Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI.

The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based radiomics analysis of magnetic resonance imaging (MRI) data. This multi-institutional study included a retrospective analysis of patients diagnosed with glioblastoma who underwent surgery with complete resection of the enhancing tumor. Fifty-five patients met the selection criteria. The study sample was split into training (N = 40) and testing (N = 15) datasets. Follow-up MRI was used for ground truth definition, and postoperative structural multiparametric MRI was used to extract voxel-based radiomic features. Deformable coregistration was used to register the MRI sequences for each patient, followed by segmentation of the peritumoral region in the postoperative scan and the enhancing tumor in the follow-up scan. Peritumoral voxels overlapping with enhancing tumor voxels were labeled as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Voxel-based radiomic features were extracted from the peritumoral region. Four machine learning-based classifiers were trained for recurrence prediction. A region-based evaluation approach was used for model evaluation. The Categorical Boosting (CatBoost) classifier obtained the best performance on the testing dataset with an average area under the curve (AUC) of 0.81 ± 0.09 and an accuracy of 0.84 ± 0.06, using region-based evaluation. There was a clear visual correspondence between predicted and actual recurrence regions. We have developed a method that accurately predicts the region of future tumor recurrence in MRI scans of glioblastoma patients. This could enable the adaptation of surgical and radiotherapy treatment to these areas to potentially prolong the survival of these patients.

Positron emission tomography and single photon emission computed tomography imaging of tertiary lymphoid structures during the development of lupus nephritis.

Lymphoid neogenesis occurs in tissues targeted by chronic inflammatory processes, such as infection and autoimmunity. In systemic lupus erythematosus (SLE), such structures develop within the kidneys of lupus-prone mice ((NZBXNZW)F1) and are observed in kidney biopsies taken from SLE patients with lupus nephritis (LN). The purpose of this prospective longitudinal animal study was to detect early kidney changes and tertiary lymphoid structures (TLS) using in vivo imaging. Positron emission tomography (PET) by tail vein injection of 18-F-fluoro-2-deoxy-D-glucose (18F-FDG)(PET/FDG) combined with computed tomography (CT) for anatomical localization and single photon emission computed tomography (SPECT) by intraperitoneal injection of 99mTC labeled Albumin Nanocoll (99mTC-Nanocoll) were performed on different disease stages of NZB/W mice (n = 40) and on aged matched control mice (BALB/c) (n = 20). By using one-way ANOVA analyses, we compared two different compartmental models for the quantitative measure of 18F-FDG uptake within the kidneys. Using a new five-compartment model, we observed that glomerular filtration of 18FFDG in lupus-prone mice decreased significantly by disease progression measured by anti-dsDNA Ab production and before onset of proteinuria. We could not visualize TLS within the kidneys, but we were able to visualize pancreatic TLS using 99mTC Nanocoll SPECT. Based on our findings, we conclude that the five-compartment model can be used to measure changes of FDG uptake within the kidney. However, new optimal PET/SPECT tracer administration sites together with more specific tracers in combination with magnetic resonance imaging (MRI) may make it possible to detect formation of TLS and LN before clinical manifestations.

Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function.

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of 15O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects.Twenty-five subjects underwent two 10 min dynamic 15O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF.The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using 15O-water PET and kinetic modelling.

Machine learning derived input-function in a dynamic 18F-FDG PET study of mice.

Tracer kinetic modelling, based on dynamic 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. Our aim was to explore two non-invasive machine learning-based models, for AIF prediction in a small-animal dynamic FDG PET study. 7 tissue regions were delineated in images from 68 FDG PET/computed tomography mouse scans. Two machine learning-based models were trained for AIF prediction, based on Gaussian processes (GP) and a long short-term memory (LSTM) recurrent neural network, respectively. Because blood data were unavailable, a reference AIF was formed by fitting an established AIF model to vena cava and left ventricle image data. The predicted and reference AIFs were compared by the area under curve (AUC) and root mean square error (RMSE). Net-influx rate constants, K i , were calculated with a two-tissue compartment model, using both predicted and reference AIFs for three tissue regions in each mouse scan, and compared by means of error, ratio, correlation coefficient, P value and Bland-Altman analysis. The impact of different tissue regions on AIF prediction was evaluated by training a GP and an LSTM model on subsets of tissue regions, and calculating the RMSE between the reference and the predicted AIF curve. Both models generated AIFs with AUCs similar to reference. The LSTM models resulted in lower AIF RMSE, compared to GP. K i from both models agreed well with reference values, with no significant differences. Myocardium was highlighted as important for AIF prediction, but AIFs with similar RMSE were obtained also without myocardium in the input data. Machine learning can be used for accurate and non-invasive prediction of an image-derived reference AIF in FDG studies of mice. We recommend the LSTM approach, as this model predicts AIFs with lower errors, compared to GP.

First in vivo evaluation of a potential SPECT brain radiotracer for the gonadotropin releasing hormone receptor.

OBJECTIVES: In vivo evaluations of a gonadotropin releasing hormone-receptor single photon emission computed tomography radiotracer for non-invasive detection of gonadotropin releasing homone-receptors in brain. RESULTS: We have used a simple, robust and high-yielding procedure to radiolabel an alpha-halogenated bioactive compound with high radiochemical yield. Literature findings showed similar alpha-halogenated compounds suitable for in vivo evaluations. The compound was found to possess nano molar affinity for the gonadotropin releasing hormone-receptor in a competition dependent inhibition study. Furthermore, liquid chromatography-mass spectrometry analysis in saline, human and rat serum resulted in 46%, 52% and 44% stability after incubation for 1 h respectively. In addition, rat brain single photon emission computed tomography and biodistribution studies gave further insight into the nature of the compound as a radiotracer.

The interplay of matrix metalloproteinase-8, transforming growth factor-ß1 and vascular endothelial growth factor-C cooperatively contributes to the aggressiveness of oral tongue squamous cell carcinoma.

BACKGROUND: Matrix metalloproteinase-8 (MMP-8) has oncosuppressive properties in various cancers. We attempted to assess MMP-8 function in oral tongue squamous cell carcinoma (OTSCC). METHODS: MMP-8 overexpressing OTSCC cells were used to study the effect of MMP-8 on proliferation, apoptosis, migration, invasion and gene and protein expression. Moreover, MMP-8 functions were assessed in the orthotopic mouse tongue cancer model and by immunohistochemistry in patient samples. RESULTS: MMP-8 reduced the invasion and migration of OTSCC cells and decreased the expression of MMP-1, cathepsin-K and vascular endothelial growth factor-C (VEGF-C). VEGF-C was induced by transforming growth factor-ß1 (TGF-ß1) in control cells, but not in MMP-8 overexpressing cells. In human OTSCC samples, low MMP-8 in combination with high VEGF-C was an independent predictor of poor cancer-specific survival. TGF-ß1 treatment also restored the migration of MMP-8 overexpressing cells to the level of control cells. In mouse tongue cancer, MMP-8 did not inhibit metastasis, possibly because it was eliminated in the peripheral carcinoma cells. CONCLUSIONS: The suppressive effects of MMP-8 in OTSCC may be mediated through interference of TGF-ß1 and VEGF-C function and altered proteinase expression. Together, low MMP-8 and high VEGF-C expression have strong independent prognostic value in OTSCC.

Ascending aorta of hooded seals with particular emphasis on its vasa vasorum.

The pressure-volume relationship in the ascending aorta ("windkessel") of the hooded seal was determined and the morphology of its vasa vasorum described in some detail. We found that the ascending aorta has a high compliance and can easily accommodate the entire stroke volume when the peripheral vascular resistance becomes much increased and maintain perfusion pressure during the much extended diastole and thereby reduce cardiac stroke work during diving. We also found that the 3- to 5-mm thick wall of the ascending aorta had a very elaborate vasa vasorum interna with a hitherto undescribed vascular structure that penetrates the entire vascular wall. If similar structures with similar importance for the nutrition of the wall of the vessel are found in humans, important implications for the understanding of pathological conditions, such as aneurisms, may be indicated.

A proposed protocol for acceptance and constancy control of computed tomography systems: a Nordic Association for Clinical Physics (NACP) work group report.

BACKGROUND: Quality assurance (QA) of computed tomography (CT) systems is one of the routine tasks for medical physicists in the Nordic countries. However, standardized QA protocols do not yet exist and the QA methods, as well as the applied tolerance levels, vary in scope and extent at different hospitals. PURPOSE: To propose a standardized protocol for acceptance and constancy testing of CT scanners in the Nordic Region. MATERIAL AND METHODS: Following a Nordic Association for Clinical Physics (NACP) initiative, a group of medical physicists, with representatives from four Nordic countries, was formed. Based on international literature and practical experience within the group, a comprehensive standardized test protocol was developed. RESULTS: The proposed protocol includes tests related to the mechanical functionality, X-ray tube, detector, and image quality for CT scanners. For each test, recommendations regarding the purpose, equipment needed, an outline of the test method, the measured parameter, tolerance levels, and the testing frequency are stated. In addition, a number of optional tests are briefly discussed that may provide further information about the CT system. CONCLUSION: Based on international references and medical physicists' practical experiences, a comprehensive QA protocol for CT systems is proposed, including both acceptance and constancy tests. The protocol may serve as a reference for medical physicists in the Nordic countries.