Volume Of Interest Research Articles

NIMG-30. DEUTERIUM METABOLIC IMAGING (DMI) SHOWS A STRONG RELATION BETWEEN TUMOR GRADE AND GLUCOSE METABOLISM IN PRIMARY BRAIN TUMORS

Abstract BACKGROUND Metabolic imaging using FDG-PET is a key component of disease management in many cancers outside of the brain, but in brain tumors, it often shows low tumor-to-brain contrast because of high background signal from normal brain. Deuterium metabolic imaging (DMI) is a new 2H magnetic resonance spectroscopic imaging-based technique that when combined with administration of 2H-labeled glucose can provide high tumor-to-brain image contrast because it can detect downstream metabolism instead of only glucose uptake. In this observational study, DMI of glucose metabolism was performed in 22 patients with primary brain tumors of different grades, and at different disease stages. METHODS DMI data were acquired using a 4 tesla MRI scanner after oral administration of 0.75g/kg of [6,6’-2H2]-glucose in water. Metabolic maps were generated of 2H-labeled glucose, glutamate+glutamine (Glx), lactate (Lac), and Lac/(Lac+Glx), a proxy for the metabolic shift observed in many cancers known as the Warburg effect. Volumes of interest (VOI) based on contrast-enhancing and non-enhancing tumor regions were created using clinical MRIs and combined with DMI maps to generate tumor-specific values for Lac/(Lac+Glx). Eta-squared was calculated using tumor grade and Lac/(Lac+Glx) as independent and dependent variables; data are reported as mean±SD. Results – Tumor-to-brain image contrast for Lac/(Lac+Glx) was 2.57±0.93 for grade 4 (n=14), 1.74±0.75 for grade 3 (n=6) and 0.94±0.28 for grade 2 lesions (n=3). Lac/(Lac+Glx) was 0.36±0.13 for grade 4; 0.19±0.049 for grade 3; 0.09±0.042 for grade 2 lesions; and 0.14±0.043 for normal brain. Eta-squared between tumor grade and Lac/(Lac+Glx) was 0.52. CONCLUSIONS VOI-based analysis showed a high dependence of metabolism-specific image contrast to tumor grade. The apparent relation between lesion grade and Lac/(Lac+Glx) indicates the potential of DMI to complement anatomical MRI and possibly provide a valuable biomarker indicating active tumor tissue and could improve evaluating disease progression.

TMIC-29. IMMUNE SPECIFIC IMAGING TECHNIQUES IN GLIOBLASTOMA: SELFI MRI IDENTIFIES AGGRESSIVE AND IMMUNOSUPPRESSIVE BIOLOGICAL FEATURES THAT CONTRIBUTE TO THERAPEUTIC RESISTANCE

Abstract Gadolinium (Gd) enhanced MRI is nonspecific for identifying aggressive glioblastoma biology. Glioblastoma-associated macrophages (GAMs) are pivotal in regrowth. Segregation and Extravascular Localization of Ferumoxytol Imaging (SELFI) is a quantitative immune specific MRI technique. SELFI+ values quantify M2 GAMs by phagocytized Ferumoxytol (Fe), while SELFI- values delineate intravascular Fe, cerebral blood volume. The relationship between SELFI and biology driving therapeutic resistance remains unknown. We aimed to determine if SELFI identified immunosuppressive GAMs and biology contributing to poor outcomes, a capability not yet achievable in clinical practice. This prospective study included four patients with glioblastoma. All underwent preoperative Gd and Fe (7 mg/kg up to 510 mg) enhanced MRI allowing for image guided biopsy based on Gd enhancement and FLAIR hyperintensity. RNA was analyzed on GeneChip Clariom Arrays. Intraoperative biopsy location was referenced to MRI for placement of volumes of interest (VOIs). VOIs quantified MRI phenotype and SELFI values. Gene Set Enrichment Analysis (GSEA) assessed differential expression between MRI features. T-test assessed differences in SELFI (p <0.05) and GSEA significance was assessed by normalized enrichment score (NES) and false discovery rate (FDR) q-value. Four patients provided 36 image guided tissue samples with sufficient quality to assess the causal relationships between MRI features and transcriptomics. Stratification by patient MGMT methylation status (N= 3 unmethylated) demonstrated Gd+ regions had significantly elevated SELFI+ values (Mean±SD, Gd+ = 0.24±0.17, Gd- = 0.08±0.09, P<0.01). GSEA demonstrated that tissues with elevated SELFI+ and lowered SELFI- values were comprised of significantly upregulated hypoxia and transforming growth factor-beta (TGFβ) signaling; among others (NES>1.5, FDR q-value<0.05). SELFI MRI uniquely quantifies glioblastoma by localizing sites of biological aggressiveness known to contribute to therapeutic resistance and disease recurrence. Patient enrollment continues to further define the causal relationships between SELFI, GAMs, and glioblastoma biology.

Detecting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions Using T2w-Derived Radiomics Feature Maps in 3T Prostate MRI

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann–Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70–0.84). The diagnostic performance for TZ lesions (AUC: 0.83–1.00) was superior to PZ lesions (AUC: 0.74–0.85). In the bivariate models, performance in the PZ improved with AUCs >0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions.

Source-detector trajectory optimization for FOV extension in dental CBCT imaging

In dental imaging, Cone Beam Computed Tomography (CBCT) is a widely used imaging modality for diagnosis and treatment planning. Small dental scanning units are the most popular due to their cost-effectiveness. However, these small systems have the limitation of a small field of view (FOV) as the source and detector move at a limited angle in a circular path. This often limits the FOV size. In this study, we addressed this issue by modifying the source-detector trajectory of the small dental device. The main goal of this study was to extend the FOV algorithmically by acquiring projection data with optimal projection angulation and isocenter location rather than upgrading any physical parts of the device. A novel algorithm to implement a Volume of Interest (VOI) guided trajectory is developed in this study based on the small dental imaging device's geometry. In addition, this algorithm is fused with a previously developed off-axis scanning method which uses an elliptical trajectory, to compensate for the existing constraints and to further extend the FOV. A comparison with standard circular trajectory is performed. The FOV of such a standard trajectory is a circle of 11 cm diameter in the axial plane. The proposed novel trajectory extends the FOV significantly and a maximum FOV of 19.5 cm is achieved with the Structural Similarity Index Measure (SSIM) score ranging between (≈98-99%) in different VOIs. The study results indicate that the proposed source-detector trajectory can extend dental imaging FOV and increase imaging performance, which ultimately results in more precise diagnosis and enhanced patient outcomes.

Comparison study of quantitative susceptibility mapping with GRAPPA and wave-CAIPI: reproducibility, consistency, and microbleeds detection.

We compared quantitative susceptibility mapping (QSM) with wave-CAIPI 9 × (QSM_WC9 ×) with reference standard QSM with GRAPPA 2 × (QSM_G2 ×) in two MR scanners. We also compared detectability of microbleeds in both QSMs to demonstrate clinical feasibility of both QSMs. This prospective study was approved by the institutional review board and written informed consent was obtained from each subject. Healthy subjects were recruited to evaluate intra-scanner reproducibility, inter-scanner consistency, and inter-sequence consistency of QSM_G2 × and QSM_WC9 × at 2 MR scanners. Susceptibility values measured with volume of interests (VOIs) were evaluated. Patients who were requested for susceptibility weighted imaging were also recruited in this study to measure microbleeds on QSM_G2 × and QSM_WC9 × . The number of microbleeds was compared between two QSMs. Total 55 healthy subjects (male 34, female 21, 38.3years [23-79]) were included in this study. We investigated reproducibility and consistency of QSM_WC9 × by comparing reference standard QSM_G2 × in two MR scanners in this study, and high correlation (ρ, 0.93-0.97) and high intraclass correlation coefficient (ICC) (0.97-0.99) were obtained. Sixty patients (male 30, female 30; age, 55.4years [21-85]) were finally enrolled in this prospective study. The ICC of the detected number of microbleeds between QSM_G2 × and QSM_WC9 × was 0.99 (0.98-0.99). QSM_WC9 × and reference standard QSM_G2 × in two MR scanners showed good reproducibility and consistency in estimating magnetic susceptibilities. QSM_WC9 × and QSM_G2 × were also comparable in terms of microbleeds detection with good agreement of raters and high ICC.

Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics

BackgroundSentinel lymph node (SLN) biopsy (SLNB) is considered the gold standard for detecting SLN metastases in patients with invasive ductal breast cancer (IDC). However, SLNB is invasive and associated with several complications. Thus, this study aimed to evaluate the diagnostic performance of a non-invasive radiomics analysis utilizing 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography/computed tomography (18F-FDG-PET/CT) for assessing SLN metastasis in IDC patients.MethodsThis retrospective study included 132 patients with biopsy-confirmed IDC, who underwent 18F-FDG PET/CT scans prior to mastectomy or breast-conserving surgery with SLNB. Tumor resection or SLNB was conducted within one-week post-scan. Clinical data and metabolic parameters were analyzed to identify independent SLN metastasis predictors. Radiomic features were extracted from each PET volume of interest (VOI) and CT-VOI. Feature selection involved univariate and multivariate logistic regression analysis, and the least absolute shrinkage and selection operator (LASSO) method. Three models were developed to predict SLN status using the random forest (RF), decision tree (DT), and k-Nearest Neighbors (KNN) classifiers. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).ResultsThe study included 91 cases (32 SLN-positive and 59 SLN-negative patients) in the training cohort and 41 cases (29 SLN-positive and 12 SLN-negative patients) in the validation cohort. Multivariate logistic regression analysis identified Ki 67 and TLG as independent predictors of SLN status. Five PET-derived features, three CT-derived features, and two clinical variables were selected for model development. The AUC values of the RF, KNN, and DT models for the training cohort were 0.887, 0.849, and 0.824, respectively, and for the validation cohort were 0.856, 0.830, and 0.819, respectively. The RF model demonstrated the highest accuracy for the preoperative prediction of SLN metastasis in IDC patients.ConclusionThe PET-CT radiomics approach may offer robust and non-invasive predictors for SLN status in IDC patients, potentially aiding in the planning of personalized treatment strategies for IDC patients.

Cognitive Impact of β-Amyloid Load in the Rapid Eye Movement Sleep Behavior Disorder-Lewy Body Disease Continuum.

Rapid eye movement sleep behavior disorder (RBD) is linked to the diffuse-malignant subtype and higher cognitive burden in Lewy body disease (LBD). This study explores brain β-amyloid deposition and its association with cognitive decline across the RBD-LBD continuum. Patients with isolated RBD (iRBD), Parkinson's disease with probable RBD (PDRBD), and dementia with Lewy bodies with probable RBD (DLBRBD) underwent 18F-florbetaben positron emission tomography, 3T magnetic resonance imaging scans, and comprehensive neuropsychological assessments. Subjects were categorized as cognitively normal (NC), mild cognitive impairment (MCI), or dementia. Global and regional standardized uptake value ratios (SUVR) were estimated in predefined cognitive volumes of interest (VOI) derived from voxel-wise comparison analysis among the cognitive groups, namely the prefrontal, parietal, precentral cortices, lingual gyrus, and supplementary motor area. Generalized linear models assessed the relationship between 18F-florbetaben SUVRs and neuropsychological testing, adjusting for age and sex. Subgroup analysis focused on the polysomnography-confirmed iRBD-continuum subset (n = 41) encompassing phenoconverters and nonconverters in our prospective iRBD cohort. Eighty-six subjects were classified as follows: 14 NC, 54 MCI, and 18 dementia. The proportion of positive β-amyloid scans increased with advanced cognitive stages (P = 0.038). β-Amyloid signals in cognitive VOIs were elevated in subgroups showing impairment in Trail-Making Test B (TMT-B). A linear association between TMT-B z score and global cortical β-amyloid levels was observed in the iRBD-continuum subset (P = 0.013). Cortical β-amyloid accumulates with declines in executive function within the RBD-LBD continuum. TMT-B performance may be a useful marker associating with β-amyloid load, particularly in the iRBD population. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Diffusion MRI is superior to quantitative T2-FLAIR mismatch in predicting molecular subtypes of human non-enhancing gliomas.

This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes. A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses. T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051). T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.

Radiomic features on multiparametric MRI for differentiating pseudoprogression from recurrence in high-grade gliomas.

Distinguishing between tumor recurrence and pseudoprogression (PsP) in high-grade glioma postoperatively is challenging. This study aims to enhance this differentiation using a combination of intratumoral and peritumoral radiomics. To assess the effectiveness of intratumoral and peritumoral radiomics in improving the differentiation between high-grade glioma recurrence and pseudoprogression after surgery. A total of 109 cases were randomly divided into training and validation sets, with 1316 features extracted from intratumoral and peritumoral volumes of interest (VOIs) on conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps. Feature selection was performed using the mRMR algorithm, resulting in intratumoral (100 features), peritumoral (100 features), and combined (200 features) subsets. Optimal features were then selected using PCC and RFE algorithms and modeled using LR, SVM, and LDA classifiers. Diagnostic performance was compared using area under the receiver operating characteristic curve (AUC), evaluated in the validation set. A nomogram was established using radscores from intratumoral, peritumoral, and combined models. The combined model, utilizing 14 optimal features (8 peritumoral, 6 intratumoral) and LR as the best classifier, outperformed the single intratumoral and peritumoral models. In the training set, the AUC values for the combined model, intratumoral model, and peritumoral model were 0.938, 0.921, and 0.847, respectively; in the validation set, the AUC values were 0.841, 0.755, and 0.705. The nomogram model demonstrated AUCs of 0.960 (training set) and 0.850 (validation set). The combination of intratumoral and peritumoral radiomics is effective in distinguishing high-grade glioma recurrence from pseudoprogression after surgery.

Comparison of Early Postoperative Stress Distribution around Short and Tapered Wedge Stems in Femurs with Different Femoral Marrow Cavity Geometries Using Finite Element Analysis.

In total hip arthroplasty (THA), the ideal stem length remains uncertain; different stem lengths are used in different cases or institutions. We aimed to compare the stress distributions of cementless tapered wedges and short stems in femurs with different femoral marrow geometries and determine the appropriate fit. Finite element models were created and analyzed using HyperMesh and LS-DYNA R11.1, respectively. The 3-dimensional shape data of the femurs were extracted from computed tomography images using the RETOMO software. Femurs were divided into 3 groups based on the Dorr classification. The computer-aided design data of cementless tapered wedge-type and short stems were used to select the appropriate size. In the finite element analysis, the loading condition of the femur was assumed to be walking. Volumes of interest (VOIs) were placed within the femur model at the internal and external contact points of the stem based on Gruen zones. The average stresses and strain energy density (SED) of the elements included in each VOI were obtained from the preoperative and postoperative models. The von Mises stress and SED distributions of the cementless tapered wedge and short stems were similar in their respective Dorr classifications. In both stems, the von Mises stress and SED after THA were lower than before THA. The von Mises stress and SED of the cementless tapered wedge stem were higher than those of short stems. Cementless tapered wedge-type stems tended to have lower rates of change than short stems; however, Dorr C exhibited the opposite trend. In the Dorr classification comparison, the von Mises stress and SED were greater for both stems in the order of Dorr C > Dorr B > Dorr A, from Zone 2 to Zone 6. In Dorr A and B, the short stem exhibited a natural stress distribution closer to the preoperative femur than the tapered wedge stem; however, in Dorr C, the short stem may have a greater effect on stress distribution, suggesting that it may cause greater effects, such as fracture in the early postoperative period, than other Dorr types.

A Methodology to Measure Glucose Metabolism by Quantitative Analysis of PET Images.

Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) tracer is the standard clinical technique to measure myocardial and vessel metabolism and viability and to investigate the metabolic syndrome associated with cardiovascular diseases. The quantitative analysis of PET images allows one to study the cardiovascular physiological processes, by extracting quantitative parameters from the analysis of the tracer kinetic. Here, we propose a new methodology to quantify and evaluate the evolution of glucose metabolism inside the myocardium and the large vascular structures over time. We merge and analyze PET and CT cardiac images, extracting different volumes of interest (VOI) and performing quantitative measurements. To validate it, we apply the methodology to merge images of the aorta vessel for patients affected by metabolic syndrome. The application of the proposed approach to the use case reveals a correlation between administered drugs and metabolic syndrome, measuring the glucose metabolic rate (MRGlu) in both the myocardium and aorta. The proposed methodology can be used to evaluate some cardiovascular risk indexes of diabetic patients, too. The proposed methodology can also be deployed to analyze other application domains.

Quantifying the effect of scanning parameters on digital volume correlation analysis for in situ X-ray imaging of concrete

The potential effects of X-ray Computed Tomography (XCT) scan settings on the kinematic field computed via Digital Volume Correlation (DVC) are discussed in this paper. A parametric sensitivity study was conducted, including a series of acquired XCT scans, probing the same volume of interest (VOI) of a concrete specimen, and applying various combinations of XCT scan settings. Different values were adopted for the number of projections acquired, the exposure time and the pixel size. The DVC results revealed a linear correlation between the mean normalised correlation residuals, designating the quality of the correlation and the reliability of the computed kinematic field and the scanning parameters. Gaussian and median filters were applied to the raw data to explore the effect of image noise smoothness in the DVC analysis. The significant reduction noted for the mean normalised correlation residuals was not accompanied by similar levels of change for the corresponding error uncertainties, as defined by the displacement magnitude. The concept was tested against a realistic loading scenario, where the DVC kinematic fields, computed for various XCT “recipes”, were compared. Slim differences in the distribution of the mean minimum principal strain were observed.

Improved Quantification of MicroPET/CT Imaging Using CT-derived Scaling Factors.

Combined micro-PET/CT scanners are widely employed to investigate models of brain disorders in rodents using PET-based coregistration. We examined if CT-based coregistration could improve estimates of brain dimensions and consequently estimates of nondisplaceable binding potential (BPND) in rodent PET studies. PET and CT scans were acquired on 5 female and 5 male CD-1 mice with 3-[18F]fluoro-5-(2-pyridinylethynyl)benzonitrile ([18F]FPEB), a radiotracer for the metabotropic glutamate receptor subtype 5 (mGluR5). In the proposed PET/CT (PTCT) approach, the tracer-specific standard volume was dimension-customized to each animal using the scaling factors from CT-to-standard CT coregistration to simplify PET-to-standard PET coregistration (i.e., 3 CT- and 6 PET-derived parameters). For comparison, conventional PET-based coregistration was performed with 9 (PT9) or 12 (PT12) parameters. PET frames were transferred to the standard space by the three approaches (PTCT, PT9, and PT12) to obtain regional time-activity curves (TACs) and BPND in 14 standard volumes of interest (VOIs). Lastly, CT images of the animals were transferred to the standard space by CT-based parameters from PTCT and with the scaling factors replaced with those from PET-based PT9 to evaluate agreement of the skull to the standard CT. The PET-based approaches showed various degrees of underestimations of scaling factors in the posterior-anterior-direction compared to PTCT, which resulted in negatively proportional overestimation of radioactivity in the cerebellum (reference region) up to 20%, and proportional, more prominent underestimation of BPND in target regions down to -50%. The skulls of individual animals agreed with the standard skull for scaling factors from PTCT but not for the scaling factors from PT9, which suggested inaccuracy of the latter. The results indicated that conventional PET-based coregistration approaches could yield biased estimates of BPND in proportion to errors of brain dimensions when applied to tracers for which the cerebellum serves as reference region. The proposed PTCT provides evidence of a quantitative improvement over PET-based approaches for brain studies using micro-PET/CT scanners.

Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor

BackgroundThis study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences.MethodsThis study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists.ResultsThe single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists.ConclusionsThe combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.

Limbic Network Derangement Mediates Unawareness of Apathy in Mild Cognitive Impairment due to Alzheimer's Disease: Clues from [18F]FDG PET Voxel-Wise Analysis.

Discrepancy between caregiver and patient assessments of apathy in mild cognitive impairment (MCI) is considered an index of apathy unawareness, independently predicting progression to AD dementia. However, its neural underpinning are uninvestigated. To explore the [18F]FDG PET-based metabolic correlates of apathy unawareness measured through the discrepancy between caregiver and patient self-report, in patients diagnosed with MCI. We retrospectively studied 28 patients with an intermediate or high likelihood of MCI-AD, progressed to dementia over an average of two years, whose degree of apathy was evaluated by means of the Apathy Evaluation Scale (AES) for both patients (PT-AES) and caregivers (CG-AES). Voxel-based analysis at baseline was used to obtain distinct volumes of interest (VOIs) correlated with PT-AES, CG-AES, or their absolute difference (DISCR-AES). The resulting DISCR-AES VOI count densities were used as covariates in an inter-regional correlation analysis (IRCA) in MCI-AD patients and a group of matched healthy controls (HC). DISCR-AES negatively correlated with metabolism in bilateral parahippocampal gyrus, posterior cingulate cortex, and thalamus, PT-AES score with frontal and anterior cingulate areas, while there was no significant correlation between CG-AES and brain metabolism. IRCA revealed that MCI-AD patients exhibited reduced metabolic/functional correlations of the DISCR-AES VOI with the right cingulate gyrus and its anterior projections compared to HC. Apathy unawareness entails early disruption of the limbic circuitry rather than the classical frontal-subcortical pathways typically associated with apathy. This reaffirms apathy unawareness as an early and independent measure in MCI-AD, marked by distinct pathophysiological alterations.

Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2-weighted imaging: application to Parkinson disease diagnosis.

Accurately distinguishing between Parkinson disease (PD) and healthy controls (HCs) through reliable imaging method is crucial for appropriate therapeutic intervention. However, PD diagnosis is hindered by the subjective nature of the evaluation. We aimed to develop an automatic deep-learning method that can segment the substantia nigra areas on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) and further differentiate patients with PD from HCs using a machine learning algorithm. Magnetic resonance imaging (MRI) data from 83 patients with PD and 83 age- and sex-matched HCs were obtained on the same 3.0-T MRI scanner. A deep learning method with Swin-Unet was developed to segment volumes of interest (VOIs) on SWI and then map the VOIs on SWI to the corresponding T2WI; features were then extracted from the VOIs on SWI and T2WI. Three machine learning models were developed and compared to differentiate those with PD from HCs. Swin-Unet achieved a better Dice coefficient than did U-Net in SWI segmentation (0.832 vs. 0.712). Machine learning models outperformed visual analysis (P>0.05), and logistic regression (LR) achieved the best performance [area under the curve (AUC) ≥0.819] and the most stable (relative standard deviations in AUC ≤0.05). The test results showed that the AUC of the LR model based on SWI segmentation was 0.894 while that of the LR model based on T2WI segmentation was 0.876. There was no significant difference in VOIs based on manual labeling or automatic segmentation across T2WI, SWI, or a combination of the two (P>0.05). The AUCs of the LR model based on automatic segmentation were close to those of the model based on manual labeling (P>0.05). Our approach could provide a powerful and useful method for automatically and rapidly diagnosing PD in the clinic with only T2WI.

Accurate Whole-Brain Image Enhancement for Low-Dose Integrated PET/MR Imaging Through Spatial Brain Transformation.

Positron emission tomography/magnetic resonance imaging (PET/MRI) systems can provide precise anatomical and functional information with exceptional sensitivity and accuracy for neurological disorder detection. Nevertheless, the radiation exposure risks and economic costs of radiopharmaceuticals may pose significant burdens on patients. To mitigate image quality degradation during low-dose PET imaging, we proposed a novel 3D network equipped with a spatial brain transform (SBF) module for low-dose whole-brain PET and MR images to synthesize high-quality PET images. The FreeSurfer toolkit was applied to derive the spatial brain anatomical alignment information, which was then fused with low-dose PET and MR features through the SBF module. Moreover, several deep learning methods were employed as comparison measures to evaluate the model performance, with the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and Pearson correlation coefficient (PCC) serving as quantitative metrics. Both the visual results and quantitative results illustrated the effectiveness of our approach. The obtained PSNR and SSIM were 41.96 ± 4.91 dB (p < 0.01) and 0.9654 ± 0.0215 (p < 0.01), which achieved a 19% and 20% improvement, respectively, compared to the original low-dose brain PET images. The volume of interest (VOI) analysis of brain regions such as the left thalamus (PCC = 0.959) also showed that the proposed method could achieve a more accurate standardized uptake value (SUV) distribution while preserving the details of brain structures. In future works, we hope to apply our method to other multimodal systems, such as PET/CT, to assist clinical brain disease diagnosis and treatment.

Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation?

We studied the application of CT texture analysis in adrenal incidentalomas with baseline characteristics of benignity that are highly suggestive of adenoma to find whether there is a correlation between the extracted features and clinical data. Patients with hormonal hypersecretion may require medical attention, even if it does not cause any symptoms. A total of 206 patients affected by adrenal incidentaloma were retrospectively enrolled and divided into non-functioning adrenal adenomas (NFAIs, n = 115) and mild autonomous cortisol secretion (MACS, n = 91). A total of 136 texture parameters were extracted in the unenhanced phase for each volume of interest (VOI). Random Forest was used in the training and validation cohorts to test the accuracy of CT textural features and cortisol-related comorbidities in identifying MACS patients. Twelve parameters were retained in the Random Forest radiomic model, and in the validation cohort, a high specificity (81%) and positive predictive value (74%) were achieved. Notably, if the clinical data were added to the model, the results did not differ. Radiomic analysis of adrenal incidentalomas, in unenhanced CT scans, could screen with a good specificity those patients who will need a further endocrinological evaluation for mild autonomous cortisol secretion, regardless of the clinical information about the cortisol-related comorbidities.

Reproducibility of Radiomic Features in Glial Brain Tumors.

In our recent research, we have effectively demonstrated the feasibility of classifying magnetic resonance images (MRI) of glial tumors into four histological types utilizing standardized volume of interest (VOI), radiomics and machine learning. This research aims to determine the reproducibility of our approach when the locations of VOI are changed. We were able to demonstrate high reproducibility of ML results when the same feature selection methodology was employed across different VOIs. However, the reproducibility of radiomic features and their sets among various VOIs was not ensured for the sample size (n = 85) studied. The limited reproducibility of radiomic features should be taken into account when evaluating radiomics studies in glial tumors.

Intratumoral habitat radiomics based on magnetic resonance imaging for preoperative prediction treatment response to neoadjuvant chemotherapy in nasopharyngeal carcinoma.

The aim of this study is to determine intratumoral habitat regions from multi-sequences magnetic resonance imaging (MRI) and to assess the value of those regions for prediction of patient response to neoadjuvant chemotherapy (NAC) in nasopharyngeal carcinoma (NPC). Two hundred and ninety seven patients with NPC were enrolled. Multi-sequences MRI data were used to outline three-dimensional volumes of interest (VOI) of the whole tumor. The original imaging data were divided into two groups, which were resampled to an isotropic resolution of 1 × 1 × 1mm3 (group_1mm) and 3 × 3 × 3mm3 (group_3mm). Nineteen radiomics features were computed for each voxel of three sequences in group_3mm, within the tumor region to extract local information. Then, k-means clustering was implemented to segment the whole tumor regions in two groups. After radiomics features were extracted and dimension reduction, habitat models were built using Multi-Layer Perceptron (MLP) algorithm. Only T stage was included as the clinical model. The habitat3mm model, which included 10 radiomics features, achieved AUCs of 0.752 and 0.724 in the training and validation cohorts, respectively. Given the slightly better outcome of habitat3mm model, nomogram was developed in combination with habitat3mm model and T stage with the AUC of 0.749 and 0.738 in the training and validation cohorts. The decision curve analysis provides further evidence of the nomogram's clinical practicality. A nomogram based on intratumoral habitat predicts the efficacy of NAC in NPC patients, offering the potential to improve both the treatment plan and patient outcomes.