Volume Of Interest Research Articles

Early tau detection in flortaucipir images: validation in autopsy-confirmed data and implications for disease progression

BackgroundThere is an increasing interest in utilizing tau PET to identify patients early in Alzheimer’s disease (AD). In this work, a temporal lobe composite (Eτ) volume of interest (VOI) was evaluated in a longitudinal flortaucipir cohort and compared to a previously described global neocortical VOI. In a separate autopsy-confirmed study, the sensitivity of the Eτ VOI for identifying intermediate (B2) neurofibrillary tangle (NFT) pathology was evaluated.MethodsA total of 427 subjects received flortaucipir, florbetapir, MRI, and cognitive evaluation at baseline and 18 months. In a separate autopsy study, 67 subjects received ante-mortem flortaucipir scans, and neuropathological findings were recorded according to NIA-AA recommendations by two experts. Two VOIs: Eτ comprising FreeSurfer volumes (bilateral entorhinal cortex, fusiform, parahippocampal, and inferior temporal gyri) transformed to MNI space and a previously published global AD signature-weighted neocortical VOI (ADsignature) (Devous et al., J Nucl Med 59:937–43, 2018), were used to calculate SUVr relative to a white matter reference region (PERSI) (Southekal et al., J Nucl Med Off Publ Soc Nucl Med 59:944–51, 2018). SUVr cutoffs for positivity were determined based on a cohort of young, cognitively normal subjects. Subjects were grouped based on positivity on both VOIs (Eτ+/ADsignature+; Eτ+/ADsignature–; Eτ−/ADsignature−). Groupwise comparisons were performed for baseline SUVr, 18-month changes in SUVr, neurodegeneration, and cognition. For the autopsy study, the sensitivity of Eτ in identifying intermediate Braak pathology (B2) subjects was compared to that of AD signature-weighted neocortical VOI. The average surface maps of subjects in the Eτ+/ADsignature− group and B2 NFT scores were created for visual evaluation of uptake.ResultsSixty-four out of 390 analyzable subjects were identified as Eτ+/ADsignature–: 84% were Aβ+, 100% were diagnosed as MCI or AD, and 59% were APOE ε4 carriers. Consistent with the hypothesis that Eτ+/ADsignature– status reflects an early stage of AD, Eτ+/ADsignature– subjects deteriorated significantly faster than Eτ–/ADsignature– subjects, but significantly slower than Eτ+/ADsignature+ subjects, on most measures (i.e., change in ADsignature SUVr, Eτ ROI cortical thickness, and MMSE). The ADsignature VOI was selective for subjects who came to autopsy with a B3 NFT score. In the autopsy study, 12/15 B2 subjects (including 10/11 Braak IV) were Eτ+/ADsignature–. Surface maps showed that flortaucipir uptake was largely captured by the Eτ VOI regions in B2 subjects.ConclusionThe Eτ VOI identified subjects with elevated temporal but not global tau (Eτ+/ADsignature–) that were primarily Aβ+, APOE ε4 carriers, and diagnosed as MCI or AD. Eτ+/ADsignature– subjects had greater accumulation of tau, greater atrophy, and higher decline on MMSE in 18 months compared to Eτ−/ADsignature− subjects. Finally, the Eτ VOI identified the majority of the intermediate NFT score subjects in an autopsy-confirmed study. As far as we know, this is the first study that presents a visualization of ante-mortem FTP retention patterns that at a group level agree with the neurofibrillary tangle staging scheme proposed by Braak. These findings suggest that the Eτ VOI may be sensitive for detecting impaired subjects early in the course of Alzheimer’s disease.

In vivo Comparison of Volume of Interest Determination on Maxillary Bone Regeneration in Orthodontic Treatment: Cone-Beam Computed Tomography Versus Micro-Computed Tomography

Orthodontic treatment can result in relapse, side effects associated with Orthodontic Tooth Movement (OTM) are root resorption, bone loss, and gingival inflammation which impairs long-term stability during orthodontic treatment. OTM creates a gap causing relapse after orthodontic treatment. Regenerate bone to prevent relapse after orthodontic treatment. Volume of interest (VOI) measurement by (3 Dimension) 3D imaging Cone-beam computed tomography (CBCT) or Micro-computed tomography (Micro-CT) in vivo. In vivo tests were carried out on 6 Wistar rats aged 5 months and performed 10gf maxillary Molar 1 (M1) withdrawal with 0.010 inch Nickel-Titanium (NiTi) and 8 mm close coil spring, maxillary separation was carried out by CBCT and Micro-CT tests. The tensile force of the orthodontic appliance in the compressed and tense area is a characteristic of OTM causing changes in remodeling. Alveolar bone displacement can occur when tooth roots move through the alveolar cortical bone occurring during OTM. Measurements on samples from CBCT and Micro-CT to investigate the accuracy and reproducibility of landmarks. CBCT resolution 0.0131 µm / pixel and Micro-CT 5.7 µm/pixel, Micro-CT can determine VOI boundaries. Comparison of VOI from CBCT and Micro-CT showed VOI CBCT 9.99% and VOI Micro-CT 22.22%. 3D Micro-CT visualization is more detailed than CBCT, so VOI can be determined easily and more accurately. Detailed, clear, and accurate 3D Micro-CT visualization in the determination of VOI versus CBCT.

Microstructural adaptations of the subchondral bone are related to the mechanical axis deviation in end stage varus oa knees.

Recent studies highlighted the crucial contribution of subchondral bone to OA development. Yet, only limited data have been reported on the relation between alteration to cartilage morphology, structural properties of the subchondral bone plate (SBP) and underlying subchondral trabecular bone (STB). Furthermore, the relationship between the morphometry of the cartilage and bone in the tibial plateau and the OA-induced changes in the joint's mechanical axis remains unexplored. Therefore, a visualisation and quantification of cartilage and subchondral bone microstructure in the medial tibial plateau was performed. End stage knee-OA patients with varus alignment and scheduled for total knee arthroplasty (TKA) underwent preoperative fulllength radiography to measure the hip-knee-ankle angle (HKA) and the mechanical-axis deviation (MAD). 18 tibial plateaux were μ-CT scanned (20.1 μm/voxel). Cartilage thickness, SBP, and STB microarchitecture were quantified in 10 volumes of interest (VOIs) in each medial tibial plateau. Significant differences (p < 0.001) were found for cartilage thickness, SBP, and STB microarchitecture parameters among the VOIs. Closer to the mechanical axis, cartilage thickness was consistently smaller, while SBP thickness and STB bone volume fraction (BV/TV) were higher. Moreover, trabeculae were also more superior-inferiorly oriented, i.e. perpendicular to the transverse plane of the tibial plateau. As cartilage and subchondral bone changes reflect responses to local mechanical loading patterns in the joint, the results suggested that region-specific subchondral bone adaptations were related to the degree of varus deformity. More specifically, subchondral sclerosis appeared to be most pronounced closer to the mechanical axis of the knee.

Predictive value of 18F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation.

To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods. One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test. A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test. It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.

Performance of a deep learning enhancement method applied to PET images acquired with a reduced acquisition time.

This study aims to evaluate the performance of a deep learning enhancement method in PET images reconstructed with a shorter acquisition time, and different reconstruction algorithms. The impact of the enhancement on clinical decisions was also assessed. Thirty-seven subjects underwent clinical whole-body [18F]FDG PET/CT exams with an acquisition time of 1.5 min per bed position. PET images were reconstructed with the OSEM algorithm using 66% counts (imitating 1 min/bed acquisition time) and 100% counts (1.5 min/bed). Images reconstructed from 66% counts were subsequently enhanced using the SubtlePET™ (SP) deep-learning-based software, (Subtle Medical, USA) - with two different software versions (SP1 and SP2). Additionally, images obtained with 66% counts were reconstructed with QClear™ (GE, USA) algorithm and enhanced with SP2. Volumes of interest (VOI) of the lesions and reference VOIs in the liver, brain, bladder, and mediastinum were drawn on OSEM images and copied on SP images. Quantitative SUVmax values per VOI of OSEM or QClear™ and AI-enhanced 'shortened' acquisitions were compared. Two hundred and fifty-two VOIs were identified (37 for each reference region, and 104 for the lesions) for OSEM, SP1, SP2, and QClear™ images AI-enhanced with SP2. SUVmax values on SP1 images were lower than standard OSEM, but on SP2 differences were smaller (average difference for SP1 11.6%, for SP2 -4.5%). For images reconstructed with QClear™, SUVmax values were higher (average +8.9%, median 6.1%, SD 18.9%). For small lesions with SUVmax values range 2.0 to 4.0 decrease of measured SUVmax was much less significant with SP2 (for liver average -6.5%, median -5.6% for lesions average -5.6%, median - 6.0, SD 5.2%) and showed the best correlation with original OSEM. While no artifacts and good general diagnostic confidence were found in AI-enhanced images, SP1, the images were not equal to the original OSEM - some lesions were hard to spot. SP2 produced images with almost the same quality as the original 1.5 min/bed OSEM reconstruction. The studied deep learning enhancement method can be used to accelerate PET acquisitions without compromising quantitative SUVmax values. AI-based algorithms can enhance the image quality of accelerated PET acquisitions, enabling the dose reduction to the patients and improving the cost-effectiveness of PET/CT imaging.

Combined computational analysis and cytology show limited depth osteogenic effect on bone defects in negative pressure wound therapy.

Background: The treatment of bone defects remains a clinical challenge. The effect of negative pressure wound therapy (NPWT) on osteogenesis in bone defects has been recognized; however, bone marrow fluid dynamics under negative pressure (NP) remain unknown. In this study, we aimed to examine the marrow fluid mechanics within trabeculae by computational fluid dynamics (CFD), and to verify osteogenic gene expression, osteogenic differentiation to investigate the osteogenic depth under NP. Methods: The human femoral head is scanned using micro-CT to segment the volume of interest (VOI) trabeculae. The VOI trabeculae CFD model simulating the bone marrow cavity is developed by combining the Hypermesh and ANSYS software. The effect of trabecular anisotropy is investigated, and bone regeneration effects are simulated under NP scales of -80, -120, -160, and -200mmHg. The working distance (WD) is proposed to describe the suction depth of the NP. Finally, gene sequence analysis, cytological experiments including bone mesenchymal stem cells (BMSCs) proliferation and osteogenic differentiation are conducted after the BMSCs are cultured under the same NP scale. Results: The pressure, shear stress on trabeculae, and marrow fluid velocity decrease exponentially with an increase in WD. The hydromechanics of fluid at any WD inside the marrow cavity can be theoretically quantified. The NP scale significantly affects the fluid properties, especially those fluid close to the NP source; however, the effect of the NP scale become marginal as WD deepens. Anisotropy of trabecular structure coupled with the anisotropic hydrodynamic behavior of bone marrow; An NP of -120mmHg demonstrates the majority of bone formation-related genes, as well as the most effective proliferation and osteogenic differentiation of BMSCs compared to the other NP scales. Conclusion: An NP of -120mmHg may have the optimal activated ability to promote osteogenesis, but the effective WD may be limited to a certain depth. These findings help improve the understanding of fluid mechanisms behind NPWT in treating bone defects.

Characterization of Microvascular Invasion in Hepatocellular Carcinoma Using Computational Modeling of Interstitial Fluid Pressure and Velocity.

Most solid tumors show increased interstitial fluid pressure (IFP), and this increased IFP is an obstacle to treatment. A noninvasive model for measuring IFP in hepatocellular carcinoma (HCC) is an unresolved issue. To develop a noninvasive model to measure IFP and interstitial fluid velocity (IFV) in HCC and to characterize the microvascular invasion (MVI) status by using this model. Retrospective. A total of 97 HCC patients (mean age 57.6± 10.9 years, 77.3% males), 53 of them with MVI and 44 of them without MVI. A 3-T, three-dimensional spoiled gradient-recalled echo. MVI was defined as microscopic vascular invasion of small vessels within the peritumoral liver tissue. The volumes of interest (VOIs) were manually delineated and enclosed the tumor lesion and healthy liver parenchyma, respectively. The extended Tofts model (ETM) was used to estimate permeability parameters from all the VOIs. Subsequently, the continuity partial differential equation (PDE) was implemented and IFP and IFV were acquired. Wilcoxon signed-ranks tests, histogram analysis, Mann-Whitney U test, Fisher's exact test, least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC), Youden index, DeLong test, and Benjamini-Hochberg correction. A P value <0.05 was considered statistically significant. The HCC lesions exhibited elevated IFP and reduced IFV. There were no significant differences in any measured demographic and clinical features between the MVI-positive and MVI-negative groups, except for tumor size. Nine IFP histogram analysis-derived parameters and seven IFV histogram analysis-derived parameters could be used to characterize the MVI status. LASSO regression selected five features: IFP maximum, IFP 10th percentile, IFP 90th percentile, IFV SD, and IFV 10th percentile. The combination of these features showed the highest AUC (0.781) and specificity (77.3%). A noninvasive IFP and IFV measurement model for HCC was developed. Specific IFP- and IFV-derived parameters exhibited significant association with the MVI status. 3. Stage 2.

Dosimetric impact of 3D motion-compensated SPECT reconstruction for SIRT planning

BackgroundIn selective internal radiation therapy, 99mTc SPECT images are used to optimize patient treatment planning, but they are affected by respiratory motion. In this study, we evaluated on patient data the dosimetric impact of motion-compensated SPECT reconstruction on several volumes of interest (VOI), on the tumor-to-normal liver (TN) ratio and on the activity to be injected.MethodsTwenty-nine patients with liver cancer or hepatic metastases treated by radioembolization were included in this study. The biodistribution of 90Y is assumed to be the same as that of 99mTc when predictive dosimetry is implemented. A total of 31 99mTc SPECT images were acquired and reconstructed with two methods: conventional OSEM (3D) and motion-compensated OSEM (3Dcomp). Seven VOI (liver, lungs, tumors, perfused liver, hepatic reserve, healthy perfused liver and healthy liver) were delineated on the CT or obtained by thresholding SPECT images followed by Boolean operations. Absorbed doses were calculated for each reconstruction using Monte Carlo simulations. Percentages of dose difference (PDD) between 3Dcomp and 3D reconstructions were estimated as well as the relative differences for TN ratio and activities to be injected. The amplitude of movement was determined with local rigid registration of the liver between the 3Dcomp reconstructions of the extreme phases of breathing.ResultsThe mean amplitude of the liver was 9.5 ± 2.7 mm. Medians of PDD were closed to zero for all VOI except for lungs (6.4%) which means that the motion compensation overestimates the absorbed dose to the lungs compared to the 3D reconstruction. The smallest lesions had higher PDD than the largest ones. Between 3D and 3Dcomp reconstructions, means of differences in lung dose and TN ratio were not statistically significant, but in some cases these differences exceed 1 Gy (4/31) and 8% (2/31). The absolute differences in activity were on average 3.1% ± 5.1% and can reach 22.8%.ConclusionThe correction of respiratory motion mainly impacts the lung and tumor doses but only for some patients. The largest dose differences are observed for the smallest lesions.

Prussian blue nanoparticles as isotope-labeled and fluorescent contrast material

The study presents the synthesis and development of a Prussian Blue based biocompatible and chemically stable T1 magnetic resonance imaging (MRI) contrast agent with near infrared (NIR) optical contrast for preclinical application. The physical properties of the Prussian blue nanoparticles (PBNPs) (iron (II); iron (III); octadecacyanide) were characterized with dynamic light scattering (DLS), zeta potential measurement, atomic force microscopy (AFM), and transmission electron microscopy (TEM). In vitro contrast enhancement properties of PBNPs were determined by MRI. In vivo T1-weighted contrast of the prepared PBNPs was investigated by MRI and optical imaging modality after intravenous administration to mice. PBNP-s doped with 201Tl isotope have also been synthetised and biodistribution in mice was determined by using SPECT and MRI. Activity concentrations (MBq/cm3) were calculated from the SPECT scans for each dedicated volume of interest (VOI) of liver, kidneys, salivary glands, heart, lungs, and brain. Isotope-labeled citrate-coated PBNP accumulation peaked at 2 hours after injection in the kidneys and the liver followed by a gradual decrease in activity in later time points. We synthesized, characterized, and radiolabeled a Prussian blue-based nanoparticle platform for contrast material applications. In vivo radiochemical stability and biodistribution open up the way for further diagnostic applications. The presence and detectability of PBNPs during initial biodistribution in the cardiovascular system indicates vascular contrast material applications, too.

Thyroid segmentation using perithyroidal halo layer on 99mTc-pertechnetate thyroid SPECT/CT: An easy and reliable method for accurate quantification of thyroid activity.

In previous fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) studies, tumor segmentation using peritumoral halo layer (PHL; SegPHL) was shown to be reliable and accurate segmentation method in various malignant tumors. We found that the halo layer also was observed on the 99mTc-pertechnetate (99mTcO4) thyroid single photon emission computed tomography (SPECT)/CT. In the present study, we attempted to apply thyroid segmentation using the perithyroidal halo layer (PTHL; SegPTHL) on 99mTcO4 thyroid SPECT/CT and compared SegPTHL with CT-based thyroid segmentation (SegCT). A total of 33 patients (19 females, 14 males; mean age, 46.91±15.7 years old) were enrolled in this study. For SegCT, three-dimensional volume of interest (VOI) of the thyroid was generated via multiple 2-dimensional regions of interest (ROI) along the thyroid margin on transaxial CT images that were manually drawn slice by slice. The PTHL was easily identified by an abrupt increase in layer thickness with minimal or mild distortion of the main thyroid contour, and the thyroid margin for SegPTHL was determined at the innermost portion of PTHL. An automated VOI generation for SegPTHL was performed using the Q. Volumetrix software. The correlation and reliability tests were performed between the quantification parameters of SegPTHL and SegCT. The PTHL threshold adjusted according to maximal SUV of thyroid were similar to the results of previous SegPHLstudies of 18F-FDG PET/CT. A good correlation was observed between the thyroid volumes of SegCT and SegPTHL (r=0.725; P<0.0001), although the thyroid volume of SegPTHL was slightly larger than that of SegCT (P=0.0017). The % thyroid uptake (TcTU), total lesion activity (TLA), and mean standardized uptake value (SUVmean) of SegPTHL correlated well with those of SegCT (r=0.9877, 0.9883, 0.9875, respectively; P<0.0001). No significant error was observed between the parameters (i.e., TcTU, TLA, and SUVmean) of SegPTHL and SegCT. Thyroid segmentation PTHL may be a useful method for reliable quantification of thyroid uptake, because the SPECT/CT parameters of SegPTHL were strongly correlated with those of SegCT, as well as the process of SegPTHL is easier and faster than that of SegCT.

Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer on CT images: A multi-dataset study.

Classifying the subtypes of non-small cell lung cancer (NSCLC) is essential for clinically adopting optimal treatment strategies and improving clinical outcomes, but the histological subtypes are confirmed by invasive biopsy or post-operative examination at present. Based on multi-center data, this study aimed to analyze the importance of extracted CT radiomics features and develop the model with good generalization performance for precisely distinguishing major NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). We collected a multi-center CT dataset with 868 patients from eight international databases on the cancer imaging archive (TCIA). Among them, patients from five databases were mixed and split to training and test sets (560:140). The remaining three databases were used as independent test sets: TCGA set (n=97) and lung3 set (n=71). A total of 1409 features containing shape, intensity, and texture information were extracted from tumor volume of interest (VOI), then the ℓ2,1 -norm minimization was used for feature selection and the importance of selected features was analyzed. Next, the prediction and generalization performance of 130 radiomics models (10 common algorithms and 120 heterogeneous ensemble combinations) were compared by the average AUC value on three test sets. Finally, predictive results of the optimal model were shown. After feature selection, 401 features were obtained. Features of intensity, texture GLCM, GLRLM, and GLSZM had higher classification weight coefficients than other features (shape, texture GLDM, and NGTDM), and the filtered image features exhibited significant importance than original image features (p-value=0.0210). Moreover, five ensemble learning algorithms (Bagging, AdaBoost, RF, XGBoost, GBDT) had better generalization performance (p-value=0.00418) than other non-ensemble algorithms (MLP, LR, GNB, SVM, KNN). The Bagging-AdaBoost-SVM model had the highest AUC value (0.815±0.010) on three test sets. It obtained AUC values of 0.819, 0.823, and 0.804 on test set, TCGA set and lung3 set, respectively. Our multi-dataset study showed that intensity features, texture features (GLCM, GLRLM, and GLSZM) and filtered image features were more important for distinguishing ADCs from SCCs. The method of ensemble learning can improve the prediction and generalization performance on the complicated multi-center data. The Bagging-AdaBoost-SVM model had the strongest generalization performance, and it showed promising clinical value for non-invasively predicting the histopathological subtypes of NSCLC.

Particle generation through energy discretization and restrictive planes in GEANT4 simulations for potential applications of cosmic ray muon tomography

In this study, by attempting to resolve the difficulties related to the angular distribution during the particle generation for the muon tomography applications in the GEANT4 simulations, we exhibit an unconventional methodology that is hinged on the direction limitation via the vectorial construction from the generation location to the restriction area rather than using a certain angular distribution or interval. In other words, we favor a momentum direction that is determined by a vector constructed between an initial point randomly chosen on a generative point/plane and a second point arbitrarily selected on a restrictive plane of the same dimensions with the basal cross section of the volume-of-interest (VOI). On account of setting out such a generation scheme, we optimize the particle loss by keeping an angular acceptance that is directly dependent on the VOI geometry as well as the vertical position of the restrictive plane for a tomographic system of a finite size. We demonstrate our strategy for a set of target materials including aluminum, iron, copper, lead, and uranium with a dimension of 40×10×40 cm3 over three restrictive planes of different positions by using a discrete energy spectrum between 0.1 and 8 GeV and we compute the scattering angle, the number of absorption, and the particle loss. Upon our simulation outcomes, we show that the particle generation by means of restrictive planes is an effective strategy that is flexible towards a variety of computational objectives in the GEANT4 simulations.

Transarterial chemoembolization (TACE) for neuroendocrine liver metastasis (NELM): Predictive value of volumetric arterial enhancement (VAE) on baseline MRI

Neuroendocrine tumors (NETs) belong to a rare family of tumors whose incidence has increased significantly over the past 50 years. To evaluate the prognostic value of volumetric arterial enhancement (VAE) on baseline magnetic resonance imaging (MRI) for patients with neuroendocrine liver metastasis (NELM) treated using transarterial chemoembolization (TACE). Between October 2012 and December 2018, VAE in 37 patients was measured with a semi-automatic volume of Interest (VOI) on subtracted T1 sequence in the arterial phase. Patients underwent 1-3 sectoral lipiodol TACE. Radiologic response using modified Response Evaluation Criteria in Solid Tumors (mRECIST) at the treatment cycle end and progression free survival were determined. Median age was 68.0 (60.0; 73.0). Twenty-three patients (62%) had a partial response, 10 (27%) had stable disease, four (11%) had progressive disease. VAE was a significant (P<0.05) predictor of radiologic response. Median progression free survival was 13 months (IC 95: 8; 16). In univariate analysis, significant predictors of local progression were alkaline phosphatase (AP) (P=0.035), Ki-67 index (P=0.014), and VAE (P<0.01). VAE over 500ms and Ki-67 index over 3%were risk factors of progression (P=<0.01) in multivariate analysis. VAE before TACE could be predictive of radiologic response and could be related to oncologic outcomes in patients with NELM.

Correction to ‘ORYX‐MRSI: A fully‐automated open‐source software for proton magnetic resonance spectroscopic imaging data analysis’

International Journal of Imaging Systems and TechnologyVolume 33, Issue 2 p. 770-770 CORRIGENDUMFree Access Correction to ‘ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis’ This article corrects the following: ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis Sevim Cengiz, Muhammed Yildirim, Abdullah Bas, Esin Ozturk-Isik, Volume 32Issue 4International Journal of Imaging Systems and Technology pages: 1068-1083 First Published online: May 17, 2022 First published: 27 January 2023 https://doi.org/10.1002/ima.22857AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat We regret to report a technical error in our manuscript entitled ‘ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis’. The volume of interest (VOI) for all the MR spectroscopic metabolites, other than the reference metabolite, are shifted in space by their respective chemical shift amounts as calculated by Equation 2 due to the chemical shift error. Even though there is a shift in the VOIs of different metabolites, it was incorrect to shift the metabolite maps along with their corresponding VOIs. This technical error has been corrected in the Oryx-MRSI source code, which has been updated on our laboratory's GitHub page. Currently, if the chemical shift correction is set to ‘on’, the corresponding VOIs for the metabolites are shifted in space by their respective chemical shift amounts, and the metabolite intensities are evaluated at their respective locations. Additionally, the metabolite ratios are evaluated only in the region that is located at the intersection of the corresponding metabolite VOIs. Moreover, the tissue fractions are calculated only once at the reference VOI. As a result, Figure 5 is not accurate after correction and hence should not be considered for further discussion. The authors thank Wolfgang Bogner, Ph.D. and Gilbert Hangel, Ph.D. for their careful reviews of the published manuscript and apologize for this error. REFERENCE 1Cengiz S, Yildirim M, Bas A, Ozturk-Isik E. ORYX-MRSI: a fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis. Int J Imaging Syst Technol. 2022; 32: 1068- 1083. doi:10.1002/ima.22748 Volume33, Issue2March 2023Pages 770-770 ReferencesRelatedInformation

Towards accurate 177Lu SPECT activity quantification and standardization using lesion-to-background voxel ratio

BackgroundConventional calibration of the gamma camera consists of the calculation of calibration factors (CFs) (ratio of counts/cc and true concentration activity) as the function of the volume of interest (VOI). However, such method shows inconsistent results when the background activity varies. The aim of the present study was to propose a new calibration method by considering the sphere-to-background counts/voxel ratio (SBVR) in addition to the VOI for CFs calculation. A PET cylindrical flood phantom, a NEMA IQ body phantom, a Data spectrum Torso Phantom (ECT/TOR/P) and a LK-S Kyoto Liver/Kidney phantom were used. The NEMA IQ phantom was used to calibrate the camera and to produce CFs for the different spheres volumes and for varying sphere-to-background activity ratios. The spheres were filled with a uniform activity concentration of 177Lu, while the background was first filled with cold water and activity was added between each SPECT scan. SPECT imaging was performed for 30-s, 20-s, and 10-s exposure per view. The calculated CFs were expressed as function of the sphere volume and SBVR. The obtained CFs were validated for an additional NEMA IQ acquisition with different activities in spheres and background and for the Torso and Liver/Kidney phantoms with inserted NEMA IQ spheres. The quantification accuracy was compared with the conventional method not taking SBVR into consideration.ResultsThe relative errors in quantification using the NEMA IQ phantom with the new calibration method were 0.16%, 5.77%, 9.34% for the large, medium and small sphere, respectively, for a time per view of 30-s. The conventional calibration method gave errors of 3.65%, 6.65%, 30.28% for 30-s. The LK-S Kyoto Liver/Kidney Phantom resulted in quantification errors of 3.40%, 2.14%, 11.18% for the large, medium and small spheres, respectively, for 30-s; compared to 11.31%, 17.54%, 14.43% for 30-s, respectively, for the conventional method. Similar results were obtained for shorter acquisitions times with 20-s and 10-s time per view.ConclusionThese results suggest that SBVR allows to improve quantification accuracy. The shorter time-per-view acquisitions had similar relative differences compared to the full-time acquisition which allows shorter imaging times with 177Lu and improved patient comfort. The SBVR method is simple to set up and can be proposed for standardization.

Differential Cortical Volumetric Bone Mineral Density within the Human Rib

Introduction: The human rib provides a vital role in the protection of thoracic contents. Rib fractures are linked to injuries and health complications that can be fatal. Current clinical methods to assess fracture risk and bone quality are insufficient to quantify intra-element differences in bone mineral density (BMD) or to identify at-risk populations. Utilizing quantitative computed tomography (QCT) provides accurate measures of volumetric BMD (vBMD) along the length of the rib which can help delineate factors influencing differential fracture risk. Methodology: One mid-level rib was obtained from 54 post-mortem human subjects (PMHS) and scanned using QCT. Volumes of interest (VOIs) were created for sites at 30%, 50%, and 75% of rib total curve length. Mean Hounsfield units (HU) from each VOI were converted to vBMD using a scan-specific cortical phantom calibration curve. Additionally, rib and lumbar areal BMD (aBMD) were obtained from a sub-sample of 33 PMHS. Results: Significant differences in vBMD were found between all sites within the rib (p<0.01). When analyzed by sex, vBMD between the 30% and 50% site were no longer different in either males or females (p>0.05). Separating the sample into discrete age groups demonstrated the relative differences in vBMD between sites diminished with age. Further, age as a continuous variable significantly predicted rib vBMD at all sites (p<0.05), but with little practical or clinical utility (R2, 14.7- 22.8%). Similarly, only small amounts of variation in rib vBMD were explained from DXA lumbar and rib aBMD (R2 , 1.1-21.8%). Conclusions: vBMD significantly decreased from the posterior (30%) site to the anterior (75%) site within the rib which may represent adaptation to localized mechanical loading. These differences could result in differential fracture risk across the rib. As thoracic injury can be fatal, using comprehensive assessments of bone quality that accounts for variation within the rib may provide more accurate identification of at-risk populations.

Hemodynamic MRI parameters to predict asymptomatic unilateral carotid artery stenosis with random forest machine learning.

Internal carotid artery stenosis (ICAS) can cause stroke and cognitive decline. Associated hemodynamic impairments, which are most pronounced within individual watershed areas (iWSA) between vascular territories, can be assessed with hemodynamic-oxygenation-sensitive MRI and may help to detect severely affected patients. We aimed to identify the most sensitive parameters and volumes of interest (VOI) to predict high-grade ICAS with random forest machine learning. We hypothesized an increased predictive ability considering iWSAs and a decreased cognitive performance in correctly classified patients. Twenty-four patients with asymptomatic, unilateral, high-grade carotid artery stenosis and 24 age-matched healthy controls underwent MRI comprising pseudo-continuous arterial spin labeling (pCASL), breath-holding functional MRI (BH-fMRI), dynamic susceptibility contrast (DSC), T2 and T2* mapping, MPRAGE and FLAIR. Quantitative maps of eight perfusion, oxygenation and microvascular parameters were obtained. Mean values of respective parameters within and outside of iWSAs split into gray (GM) and white matter (WM) were calculated for both hemispheres and for interhemispheric differences resulting in 96 features. Random forest classifiers were trained on whole GM/WM VOIs, VOIs considering iWSAs and with additional feature selection, respectively. The most sensitive features in decreasing order were time-to-peak (TTP), cerebral blood flow (CBF) and cerebral vascular reactivity (CVR), all of these inside of iWSAs. Applying iWSAs combined with feature selection yielded significantly higher receiver operating characteristics areas under the curve (AUC) than whole GM/WM VOIs (AUC: 0.84 vs. 0.90, p = 0.039). Correctly predicted patients presented with worse cognitive performances than frequently misclassified patients (Trail-making-test B: 152.5s vs. 94.4s, p = 0.034). Random forest classifiers trained on multiparametric MRI data allow identification of the most relevant parameters and VOIs to predict ICAS, which may improve personalized treatments.

Lymph node detection in CT scans using modified U-Net with residual learning and 3D deep network.

Lymph node (LN) detection is a crucial step that complements the diagnosis and treatments involved during cancer investigations. However, the low-contrast structures in the CT scan images and the nodes' varied shapes, sizes, and poses, along with their sparsely distributed locations, make the detection step challenging and lead to many false positives. The manual examination of the CT scan slices could be time-consuming, and false positives could divert the clinician's focus. To overcome these issues, our work aims at providing an automated framework for LNs detection in order to obtain more accurate detection results with low false positives. The proposed work consists of two stages: candidate generation and false positive reduction. The first stage generates volumes of interest (VOI) of probable LN candidates using a modified U-Net with ResNet architecture to obtain high sensitivity but with the cost of increased false positives. The second-stage processes the obtained candidate LNs for false positive reduction using 3D convolutional neural network (CNN) classifier. We further present an analysis of various deep learning models while decomposing 3D VOI into different representations. The method is evaluated on two publicly available datasets containing CT scans of mediastinal and abdominal LNs. Our proposed approach yields sensitivities of 87% at 2.75 false positives per volume (FP/vol.) and 79% at 1.74 FP/vol. with the mediastinal and abdominal datasets, respectively. Our method presented a competitive performance in terms of sensitivity compared to the state-of-the-art methods and encountered very few false positives. We developed an automated framework for LNs detection using a modified U-Net with residual learning and 3D CNNs. The results indicate that our method could achieve high sensitivity with relatively low false positives, which helps avoid ineffective treatments.

Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour and the third leading cause of cancer death in the world. The emerging field of radiomics involves extracting many clinical image features that cannot be recognized by the human eye to provide information for precise treatment decision making. Radiomics has shown its importance in HCC identification, histological grading, microvascular invasion (MVI) status, treatment response, and prognosis, but there is no report on the preoperative prediction of programmed death ligand-2 (PD-L2) expression in HCC. The purpose of this study was to investigate the value of MRI radiomic features for the non-invasive prediction of immunotherapy target PD-L2 expression in hepatocellular carcinoma (HCC). A total of 108 patients with HCC confirmed by pathology were retrospectively analysed. Immunohistochemical analysis was used to evaluate the expression level of PD-L2. 3D-Slicer software was used to manually delineate volumes of interest (VOIs) and extract radiomic features on preoperative T2-weighted, arterial-phase, and portal venous-phase MR images. Least absolute shrinkage and selection operator (LASSO) was performed to find the best radiomic features. Multivariable logistic regression models were constructed and validated using fivefold cross-validation. The area under the receiver characteristic curve (AUC) was used to evaluate the predictive performance of each model. The results show that among the 108 cases of HCC, 50 cases had high PD-L2 expression, and 58 cases had low PD-L2 expression. Radiomic features correlated with PD-L2 expression. The T2-weighted, arterial-phase, and portal venous-phase and combined MRI radiomics models showed AUCs of 0.789 (95% CI: 0.702-0.875), 0.727 (95% CI: 0.632-0.823), 0.770 (95% CI: 0.682-0.875), and 0.871 (95% CI: 0.803-0.939), respectively. The combined model showed the best performance. The results of this study suggest that prediction based on the radiomic characteristics of MRI could noninvasively predict the expression of PD-L2 in HCC before surgery and provide a reference for the selection of immune checkpoint blockade therapy.

MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma

BRAF V600E mutation can compensate for the low detection rate by fine-needle aspiration (FNA) and is related to aggressiveness and lymph node metastasis. This study aimed to investigate the relationship between texture analysis features based on magnetic resonance imaging (MRI) and mutations. Retrospective analysis was performed on patients with postoperative pathology confirmed papillary thyroid carcinoma (PTC) from 2017 to 2021. One thousand one hundred and thirty-two texture features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) separately by outlining the tumor volume of interest (VOI). Univariate, minimum redundancy maximum relevance (mRMR), and multivariate analyses were used for feature selection to construct 3 models (T2WI, CE-T1WI, and combined model) to predict mutation. The reproducibility between observers was evaluated by intraclass correlation coefficient (ICC). Receiver operating characteristic (ROC) analysis was used to assess the performance of models. The diagnostic performance of the optimal cut-off value of models were calculated and validated by 10-fold cross-validation. A total of 80 PTCs (22 BRAF V600E wild-type and 58 BRAF V600E mutant) were included in our study. Good interobserver agreement was found on texture features we selected (all ICCs >0.75). The area under the ROC curves (AUCs) for the T2WI model, CE-T1WI model, and combined model were 0.83 (95% CI: 0.75-0.91), 0.83 (95% CI: 0.73-0.90), and 0.88 (95% CI: 0.81-0.94), respectively. The accuracy, sensitivity, specificity, PPV, and NPV were 0.776, 0.679, 0.905, 0.905, and 0.679 for the T2WI model at a cut-off value of 0.674; 0.755, 0.750, 0.762, 0.808, and 0.696 for the CE-T1WI model at a cut-off value of 0.573; 0.816, 0.893, 0.714, 0.806, and 0.833 for the combined model at a cut-off value of 0.420. MRI-based texture analysis could be a potential method for predicting BRAF V600E mutation in PTC preoperatively.