and reliable prognostic factors are lacking. Dynamic contrast-enhanced computed tomography (DCECT) can be used to assess tumor perfusion. The objectives of this study were to describe the perfusion parameters of different types of orofacial tumors and to describe the changes in perfusion parameters during radiotherapy (RT) in a subset of them. ANIMALS: 11 dogs with orofacial tumors prospectively recruited. CLINICAL PRESENTATION AND PROCEDURES: All dogs had baseline DCECT to assess blood volume (BV), blood flow (BF), and transit time (TT). Five dogs had repeat DCECT during megavoltage RT. RESULTS: 5 squamous cell carcinomas, 3 sarcomas, 1 melanoma, 1 histiocytic sarcoma, and 1 acanthomatous ameloblastoma were included. Blood volume and BF were higher in squamous cell carcinomas than in sarcomas, although no statistical analysis was performed. At repeat DCECT, 4 dogs showed a reduction in the size of their tumor during RT. Among these dogs, 3 showed an increase in BV and BF and 1 a decrease in these parameters between the baseline and the follow-up DCECT. The only dog whose tumor increased in size between the first and the second DCECT showed a decrease in BV and BF. CLINICAL RELEVANCE: Perfusion parameters derived from DCECT were described in a series of dogs with various types of orofacial tumors. The results suggest that epithelial tumors could have higher BV and BF than mesenchymal tumors, although larger sample sizes are needed to support these preliminary findings.

The dynamics and prognostic value of FDG PET-metrics in weekly monitoring of (chemo)radiotherapy for NSCLC.

PURPOSE: To test if the relative change in FDG-PET SUVmax over the course of treatment was associated with disease progression and overall survival. Additionally, the prognostic values of other first-order PET-metric changes were investigated. METHODS: The study included 38 patients with stage II-III NSCLC, who underwent concurrent chemoradiotherapy. Patients received two pre-treatment FDG-PET scans and four during-treatment scans at weekly intervals. SUVmax was normalized to the start of treatment and analyzed using linear regression. Linear regression coefficients of other first order PET-metrics were grouped according to dissimilarity. Associations to patient outcome were analyzed using Cox hazard ratio. RESULTS: Twenty-eight patients satisfied the criteria for analysis. All PET-metrics demonstrated a strong linear correlation with time during treatment [median R-range: -0.87: -0.97]. No strong associations (p > 0.10) were found for the relative slope of SUVmax to patient outcomes. Other first-order metrics did correlate with outcome but the single imaging time-point maximizing the association of PET response with outcome varied per PET metric and outcome parameter. CONCLUSION: All investigated FDG PET metrics linearly decreased during treatment. Relative change in SUVmax was not associated to patient outcome while several other first order PET-metrics were related to patient outcome. A single optimal imaging time-point could not be identified.

Single-Center Prospective Trial Investigating the Feasibility of Serial FDG-PET Guided Adaptive Radiation Therapy for Head and Neck Cancer.

PURPOSE: We investigated in a single-center prospective trial (NCT03376386) the use of serial fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) to determine the boost dose and to guide boost segmentation in head and neck cancer. METHODS AND MATERIALS: Patients were eligible when treated with curative radiation therapy with or without systemic treatment for T2-4 squamous cell carcinoma of the hypopharynx, larynx, or oropharynx (20 patients in total). FDG-PET/CT scans were made at baseline and for redelineation and replanning at the end of weeks 2 and 4 of radiation therapy. The metabolically active part of the primary tumor received a 4 Gy boost on top of the 70 Gy baseline dose per partial metabolic response. The study would be considered feasible when ≥80% of adaptations were successful and no Common Terminology Criteria for Adverse Events grade ≥4 acute toxicity occurred. RESULTS: One patient received 70 Gy after complete metabolic response in week 2, and 12 patients received 78 Gy because of partial metabolic response at weeks 2 and 4. Seven patients received 74 Gy, either because of complete metabolic response at week 4 (n = 3) or a missed FDG-PET/CT (n = 4). The patients missed their FDG-PET/CT scans because they did not fast (n = 2) or at patients' request (n = 2). In addition to the 4 missed FDG-PET/CT scans, 2 adaptive plans could not be finished successfully owing to logistical problems. In total, 85% of adaptations were completed correctly. No patient experienced grade ≥4 toxicity, and 40% had grade 3 dysphagia (tube feeding) during treatment. This decreased at 12 weeks posttreatment to 20%. CONCLUSIONS: This prospective trial demonstrates the feasibility of serial FDG-PET/CT scans for dose escalation and patient selection.

Robust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any?

In locally advanced lung cancer, established baseline clinical variables show limited prognostic accuracy and 18F-fluorodeoxyglucose positron emission tomography (FDG PET) radiomics features may increase accuracy for optimal treatment selection. Their robustness and added value relative to current clinical factors are unknown. Hence, we identify robust and independent PET radiomics features that may have complementary value in predicting survival endpoints. A 4D PET dataset (n = 70) was used for assessing the repeatability (Bland-Altman analysis) and independence of PET radiomics features (Spearman rank: |ρ|<0.5). Two 3D PET datasets combined (n = 252) were used for training and validation of an elastic net regularized generalized logistic regression model (GLM) based on a selection of clinical and robust independent PET radiomics features (GLMall). The fitted model performance was externally validated (n = 40). The performance of GLMall (measured with area under the receiver operating characteristic curve, AUC) was highest in predicting 2-year overall survival (0.66±0.07). No significant improvement was observed for GLMall compared to a model containing only PET radiomics features or only clinical variables for any clinical endpoint. External validation of GLMall led to AUC values no higher than 0.55 for any clinical endpoint. In this study, robust independent FDG PET radiomics features did not have complementary value in predicting survival endpoints in lung cancer patients. Improving risk stratification and clinical decision making based on clinical variables and PET radiomics features has still been proven difficult in locally advanced lung cancer patients.

The Prognostic Value of Baseline 18F-FDG PET/CT in Human Papillomavirus-Positive Versus Human Papillomavirus-Negative Patients With Oropharyngeal Cancer.

PURPOSE: Oropharynx cancer (OPC) is heterogeneous; human papillomavirus (HPV)-positive and HPV tumors represent 2 disease entities with a different prognosis. Earlier studies investigating the prognostic value of pretreatment F-FDG PET in OPC are small or included patients with unknown HPV status. This study assessed the prognostic value of PET variables, in a large cohort with balanced HPV status. METHODS: Retrospectively, primary tumor SUVmax, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were extracted from baseline FDG PET/CT of patients with OPC treated with (chemo)radiation. The Pearson correlation between the PET variables was calculated. With linear regression, the correlation between the PET variables and HPV status, age, smoking status, T stage, N stage, and American Joint Committee on Cancer stage was calculated. Univariable and multivariable Cox models analyzed local control, overall survival, and disease-free survival (DFS). RESULTS: Of 201 patients, 109 were HPV. Metabolic tumor volume and TLG correlated (r = 0.96), as did SUVpeak and SUVmax (r = 0.97). The PET variables correlated strongest with HPV status and T stage. These two accounted for 40% of the variance of MTV and 33% of TLG. Human papillomavirus-negative tumors had a significantly higher SUVmax, SUVpeak, MTV, and TLG. In univariable analysis, all PET variables were significantly associated with local control, overall survival, and DFS. In multivariable analysis, TLG was significantly associated to DFS in patients with HPV OPC (hazard ratio, 1.005; 95% confidence interval, 1.001-1.010; P = 0.03). However, we did not observe this in HPV patients. CONCLUSIONS: Increased baseline TLG is associated with worse DFS in HPV OPC and might be used as biomarker for risk stratification in these patients. Interestingly, we could not identify this association in HPV patients.

Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer.

BACKGROUND AND PURPOSE: We aimed to identify tumour subregions with characteristic phenotypes based on pre-treatment multi-parametric functional imaging and correlate these subregions to treatment outcome. The subregions were created using imaging of metabolic activity (FDG-PET/CT), hypoxia (HX4-PET/CT) and tumour vasculature (DCE-CT). MATERIALS AND METHODS: 36 non-small cell lung cancer (NSCLC) patients underwent functional imaging prior to radical radiotherapy. Kinetic analysis was performed on DCE-CT scans to acquire blood flow (BF) and volume (BV) maps. HX4-PET/CT and DCE-CT scans were non-rigidly co-registered to the planning FDG-PET/CT. Two clustering steps were performed on multi-parametric images: first to segment each tumour into homogeneous subregions (i.e. supervoxels) and second to group the supervoxels of all tumours into phenotypic clusters. Patients were split based on the absolute or relative volume of supervoxels in each cluster; overall survival was compared using a log-rank test. RESULTS: Unsupervised clustering of supervoxels yielded four independent clusters. One cluster (high hypoxia, high FDG, intermediate BF/BV) related to a high-risk tumour type: patients assigned to this cluster had significantly worse survival compared to patients not in this cluster (p = 0.035). CONCLUSIONS: We designed a subregional analysis for multi-parametric imaging in NSCLC, and showed the potential of subregion classification as a biomarker for prognosis. This methodology allows for a comprehensive data-driven analysis of multi-parametric functional images.

Predicting tumor hypoxia in non-small cell lung cancer by combining CT, FDG PET and dynamic contrast-enhanced CT.

BACKGROUND: Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT). MATERIAL AND METHODS: For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUVmean) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared. RESULTS: Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm3) by the best performing model. CONCLUSIONS: We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.