Volume Of Interest Research Articles

Dynamic rubidium-82 PET/CT as a novel tool for quantifying hemodynamic differences in renal blood flow using a one-tissue compartment model.

Assessing renal perfusion in-vivo is challenging and quantitative information regarding renal hemodynamics is hardly incorporated in medical decision-making while abnormal renal hemodynamics might play a crucial role in the onset and progression of renal disease. Combining physiological stimuli with rubidium-82 positron emission tomography/computed tomography (82Rb PET/CT) offers opportunities to test the kidney perfusion under various conditions. The aim of this study is: (1) to investigate the application of a one-tissue compartment model for measuring renal hemodynamics with dynamic 82Rb PET/CT imaging, and (2) to evaluate whether dynamic PET/CT is sensitive to detect differences in renal hemodynamics in stress conditions compared to resting state. A one-tissue compartment model for the kidney was applied to cardiac 82Rb PET/CT scans that were obtained for ischemia detection as part of clinical care. Retrospective data, collected from 17 patients undergoing dynamic myocardial 82Rb PET/CT imaging in rest, were used to evaluate various CT-based volumes of interest (VOIs) of the kidney. Subsequently, retrospective data, collected from 10 patients (five impaired kidney functions and five controls) undergoing dynamic myocardial 82Rb PET/CT imaging, were used to evaluate image-derived input functions (IDIFs), PET-based VOIs of the kidney, extraction fractions, and whether dynamic 82Rb PET/CT can measure renal hemodynamics differences using the renal blood flow (RBF) values in rest and after exposure to adenosine pharmacological stress. The delivery rate (K1) values showed no significant (p=0.14) difference between the mean standard deviation (SD) K1 values using one CT-based VOI and the use of two, three, and four CT-based VOIs, respectively 2.01(0.32), 1.90(0.40), 1.93(0.39), and 1.94(0.40) mL/min/mL. The ratio between RBF in rest and RBF in pharmacological stress for the controls were overall significantly lower compared to the impaired kidney function group for both PET-based delineation methods (region growing and iso-contouring), with the smallest median interquartile range (IQR) of 0.40(0.28-0.66) and 0.96(0.62-1.15), respectively (p<0.05). The K1 of the impaired kidney function group were close to 1.0mL/min/mL. This study demonstrated that obtaining renal K1 and RBF values using 82Rb PET/CT was feasible using a one-tissue compartment model. Applying iso-contouring as the PET-based VOI of the kidney and using AA as an IDIF is suggested for consideration in further studies. Dynamic 82Rb PET/CT imaging showed significant differences in renal hemodynamics in rest compared to when exposed to adenosine. This indicates that dynamic 82Rb PET/CT has potential to detect differences in renal hemodynamics in stress conditions compared to the resting state, and might be useful as a novel diagnostic tool for assessing renal perfusion.

Finite element study on the micromechanics of cement-augmented proximal femoral nail anti-rotation (PFNA) for intertrochanteric fracture treatment

Blade cut-out is a common complication when using proximal femoral nail anti-rotation (PFNA) for the treatment of intertrochanteric fractures. Although cement augmentation has been introduced to overcome the cut-out effect, the micromechanics of this approach remain to be clarified. While previous studies have developed finite element (FE) models based on lab-prepared or cadaveric samples to study the cement-trabeculae interface, their demanding nature and inherent disadvantages limit their application. The aim of this study was to develop a novel 'one-step forming' method for creating a cement-trabeculae interface FE model to investigate its micromechanics in relation to PFNA with cement augmentation. A human femoral head was scanned using micro-computed tomography, and four volume of interest (VOI) trabeculae were segmented. The VOI trabeculae were enclosed within a box to represent the encapsulated region of bone cement using ANSYS software. Tetrahedral meshing was performed with Hypermesh software based on Boolean operation. Finally, four cement-trabeculae interface FE models comprising four interdigitated depths and five FE models comprising different volume fraction were established after element removal. The effects of friction contact, frictionless contact, and bond contact properties between the bone and cement were identified. The maximum micromotion and stress in the interdigitated and loading bones were quantified and compared between the pre- and post-augmentation situations. The differences in micromotion and stress with the three contact methods were minimal. Micromotion and stress decreased as the interdigitation depth increased. Stress in the proximal interdigitated bone showed a correlation with the bone volume fraction (R2 = 0.70); both micromotion (R2 = 0.61) and stress (R2 = 0.93) at the most proximal loading region exhibited a similar correlation tendency. When comparing the post- and pre-augmentation situations, micromotion reduction in the interdigitated bone was more effective than stress reduction, particularly near the cement border. The cementation resulted in a significant reduction in micromotion within the loading bone, while the decrease in stress was minimal. Noticeable gradients of displacement and stress reduction can be observed in models with lower bone volume fraction (BV/TV). In summary, cement augmentation is more effective at reducing micromotion rather than stress. Furthermore, the reinforcing impact of bone cement is particularly prominent in cases with a low BV/TV. The utilization of bone cement may contribute to the stabilization of trabecular bone and PFNA primarily by constraining micromotion and partially shielding stress.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification

IntroductionSpatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images. MethodsWe propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR). ResultsIn total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05). ConclusionThe automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

Applications of magnetic resonance spectroscopy in diagnosis of neurodegenerative diseases: A systematic review

BackgroundMagnetic resonance spectroscopy (MRS) is an imaging technique used to measure metabolic changes in the tissue. Due to the lack of evidence, MRS is not a priority in diagnosing neurodegenerative diseases because it is a relatively specialized technique that requires specialized equipment and expertise to perform and interpret. This systematic review aimed to present a comprehensive collection of MRS results in the most common neurodegenerative diseases. MethodsA systematic search of four electronic databases (PubMed, Scopus, Web of Science, and ScienceDirect) was conducted for studies published from 2017 to 2022. Articles that provided specific biomarker levels were selected, and studies that assessed the diseases via treatment, featured MRS applying nuclei other than 1H, or compared different animal models were excluded. ResultsA total of 25 articles, plus 3 articles for extra information in the introduction, were included in this review. Six of the most common neurodegenerative diseases, i.e., Alzheimer's and Parkinson's disease, Huntington chorea, ataxia, multiple sclerosis (MS), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) were examined via MRS. The changes and ratios of N-acetylaspartate (NAA) could be seen in all of these disorders, which could lead to early diagnosis. However, there are other biomarkers, such as Cr and Chon, which can give convincing results. DiscussionThis observational study is the first synthesis of the latest evidence proving metabolic changes during neurodegenerative diseases using MRS as a diagnosis method. The findings indicate decreased N-acetylaspartate (NAA) and NAA/Cr ratios in Alzheimer's disease (AD), Parkinson's disease (PD), ataxias, and MS, reflecting neuronal loss or dysfunction. Increased choline and myo-inositol were noted in some studies, suggesting cell membrane turnover and neuroinflammation. Findings were less consistent for other metabolites like glutamate and gamma-aminobutyric acid. However, there were limitations due to the lack of studies on the same volumes of interest (VOIs) and the small number of participants.

Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?

Objectives: Radiomics and machine learning are innovative approaches to improve the clinical management of NSCLC. However, there is less information about the additive value of FDG PET-based radiomics compared with clinical and imaging variables. Methods: This retrospective study included 320 NSCLC patients who underwent PET/CT with FDG at initial staging. VOIs were placed on primary tumors only. We included a total of 94 variables, including 87 textural features extracted from PET studies, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. We used the least absolute shrinkage and selection operator (LASSO) regression to select variables with the highest predictive value. Although several radiomics variables are available, the added value of these predictors compared with clinical and imaging variables is still under evaluation. Three hundred and twenty NSCLC patients were included in this retrospective study and underwent 18F-FDG PET/CT at initial staging. In this study, we evaluated 94 variables, including 87 textural features, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. Image-based predictors were extracted from a volume of interest (VOI) positioned on the primary tumor. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to reduce the number of variables and select only those with the highest predictive value. The predictive model implemented with the variables selected using the LASSO analysis was compared with a reference model using only a tumor stage and SUVmax. Results: NGTDM coarseness, SUVmax, and TNM stage survived the LASSO analysis and were used for the radiomic model. The AUCs obtained from the reference and radiomic models were 80.82 (95%CI, 69.01-92.63) and 81.02 (95%CI, 69.07-92.97), respectively (p = 0.98). The median OS in the reference model was 17.0 months in high-risk patients (95%CI, 11-21) and 113 months in low-risk patients (HR 7.47, p < 0.001). In the radiomic model, the median OS was 16.5 months (95%CI, 11-20) and 113 months in high- and low-risk groups, respectively (HR 9.64, p < 0.001). Conclusions: Our results indicate that a radiomic model composed using the tumor stage, SUVmax, and a selected radiomic feature (NGTDM_Coarseness) predicts survival in NSCLC patients similarly to a reference model composed only by the tumor stage and SUVmax. Replication of these preliminary results is necessary.

Radiosynthesis automation, non-human primate biodistribution and dosimetry of K+ channel tracer [11C]3MeO4AP

Background4-Aminopyridine (4AP) is a medication for the symptomatic treatment of multiple sclerosis. Several 4AP-based PET tracers have been developed for imaging demyelination. In preclinical studies, [11C]3MeO4AP has shown promise due to its high brain permeability, high metabolic stability, high plasma availability, and high in vivo binding affinity. To prepare for the translation to human studies, we developed a cGMP-compatible automated radiosynthesis protocol and evaluated the whole-body biodistribution and radiation dosimetry of [11C]3MeO4AP in non-human primates (NHPs).MethodsAutomated radiosynthesis was carried out using a GE TRACERlab FX-C Pro synthesis module. One male and one female adult rhesus macaques were used in the study. A high-resolution CT from cranial vertex to knee was acquired. PET data were collected using a dynamic acquisition protocol with four bed positions and 13 passes over a total scan time of ~ 150 min. Based on the CT and PET images, volumes of interest (VOIs) were manually drawn for selected organs. Non-decay corrected time-activity curves (TACs) were extracted for each VOI. Radiation dosimetry and effective dose were calculated from the integrated TACs using OLINDA software.ResultsFully automated radiosynthesis of [11C]3MeO4AP was achieved with 7.3 ± 1.2% (n = 4) of non-decay corrected radiochemical yield within 38 min of synthesis and purification time. [11C]3MeO4AP distributed quickly throughout the body and into the brain. The organs with highest dose were the kidneys. The average effective dose of [11C]3MeO4AP was 4.0 ± 0.6 μSv/MBq. No significant changes in vital signs were observed during the scan.ConclusionA cGMP-compatible automated radiosynthesis of [11C]3MeO4AP was developed. The whole-body biodistribution and radiation dosimetry of [11C]3MeO4AP was successfully evaluated in NHPs. [11C]3MeO4AP shows lower average effective dose than [18F]3F4AP and similar average effective dose as other carbon-11 tracers.

Assessment of regional and total skeletal metabolism using 18F-NaF PET/CT in patients with chronic kidney disease.

The study aims to assess regional and total bone metabolic activity in patients with chronic kidney disease using Na[18F]F PET and correlation between semi-quantitative indices and blood parameters. Seventy-two subjects (mean age 61.8 ± 13.8years) were included. Of these 24/72 patients had end-stage renal disease (ESRD) (GFR < 15mL/min/1.73 m2), 38/72 had chronic kidney disease (CKD) (GFR between 60 and 15mL/min/1.73 m2), and 10/72 were controls with normal renal function. All subjects underwent Na[18F]F PET-CT with a dose activity of 0.06mCi/Kg. Regional and total skeletal metabolism were assessed with mean SUVs in a skeletal volume of interest (VOI), bone to soft tissue index (B/S), global SUV mean (GSUV mean) of the whole bone, and uptake in the femoral neck. Statistically significant differences were observed in a number of 18F-NaF metrics like femoral neck metabolism in CKD and ERSD groups in comparison to control in right (P = 0.003) and left femur (P = 0.006), bone to soft tissue index in the femur (P = 0.016) and GSUV5 (P = 0.006). There is also a significant difference in SUV mean in lumbar vertebrae (L1-L4) among CKD, ESRD, and controls. There was a moderate correlation between 18F-NaF PET scan uptake and blood parameters such as ALP and PTH. Na[18F]F uptake parameters were significantly different in low versus high bone turnover state. The assessment of total skeleton and regional metabolism and bone turnover in CKD patients is feasible with Na[18F]F PET. Na[18F]F can help to detect early changes in bone metabolism and assess the progression of bone disease in this complex condition. Quantification with Na[18F]F PET might provide better assessment of the bone turnover. The difference in Na[18F]F uptake in CKD compared to controls is likely related to a change in bone turnover which, however, requires further validation.

A comparison study of dynamic [18F]Alfatide II imaging and [11C]MET in orthotopic rat models of glioblastoma

PurposeTo investigate and compare the dynamic positron emission tomography (PET) imaging with [18F]Alfatide II Imaging and [11C]Methionine ([11C]MET) in orthotopic rat models of glioblastoma multiforme (GBM), and to assess the utility of [18F]Alfatide II in detecting and evaluating neoangiogenesis in GBM.Methods[18F]Alfatide II and [11C]MET were injected into the orthotopic GBM rat models (n = 20, C6 glioma cells), followed by dynamic PET/MR scans 21 days after surgery of tumor implantation. On the PET image with both radiotracers, the MRI-based volume-of-interest (VOI) was manually delineated encompassing glioblastoma. Time-activity curves were expressed as tumor-to-normal brain ratio (TNR) parameters and PET pharmacokinetic modeling (PKM) performed using 2-tissue-compartment models (2TCM). Immunofluorescent staining (IFS), western blotting and blocking experiment of tumor tissue were performed for the validation.ResultsCompared to 11C-MET, [18F]Alfatide II presented a persistent accumulation in the tumor, albeit with a slightly lower SUVmean of 0.79 ± 0.25, and a reduced uptake in the contralateral normal brain tissue, respectively. This resulted in a markedly higher tumor-to-normal brain ratio (TNR) of 18.22 ± 1.91. The time–activity curve (TACs) showed a significant increase in radioactive uptake in tumor tissue, followed by a plateau phase up to 60 min for [18F]Alfatide II (time to peak:255 s) and 40 min for [11C]MET (time to peak:135 s) post injection. PKM confirmed significantly higher K1 (0.23/0.07) and K3 (0.26/0.09) in the tumor region compared to the normal brain with [18F]Alfatide II. Compared to [11C]MET imaging, PKM confirmed both significantly higher K1/K2 (1.24 ± 0.79/1.05 ± 0.39) and K3/K4 (11.93 ± 4.28/3.89 ± 1.29) in the tumor region with [18F]Alfatide II. IFS confirmed significant expression of integrin and tumor vascularization in tumor region.Conclusion[18F]Alfatide II demonstrates potential in imaging tumor-associated neovascularization in the context of glioblastoma multiforme (GBM), suggesting its utility as a tool for further exploration in neovascular characterization.

Mapping 18F-FDG Kinetics Together with Patient-Specific Bootstrap Assessment of Uncertainties: An Illustration with Data from a PET/CT Scanner with a Long Axial Field of View.

The purpose of this study was to examine a nonparametric approach to mapping kinetic parameters and their uncertainties with data from the emerging generation of dynamic whole-body PET/CT scanners. Methods: Dynamic PET 18F-FDG data from a set of 24 cancer patients studied on a long-axial-field-of-view PET/CT scanner were considered. Kinetics were mapped using a nonparametric residue mapping (NPRM) technique. Uncertainties were evaluated using an image-based bootstrapping methodology. Kinetics and bootstrap-derived uncertainties are reported for voxels, maximum-intensity projections, and volumes of interest (VOIs) corresponding to several key organs and lesions. Comparisons between NPRM and standard 2-compartment (2C) modeling of VOI kinetics are carefully examined. Results: NPRM-generated kinetic maps were of good quality and well aligned with vascular and metabolic 18F-FDG patterns, reasonable for the range of VOIs considered. On a single 3.2-GHz processor, the specification of the bootstrapping model took 140 min; individual bootstrap replicates required 80 min each. VOI time-course data were much more accurately represented, particularly in the early time course, by NPRM than by 2C modeling constructs, and improvements in fit were statistically highly significant. Although 18F-FDG flux values evaluated by NPRM and 2C modeling were generally similar, significant deviations between vascular blood and distribution volume estimates were found. The bootstrap enables the assessment of quite complex summaries of mapped kinetics. This is illustrated with maximum-intensity maps of kinetics and their uncertainties. Conclusion: NPRM kinetics combined with image-domain bootstrapping is practical with large whole-body dynamic 18F-FDG datasets. The information provided by bootstrapping could support more sophisticated uses of PET biomarkers used in clinical decision-making for the individual patient.

Conversion map from quantitative parameter mapping to myelin water fraction: comparison with R1·R2* and myelin water fraction in white matter.

To clarify the relationship between myelin water fraction (MWF) and R1⋅R2* and to develop a method to calculate MWF directly from parameters derived from QPM, i.e., MWF converted from QPM (MWFQPM). Subjects were 12 healthy volunteers. On a 3T MR scanner, dataset was acquired using spoiled gradient-echo sequence for QPM. MWF and R1⋅R2* maps were derived from the multi-gradient-echo (mGRE) dataset. Volume-of-interest (VOI) analysis using the JHU-white matter (WM) atlas was performed. All the data in the 48 WM regions measured VOI were plotted, and quadratic polynomial approximations of each region were derived from the relationship between R1·R2* and the two-pool model-MWF. The R1·R2* map was converted to MWFQPM map. MWF atlas template was generated using converted to MWF from R1·R2* per WM region. The mean MWF and R1·R2* values for the 48 WM regions were 11.96 ± 6.63%, and 19.94 ± 4.59s-2, respectively. A non-linear relationship in 48 regions of the WM between MWF and R1·R2* values was observed by quadratic polynomial approximation (R2 ≥ 0.963, P < 0.0001). MWFQPM map improved image quality compared to the mGRE-MWF map. Myelin water atlas template derived from MWFQPM may be generated with combined multiple WM regions.

The lesion detection rate of Ga-68 DOTATATE PET/MR in multiple endocrine neoplasia type 1.

The purpose of the study was to determine the usefulness of Ga-68 DOTATATE PET/MR in the identification of tumours in individuals with multiple endocrine neoplasia type 1 (MEN1). In this retrospective investigation, five individuals who had tested positive for a hereditary MEN1 variant underwent Ga-68 DOTATATE PET/MR between May 2020 and January 2023. Several types of tumours associated with MEN1 were studied. MEN1-related tumours included pituitary, parathyroid, gastroenteropancreatic, and adrenal. The rates of lesion identification between MRI, Ga-68 DOTATATE PET, and Ga-68 DOTATATE PET/MRI were examined. The maximum and mean standard uptake values (SUVmax and SUVmean) were evaluated in carefully delineated volumes of interest (VOI) for the relevant tumours. Of the 24 primary lesions, 14 were identified by Ga-68 DOTATATE PET, 18 by MRI, and 20 by Ga-68 DOTATATE PET/MRI. Two pituitary tumours were detected by all three techniques. All parathyroid tumours that were not detected by Ga-68 DOTATATE PET and MRI were found by Tc-99m MIBI SPECT/CT or/and EUS. Ga-68 DOTATATE PET/MR detected more gastroenteropancreatic lesions. All adrenal tumours not identified by Ga-68 DOTATATE PET were found by MRI or CT. The median SUVmax for lesions identified on Ga-68 DOTATATE PET/MRI was 18.4 (range, 3.8-85.2), and the median SUVmean was 12.0 (range, 2.3-49.8). The combination of Ga-68 DOTATATE PET and MRI demonstrated a higher detection rate and may be more useful in the work-up of MEN1 providing a panoramic view of MEN1-related lesions. To increase the identification of MEN1-associated neuroendocrine lesions in the parathyroid gland, approaches other than Ga-68 DOTATATE PET/MRI should be used.

A Cross-Sectional Study to Evaluate Diffusion Tensor Imaging Findings of the Brain in End-Stage Renal Disease (ESRD) Patients Undergoing Maintenance Hemodialysis Using Volume of Interest Method

Background: Cerebral small vessel disease (CSVD) is a chronic disorder affecting small vessels within the brain, increasing the risk of stroke in patients with chronic kidney disease (CKD). Diffusion tensor imaging (DTI) is a newer quantitative method for diagnosing CSVD at an early stage of pathogenesis. Objectives: This study compares various DTI parameters in multiple white matter tracts of the brain in CKD patients undergoing maintenance hemodialysis with normal controls in the Indian population using the volume of interest (VOI) method. Additionally, it correlates these DTI parameters with each other at different locations to gain insights into the pathogenesis of CSVD. Methods: After obtaining institutional ethics approval, a cross-sectional study was conducted at a tertiary care hospital over one year (June 2022 to May 2023). The study comprised seventy-five patients in the hemodialysis group and twenty-five controls. All participants underwent MRI brain examinations on a 3 Tesla MRI scanner, and the four DTI parameters - fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) - were reviewed for nine white matter tracts to evaluate statistical differences and correlations. Results: Fractional anisotropy was significantly decreased at anterior locations – corpus callosum genu (P = 0. 357 × 10-7), right anterior corona radiata (P = 0.001), and left anterior corona radiata (P = 0.45 × 10-5). In these locations, FA negatively correlated with RD (R = -0.7904, P &lt; 0.00001), and RD was also significantly increased. Axial diffusivity was significantly increased at posterior locations in the corpus callosum splenium (P = 0.108 × 10-5) and left posterior corona radiata (P = 0.244 × 10-5). However, none of the four DTI parameters showed significant differences between hemodialysis patients and the control group for the subset of patients with normal routine brain MRI features. The intraclass correlation coefficients (ICCs) were high for all four DTI parameters for both patients (0.78 to 0.85) and controls (0.82 to 0.89). Conclusions: This study on CKD patients undergoing maintenance hemodialysis reveals significant differences in some DTI parameters in widespread white matter tracts of the brain using the VOI method, with acceptable to excellent interobserver agreement.

Motor or non-motor speech interference? A multimodal fMRI and direct cortical stimulation mapping study

We retrospectively analyzed data from 15 patients, with a normal pre-operative cognitive performance, undergoing awake surgery for left fronto-temporal low-grade glioma. We combined a pre-surgical measure (fMRI maps of motor- and language-related centers) with intra-surgical measures (MNI-registered cortical sites data obtained during intra-operative direct electrical stimulation, DES, while they performed the two most common language tasks: number counting and picture naming).Selective DES effects along the precentral gyrus/inferior frontal gyrus (and/or the connected speech articulation network) were obtained. DES of the precentral gyrus evoked the motor speech arrest, i.e., anarthria (with apparent mentalis muscle movements). We calculated the number of shared voxels between the lip-tongue and overt counting related- and silent naming-related fMRI maps and the Volumes of Interest (VOIs) obtained by merging together the MNI sites at which a given speech disturbance was observed, normalized on their mean the values (i.e., Z score). Both tongue- and lips-related movements fMRI maps maximally overlapped (Z = 1.05 and Z = 0.94 for lips and tongue vs. 0.16 and −1.003 for counting and naming) with the motor speech arrest seed. DES of the inferior frontal gyrus, pars opercularis and the rolandic operculum induced speech arrest proper (without apparent mentalis muscle movements). This area maximally overlapped with overt counting-related fMRI map (Z = −0.11 and Z = 0.09 for lips and tongue vs. 0.9 and 0.0006 for counting and naming). Interestingly, our fMRI maps indicated reduced Broca's area activity during silent speech compared to overt speech. Lastly, DES of the inferior frontal gyrus, pars opercularis and triangularis evoked variations of the output, i.e., dysarthria, a motor speech disorder occurring when patients cannot control the muscles used to produce articulated sounds (phonemes). Silent object naming-related fMRI map maximally overlapped (Z = −0.93 and Z = −1.04 for lips and tongue vs. −1.07 and 0.99 for counting and naming) with this seed.Speech disturbances evoked by DES may be thought of as selective interferences with specific recruitment of left inferior frontal gyrus and precentral cortex which are differentiable in terms of the specific interference induced.

The use of individual-based FDG-PET volume of interest in predicting conversion from mild cognitive impairment to dementia

BackgroundBased on a longitudinal cohort design, the aim of this study was to investigate whether individual-based 18F fluorodeoxyglucose positron emission tomography (18F-FDG-PET) regional signals can predict dementia conversion in patients with mild cognitive impairment (MCI).MethodsWe included 44 MCI converters (MCI-C), 38 non-converters (MCI-NC), 42 patients with Alzheimer’s disease with dementia, and 40 cognitively normal controls. Data from annual cognitive measurements, 3D T1 magnetic resonance imaging (MRI) scans, and 18F-FDG-PET scans were used for outcome analysis. An individual-based FDG-PET approach was applied using seven volumes of interest (VOIs), Z transformed using a normal FDG-PET template. Hypometabolism was defined as a Z score < -2 of regional standard uptake value ratio. For the longitudinal cognitive test scores, generalized estimating equations were used. A linear mixed-effects model was used to compare the temporal impact of cortical hypometabolism and cortical thickness degeneration.ResultsThe clinical follow-up period was 6.6 ± 3.8 years (range 3.1 to 16.0 years). The trend of cognitive decline could differentiate MCI-C from MCI-NC after 3 years of follow-up. In the baseline 18F-FDG-PET scan of the patients with MCI, medial temporal lobe (MTL; 94.7% sensitivity, 80.5% specificity) and posterior cingulate cortex (PCC; 89.5% sensitivity, 73.1% specificity) hypometabolism predicted conversion with high accuracy. 18F-FDG-PET hypometabolism preceded dementia conversion at an interval of 3.70 ± 1.68 years and was earlier than volumetric changes, with the exception of the MTL.ConclusionsOur finding supports the use of individual-based 18F-FDG-PET analysis to predict MCI conversion to dementia. Reduced FDG-PET metabolism in the MTL and PCC were strongly associated with future cognitive decline in the MCI-C group. Changes in 18F-FDG-PET occurred 1 to 8 years prior to conversion to dementia. Progressive hypometabolism in the PCC, precuneus and lateral temporal lobe, but not MTL, preceded MRI findings at the MCI stage.

Intra and peritumoral PET radiomics analysis to predict the pathological response in breast cancer patients receiving neoadjuvant chemotherapy

ObjectiveThe aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). MethodsFemale patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-T) was manually segmented, then a voxel-thick VOI was added around VOI-T to define the peritumoral area (VOI-PT). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A “radiomic model” was created with only radiomic features, and a “patho-radiomic model” was created using radiomic features and immunohistochemical data. ResultsOf the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-T (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-PT; no correlation was found between Morphological_Compacity-PT and NGTDM_contrast-PT. The obtained radiomic model’s sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903). ConclusionsEvaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.

Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer.

It remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC). To evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC. Retrospective. 219 patients aged from 15 to 79 years were enrolled. 3.0 or 1.5T, axial fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (CE-T1WI). MRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics-clinical model (RCM). Mann-Whitney U test, chi-squared test, DeLong test, log-rank test. P < 0.05 indicated a significant difference. All the RSs constructed on WPTV exhibited higher AUCs (0.720-0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261). The radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut-off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency. 4 TECHNICAL EFFICACY: Stage 2.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study

Rationale and ObjectivesMedulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set. Materials and methodsAxial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers- extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1+T2WI (T1+T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center. Results31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1+T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models. ConclusionThe combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between Medulloblastoma and Ependymoma in childhood, surpassing human expert diagnosis in training and testing sets.

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics

ObjectivesTo evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM).Materials and methodsIn this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison.ResultsThe validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar.ConclusionThe NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM.Clinical relevance statementThe NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity.Key Points• The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis.• Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors.• The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study.

To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. 32 Patients (23M/9F; age 61.8 ± 10.6years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73cm) undergoing resection were prospectively recruited. All patients underwent 1.5T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM. Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization.

Radiomics features based on dual-area CT predict the expression levels of fatty acid binding protein 4 and outcome in hepatocellular carcinoma.

To evaluate the predictive value of tumor and peritumor radiomics in the fatty acid binding protein 4 (FABP4) expression levels and overall survival in patients with hepatocellular carcinoma. The genomic data of HCC patients were obtained from The Cancer Genome Atlas. The Dual-area CT images of corresponding patients were downloaded from The Cancer Imaging Archive, for radiomics feature extraction, model construction and prognosis analysis. Simultaneously, using patients from Sichuan Provincial People's Hospital, the prognostic value of the radiomics model in HCC patients was validated. In the TCIA database, the area under the curve (AUC) values of the volumes of interest (VOI)whole model in the training set and internal validation set were 0.812 and 0.754, respectively, and the AUC value of VOIwhole+periphery in the training set and internal validation set were 0.866 and 0.779, respectively. In the VOIwhole and the VOIwhole+periphery model of the independent cohort, there were significant differences in OS between the high and low rad-score groups (P = 0.009, P = 0.021, respectively). Significant positive correlations can be observed between FABP4 expression and correlations with rad-score of VOIwhole model (r = 0.691) and VOIwhole+periphery model (r = 0.732) in the independent cohort. Radiomics models of tumor and peritumor Dual-area CT images could predict stably the expression levels of FABP4 and may be helping in personalized treatment strategies.