Fractal and multifractal analysis of PET-CT images of metastatic melanoma before and after treatment with ipilimumab

PET/CT with F-18-Fluorodeoxyglucose (FDG) images of patients suffering from metastatic melanoma have been analysed using fractal and multifractal analysis to assess the impact of monoclonal antibody ipilimumab treatment with respect to therapy outcome. Our analysis shows that the fractal dimensions which describe the tracer dispersion in the body decrease consistently with the deterioration of the patient therapeutic outcome condition. In 20 out-of 24 cases the fractal analysis results match those of the medical records, while 7 cases are considered as special cases because the patients have non-tumour related medical conditions or side effects which affect the results. The decrease in the fractal dimensions with the deterioration of the patient conditions (in terms of disease progression) are attributed to the hierarchical localisation of the tracer which accumulates in the affected lesions and does not spread homogeneously throughout the body. Fractality emerges as a result of the migration patterns which the malignant cells follow for propagating within the body (circulatory system, lymphatic system). Analysis of the multifractal spectrum complements and supports the results of the fractal analysis. In the kinetic Monte Carlo modelling of the metastatic process a small number of malignant cells diffuse throughout a fractal medium representing the blood circulatory network. Along their way the malignant cells engender random metastases (colonies) with a small probability and, as a result, fractal spatial distributions of the metastases are formed similar to the ones observed in the PET/CT images. In conclusion, we propose that fractal and multifractal analysis has potential application in the quantification of the evaluation of PET/CT images to monitor the disease evolution as well as the response to different medical treatments.Comment: 38 pages, 9 figure

Correlation of Dynamic PET and Gene Array Data in Patients with Gastrointestinal Stromal Tumors

Introduction. The results obtained with dynamic PET (dPET) were compared to gene expression data obtained in patients with gastrointestinal stromal tumors (GIST). The primary aim was to assess the association of the dPET results and gene expression data. Material and Methods. dPET was performed following the injection of F-18-fluorodeoxyglucose (FDG) in 22 patients with GIST. All patients were examined prior to surgery for staging purpose. Compartment and noncompartment models were used for the quantitative evaluation of the dPET examinations. Gene array data were based on tumor specimen obtained by surgery after the PET examinations. Results. The data analysis revealed significant correlations for the dPET parameters and the expression of zinc finger genes (znf43, znf85, znf91, znf189). Furthermore, the transport of FDG (k1) was associated with VEGF-A. The cell cycle gene cyclin-dependent kinase inhibitor 1C was correlated with the maximum tracer uptake (SUVmax) in the tumors. Conclusions. The data demonstrate a dependency of the tracer kinetics on genes associated with prognosis in GIST. Furthermore, angiogenesis and cell proliferation have an impact on the tracer uptake

Comparison between 68Ga-bombesin (68Ga-BZH3) and the cRGD tetramer 68Ga-RGD4 studies in an experimental nude rat model with a neuroendocrine pancreatic tumor cell line

BACKGROUND: Serotonergic neurons in the rodent hypothalamus are implicated in key neuroendocrine and metabolic functions, including circadian rhythmicity. However, the assessment of the serotonergic system in the human hypothalamus in vivo is difficult as delineation of the hypothalamus is cumbersome with conventional region-of-interest analysis. In the present study, we aimed to develop a method to visualize serotonin transporters (SERT) in the hypothalamus. Additionally, we tested the hypothesis that hypothalamic SERT binding ratios are different between patients with hypothalamic impairment (HI), pituitary insufficiency (PI), and control subjects (C). METHODS: SERT availability was determined in 17 subjects (6 HI, 5 PI, and 6 healthy controls), 2 h after injection of 123I-N-ω-fluoropropyl-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane ([123I]FP-CIT), using single-photon emission computed tomography (performed on a brain-dedicated system) fused with individual magnetic resonance imaging (MRI) scans of the brain. The hypothalamus (representing specific SERT binding) and cerebellum (representing nonspecific binding) were manually delineated on each MRI to assess [123I]FP-CIT binding and specific-to-nonspecific binding ratios. RESULTS: In each healthy subject, [123I]FP-CIT binding was higher in the hypothalamus than in the cerebellum, and the mean hypothalamic binding ratio of SERT was 0.29 ± 0.23. We found no difference in hypothalamic binding ratios between HI, PI, and control subjects (HI 0.16 ± 0.24, PI 0.45 ± 0.39, C 0.29 ± 0.23, p value 0.281). CONCLUSIONS: We were able to demonstrate SERT binding in the human hypothalamus in vivo. However, we did not find altered hypothalamic SERT binding in patients with hypothalamic impairment. TRIAL REGISTRATION: Netherlands Trial Register: NTR2520

The cientificWorldJOURNAL Clinical Study Correlation of Dynamic PET and Gene Array Data in Patients with Gastrointestinal Stromal Tumors

Introduction. The results obtained with dynamic PET (dPET) were compared to gene expression data obtained in patients with gastrointestinal stromal tumors (GIST). The primary aim was to assess the association of the dPET results and gene expression data. Material and Methods. dPET was performed following the injection of F-18-fluorodeoxyglucose (FDG) in 22 patients with GIST. All patients were examined prior to surgery for staging purpose. Compartment and noncompartment models were used for the quantitative evaluation of the dPET examinations. Gene array data were based on tumor specimen obtained by surgery after the PET examinations. Results. The data analysis revealed significant correlations for the dPET parameters and the expression of zinc finger genes (znf43, znf85, znf91, znf189). Furthermore, the transport of FDG (k1) was associated with VEGF-A. The cell cycle gene cyclin-dependent kinase inhibitor 1C was correlated with the maximum tracer uptake (SUVmax) in the tumors. Conclusions. The data demonstrate a dependency of the tracer kinetics on genes associated with prognosis in GIST. Furthermore, angiogenesis and cell proliferation have an impact on the tracer uptake

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

Purpose: [18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients. Materials and methods: Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1–6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian 7 1.1 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 2: liver SUVmedian 7 1.5 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 3: liver SUVmedian 7 2 (axial skeleton), gluteal muscles SUVmedian 7 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients. Results: BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of β2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5. Conclusions: The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts

68Ga-PSMA-11 Dynamic PET/CT Imaging in Primary Prostate Cancer.

PURPOSE The aim of our study is to assess the pharmacokinetics and biodistribution of Ga-PSMA-11 in patients suffering from primary prostate cancer (PC) by means of dynamic and whole-body PET/CT. MATERIALS AND METHODS Twenty-four patients with primary, previously untreated PC were enrolled in the study. All patients underwent dynamic PET/CT (dPET/CT) scanning of the pelvis and whole-body PET/CT studies with Ga-PSMA-11. The evaluation of dPET/CT studies was based on qualitative evaluation, SUV calculation, and quantitative analysis based on two-tissue compartment modeling and a noncompartmental approach leading to the extraction of fractal dimension (FD). RESULTS A total of 23/24 patients (95.8%) were Ga-PSMA-11 positive. In 9/24 patients (37.5%), metastatic lesions were detected. PC-associated lesions demonstrated the following mean values: SUVaverage = 14.3, SUVmax = 23.4, K1 = 0.24 (1/min), k3 = 0.34 (1/min), influx = 0.15 (1/min), and FD = 1.27. The parameters SUVaverage, SUVmax, k3, influx, and FD derived from PC-associated lesions were significantly higher than respective values derived from reference prostate tissue. Time-activity curves derived from PC-associated lesions revealed an increasing Ga-PSMA-11 accumulation during dynamic PET acquisition. Correlation analysis revealed a moderate but significant correlation between PSA levels and SUVaverage (r = 0.60) and SUVmax (r = 0.57), and a weak but significant correlation between Gleason score and SUVaverage (r = 0.33) and SUVmax (r = 0.28). CONCLUSION Ga-PSMA-11 PET/CT confirmed its capacity in detecting primary PC with a detection rate of 95.8%. Dynamic PET/CT studies of the pelvis revealed an increase in tracer uptake in PC-associated lesions during the 60 minutes of dynamic PET acquisition, a finding with potential applications in anti-PSMA approaches

SIMON: A Multi-Strategy Classification Approach Resolving Ontology Heterogeneity-The P2P Meets the Semantic Web 1

Abstract. The semantic web technology is seen as a key to realizing peer-topeer for resource discovery and service combination in the ubiquitous communication environment. However, in a Peer-to-Peer environment, we must face the situation, where individual peers maintain their own view of the domain in terms of the organization of the local information sources. Ontology heterogeneity among individual peers is becoming ever more important issues. In this paper, we propose a multi-strategy learning approach to resolve the problem. We describe the SIMON (Semantic Interoperation by Matching between ONtologies) system, which applies multiple classification methods to learn the matching between ontologies. We use the general statistic classification method to discover category features in data instances and use the first-order learning algorithm FOIL to exploit the semantic relations among data instances. On the prediction results of individual methods, the system combines their outcomes using our matching committee rule called the Best Outstanding Champion. The experiments show that SIMON system achieves high accuracy on real-world domain.

Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives

Dynamic PET (dPET) studies have been used until now primarily within research purposes. Although it is generally accepted that the information provided by dPET is superior to that of conventional static PET acquisitions acquired usually 60 min post injection of the radiotracer, the duration of dynamic protocols, the limited axial field of view (FOV) of current generation clinical PET systems covering a relatively small axial extent of the human body for a dynamic measurement, and the complexity of data evaluation have hampered its implementation into clinical routine. However, the development of new-generation PET/CT scanners with an extended FOV as well as of more sophisticated evaluation software packages that offer better segmentation algorithms, automatic retrieval of the arterial input function, and automatic calculation of parametric imaging, in combination with dedicated shorter dynamic protocols, will facilitate the wider use of dPET. This is expected to aid in oncological diagnostics and therapy assessment. The aim of this review is to present some general considerations about dPET analysis in oncology by means of kinetic modeling, based on compartmental and noncompartmental approaches, and parametric imaging. Moreover, the current clinical applications and future perspectives of the modality are outlined