Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes

This study evaluates the relevance of 18F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic 18F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features—radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both—in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBRmean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values.

Accurate Total-Body Ki Parametric Imaging with Shortened Dynamic 18F-FDG PET Scan Durations via Effective Data Processing

Background: Total-body dynamic PET (dPET) imaging using 18F-fluorodeoxyglucose (18F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately one hour seriously limits the application and promotion of this imaging technique. In this study, using Patlak analysis-based Ki parametric imaging as the evaluation standard, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 mins post-injection (PI) with the help of a novel Patlak data processing algorithm.Methods: Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using 18F-FDG of 15 patients with different types of tumors were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired one hour PI. Patlak analysis-based Ki parametric imaging was carried out based on the imaging data corresponding to the first 30 mins PI, during which a Patlak data processing method based on third-order Hermite interpolation (THI) was applied. The resulting Ki images and standard Ki images were compared in terms of visual imaging effect and Ki estimation accuracy to evaluate the performance of the proposed data processing algorithm for parametric imaging with dPET with a shortened scan duration.Results: With the help of Patlak data processing, acceptable Ki parametric images were obtained from dPET data acquired with a shortened scan duration. Compared to Ki images obtained from unprocessed Patlak data, the resulting images from the proposed method contained less image noise, leading to remarkably improved imaging quality. Moreover, box plot analysis showed that that 30-min Ki images obtained from processed Patlak data have higher accuracy regarding tumor lesion Ki values.Conclusion: Acceptable Ki parametric images can be acquired from dynamic imaging data corresponding to the first 30 mins PI. Patlak data processing can help achieve higher Ki imaging quality and higher accuracy regarding tumor lesion Ki values. Clinically, it is possible to shorten the dynamic scan duration of 18F-FDG PET to 30 mins to facilitate the usage of such imaging techniques on uEXPLORER scanners.

Impact of Inhibition of the Mitochondrial Pyruvate Carrier on the Tumor Extracellular pH as Measured by CEST-MRI

(1) Background: The acidosis of the tumor micro-environment may have profound impact on cancer progression and on the efficacy of treatments. In the present study, we evaluated the impact of a treatment with UK-5099, a mitochondrial pyruvate carrier (MPC) inhibitor on tumor extracellular pH (pHe); (2) Methods: glucose consumption, lactate secretion and extracellular acidification rate (ECAR) were measured in vitro after exposure of cervix cancer SiHa cells and breast cancer 4T1 cells to UK-5099 (10 µM). Mice bearing the 4T1 tumor model were treated daily during four days with UK-5099 (3 mg/kg). The pHe was evaluated in vivo using either chemical exchange saturation transfer (CEST)-MRI with iopamidol as pHe reporter probe or 31P-NMR spectroscopy with 3-aminopropylphosphonate (3-APP). MR protocols were applied before and after 4 days of treatment; (3) Results: glucose consumption, lactate release and ECAR were increased in both cell lines after UK-5099 exposure. CEST-MRI showed a significant decrease in tumor pHe of 0.22 units in UK-5099-treated mice while there was no change over time for mice treated with the vehicle. Parametric images showed a large heterogeneity in response with 16% of voxels shifting to pHe values under 7.0. In contrast, 31P-NMR spectroscopy was unable to detect any significant variation in pHe; (4) Conclusions: MPC inhibition led to a moderate acidification of the extracellular medium in vivo. CEST-MRI provided high resolution parametric images (0.44 µL/voxel) of pHe highlighting the heterogeneity of response within the tumor when exposed to UK-5099.

Valuable Characteristic of Patlak Parametric Imaging Based on Total-Body Dynamic PET Imaging: Higher Contrast For Tumor Lesions With Respect To Hypermetabolic Tissues

Purpose To demonstrate the characteristics of high-contrast tumor lesions on total-body dynamic positron emission tomography (dPET) parametric images qualitatively and quantitatively. Method We reported the results of Patlak parametric images based on total-body dPET images of four patients with different types of tumor lesions. The contrast-to-noise ratios (CNRs) of the target tumor lesions were calculated with respect to hypermetabolic tissues, including the liver and ventricles, both on static PET and parametric images. Results Visual comparisons between the last frame of total-body dPET images and the generated parametric images illustrated the higher contrast of tumor lesions relative to other tissues in the patients. Visualization of the tumor lesions was reserved, while that of the livers and ventricles was diminished. The parametric images resulted in higher CNR values for the tumor lesions with respect to livers and ventricles compared to those given by dynamic PET images. The results were consistent in all the cases analyzed in this study. Conclusion Patlak parametric imaging provides the valuable characteristic of higher contrast for tumor lesions than hypermetabolic tissues, which helps in the clinical detection and diagnosis of tumor tissues.

Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

The efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

Statistical Model Based Breast Tumor Classification in Contourlet Transform Domain

Determination of breast tumors from B-Mode Ultrasound (US) image is a perplexing one. Researches employing statistical modeling such as Nakagami, Normal Inverse Gaussian (NIG) distributed parametric images in this classification task have already explored but experimentation of those statistical models on contourlet transformed coefficient image in breast tumor classification task has not reported yet. The proposed method is established by considering 250 clinical cases from a publicly available database. In this database each clinical case exists as *.bmp format. In the preprocessing step firstly, the ultrasound B-Mode image is binarized to detect the lesion contour. Then contourlet transformation is employed. These contourlet sub band coefficients are shown to be modeled effectively by Nakagami and NIG distributions. These Nakagami and NIG parametric images are obtained by estimating the parameters of those prior statistical distributions locally. Few shape and statistical features are chosen according to their effectiveness on those parametric images. The benign and malignant breast tumors are classified utilizing these features with different classifiers such as the support vector machine, k-nearest neighbors, fitted binary classification decision tree, binary Gaussian kernel classification model, linear classification models for binary learning with high-dimensional etc. It is observed that classification performance of NIG statistical model based parametric version of contourlet coefficient images gained better accuracy than those of Nakagami statistical model. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 21-26

Region- and voxel-based quantification in human brain of [18F]LSN3316612, a radioligand for O-GlcNAcase

Background Previous studies found that the positron emission tomography (PET) radioligand [18F]LSN3316612 accurately quantified O-GlcNAcase in human brain using a two-tissue compartment model (2TCM). This study sought to assess kinetic model(s) as an alternative to 2TCM for quantifying [18F]LSN3316612 binding, particularly in order to generate good-quality parametric images. Methods The current study reanalyzed data from a previous study of 10 healthy volunteers who underwent both test and retest PET scans with [18F]LSN3316612. Kinetic analysis was performed at the region level with 2TCM using 120-min PET data and arterial input function, which was considered as the gold standard. Quantification was then obtained at both the region and voxel levels using Logan plot, Ichise's multilinear analysis-1 (MA1), standard spectral analysis (SA), and impulse response function at 120 min (IRF120). To avoid arterial sampling, a noninvasive relative quantification (standardized uptake value ratio (SUVR)) was also tested using the corpus callosum as a pseudo-reference region. Venous samples were also assessed to see whether they could substitute for arterial ones. Results Logan and MA1 generated parametric images of good visual quality and their total distribution volume (VT) values at both the region and voxel levels were strongly correlated with 2TCM-derived VT (r = 0.96–0.99) and showed little bias (up to − 8%). SA was more weakly correlated to 2TCM-derived VT (r = 0.93–0.98) and was more biased (~ 16%). IRF120 showed a strong correlation with 2TCM-derived VT (r = 0.96) but generated noisier parametric images. All techniques were comparable to 2TCM in terms of test–retest variability and reliability except IRF120, which gave significantly worse results. Noninvasive SUVR values were not correlated with 2TCM-derived VT, and arteriovenous equilibrium was never reached. Conclusions Compared to SA and IRF, Logan and MA1 are more suitable alternatives to 2TCM for quantifying [18F]LSN3316612 and generating good-quality parametric images.