Dynamic Contrast-enhanced Research Articles

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BackgroundThe detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.MethodsWe recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan–Meier curve and log-rank tests were used for survival analysis.ResultsOverall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.ConclusionsThe proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Use of an AI-powered visualization platform to support precision clinical decision-making in patients with breast cancer undergoing neoadjuvant chemotherapy.

e12618 Background: Chemotherapy to reduce cancer burden and tumor size ("downstaging") is widely used in neoadjuvant treatment of early-stage breast cancer (ESBC) and can have a dramatic impact on selection of further treatment courses, including surgery. The resulting decision-making process between physicians and cancer patients requires detailed and effective communication of the patient's disease status, and clear presentation of this information in Tumor Board settings. Artificial intelligence (AI) -powered visualization platforms offer the potential to aid in evaluating breast tumor location and morphology, and accordingly optimize treatment. Additionally, these tools offer enhanced communication with patients regarding future treatment options, particularly serving as clinical decision support (CDS) tools. Methods: We assessed the performance of an AI platform that employs dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to render three-dimensional (3D) visualizations of breast tumors and 5 additional chest tissues on an independent validation dataset comprise of 100 ESBC cases with ground-truth tumor labels vetted by 2 breast-specialized radiologists. The platform provides 3D depictions of the tumor and its multi-tissue context, as well as functionalities for quantifying tumor morphology, tumor-to-landmark structure distances, excision volumes, and approximate surgical margins. We subsequently evaluated the tool's utility in a real-world setting to understand how the tool impacts clinical decision-making and patient communication, specifically around surgical planning in the context of neoadjuvant chemotherapy (NACT). Results: The platform generated accurate 3D tumor maps, as evidenced by a median Dice score of 75.6% and a median Hausdorff distances of 15.1 mm relative to the radiologist validation set. The automatic longest tumor dimension calculations were linearly correlated with radiologist direct measurements (Pearson correlation coefficient = 0.78) and had a median absolute difference of 6.3 mm. We found strong overlap between the platform’s ground-truth convex hulls, as shown by a median Dice score of 81.5%. When used by a skilled breast-specialized surgeon, the tool enabled rapid and intuitive patient-physician communication of the location and extent of disease, and potential degrees of downstaging to breast-conserving surgery. Conclusions: Detailed 3D tumor and surrounding multi-tissue depictions offer both qualitative and quantitative comprehension of cancer topology and may aid in formulating an optimal treatment approach for breast cancer treatment. We show that detailed depictions enhance communication between physicians and patients and may aid in streamlining decision-making.

Predicting peripheral neuropathy following neoadjuvant therapy in patients with breast cancer.

e12639 Background: Administration of neoadjuvant chemotherapy (NACT) or radiation is widely used to improve outcomes in patients with breast cancer. This strategy has demonstrated remarkable efficacy in reducing tumor burden and increasing the likelihood of breast-conserving surgery. However, neoadjuvant therapy can induce peripheral neuropathy, a condition characterized by nerve damage or dysfunction in the extremities that often manifests as pain, tingling, or weakness in the hands and feet. The exact mechanism remains unclear, although likely involves the cumulative toxic effects of therapy on peripheral nerves. Treatment-induced neuropathy poses a substantial unaddressed challenge that can significantly impact the patient’s quality of life. Therefore, predicting and ameliorating neuropathy are crucial aspects of comprehensive cancer care. Methods: We used an AI spatial biology platform to create and assess 3D depictions of a cohort of breast cancer patients based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Clinical, pathological, and radiological data were collected on 74 patients from Yale New Haven Hospital. Due to missing regimen data, the final dataset included 71 patients, with mean age 51.1 (14.5) years. An AI-based segmentation algorithm was successfully employed on 63 patients, of whom 13 had neuropathy following NACT. Vascular and microvascular (MV/V) kinetic parameters were computed for each, and radiomic features were extracted. Machine learning models were developed for clinical features, MV/V features, and the combined set of features. All models were tested with leave-one-out cross-validation. Results: The clinical model did not forecast neuropathy in any patient, indicating poor predictive ability. The MV/V model performed the best, with accuracy, precision, recall, and AUROC of 0.84, 1.0, 0.18, and 0.69, respectively. The combined model showed intermediate performance, with accuracy, precision, recall, and AUROC of 0.81, 0.5, 0.18, and 0.58, respectively. The patient population limits broad conclusions, but suggests features that are aligned with increased occurrence of neuropathy. In particular, vascular features that describe transfer of nutrients between tissues, as well as the density of blood vessels within and around the tumor, are prominent within both the MV/V and combined models. Conclusions: These results indicate that features associated with the degree of proximal and intratumoral vascularity may be predictive of peripheral neuropathy in breast cancer patients. Moreover, these features can be extracted from standard-of-care DCE-MRI data. Expansion of this vascular analysis could enable physicians to better plan and manage care for patients by tailoring treatment dose. [Table: see text]

Contrast-enhanced pelvic magnetic resonance imaging (MRI) for the prediction of treatment response in mucinous rectal cancer.

In mucinous rectal cancer, it can be difficult to differentiate between cellular and acellular mucin. The purpose of this study was to evaluate, in patients with mucinous rectal cancer, the value of static enhancement (enh) and pharmacokinetic parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting pathologic complete response. This retrospective cross-sectional study performed at Memorial Sloan Kettering Cancer Center included 43 patients (24 males and 19 females; mean age, 57 years) with mucinous rectal cancer who underwent MRI at baseline as well as after neoadjuvant chemoradiotherapy but before surgical resection between 2008 and 2019. Two radiologists independently segmented tumors on contrast-enhanced axial 3D T1-weighted images and sagittal DCE magnetic resonance images. On contrast-enhanced axial T1-weighted images, the static parameters enh and relative enhancement (renh) were estimated. On DCE images, the pharmacokinetic parameters Ktrans, kep, relative Ktrans (rKtrans), and relative kep (rkep) were estimated. Associations between all parameters with pathologic complete response were tested using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis was performed to assess the area under the curve (AUC) for each parameter. Of the 43 patients who were included in the study, 42/43 (98%) had evaluable contrast-enhanced axial T1-weighted images and 35/43 (81%) had evaluable DCE images. Of the patients with evaluable contrast-enhanced axial T1-weighted images, 9/42 (21%) had pathologic complete response and 33/42 (79%) did not have pathologic complete response. For reader 1, enh(pre-neoadjuvant chemotherapy), enh(post-neoadjuvant chemotherapy), and renh were significant predictors of pathologic complete response [P=0.045 (AUC =0.73), 0.039 (AUC =0.74), and 0.0042, respectively]. For reader 2, enh(pre-neoadjuvant chemotherapy) and renh were significant predictors [P=0.021 (AUC =0.77) and 0.002, respectively]. For renh, the AUC was 0.83 for reader 1, and 0.82 for reader 2. Meanwhile, of those patients with evaluable DCE images, 9/35 (26%) had pathologic complete response and 26/35 (74%) did not have pathologic complete response. Ktrans(pre-neoadjuvant chemotherapy), kep(pre-neoadjuvant chemotherapy), and rkep were significant predictors [P=0.016 (AUC =0.73), 0.00057 (AUC =0.81), and 0.0096 (AUC =0.74), respectively]. Static and pharmacokinetic parameters of contrast-enhanced MRI show promise to predict neoadjuvant treatment response. Static enh parameters, which are simpler to assess, showed the strongest prediction.

Correlation of Ktrans derived from dynamic contrast-enhanced MRI with treatment response and survival in locally advanced NSCLC patients undergoing induction immunochemotherapy and concurrent chemoradiotherapy

PurposeThis study aimed to investigate the prognostic significance of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters concerning tumor response following induction immunochemotherapy and survival outcomes in patients with locally advanced non-small cell...

Role of the Glymphatic System in Alzheimer's Disease and Treatment Approaches: A Narrative Review.

Currently, there is unavailability of disease-modifying medication for Alzheimer's disease (AD), a debilitating neurological disorder. The pathogenesis of AD appears to be complex and could be influenced by the glymphatic system present in the central nervous system (CNS). Amyloid-beta (Aβ) and other metabolic wastes are eliminated from the brain interstitium by the glymphatic system, which encompasses perivascular channels and astroglial cells. Dysfunction of the glymphatic system, which could occur due to decreased aquaporin 4 (AQP4) expression, aging-related alterations in the human brain, and sleep disruptions, may contribute to the pathogenesis of AD and also accelerate the development of AD by causing a buildup of harmful proteins like Aβ. Promising approaches have been examined for reducing AD pathology, including non-pharmacological therapies that target glymphatic function, like exercise and sleep regulation. In addition, preclinical research has also demonstrated the therapeutic potential of pharmaceutical approaches targeted at augmenting AQP4-mediated glymphatic flow. To identify the precise processes driving glymphatic dysfunction in AD and to find new treatment targets, more research is required. Innovative diagnostic and treatment approaches for AD could be made possible by techniques such as dynamic contrast-enhanced MRI, which promises to evaluate glymphatic function in neurodegenerative diseases. Treatment options for AD and other neurodegenerative diseases may be improved by comprehending and utilizing the glymphatic system's function in preserving brain homeostasis and targeting the mechanisms involved in glymphatic functioning. This review intends to enhance the understanding of the complex link between AD and the glymphatic system and focuses on the function of AQP4 channels in promoting waste clearance and fluid exchange.

Radiogenomics of homologous recombination deficiency (HRD) in breast cancer: Radiomics-based image biomarker for HRD.

554 Background: Homologous recombination deficiency (HRD) in breast cancer is an actionable target, with treatment efficacy potentially linked to timely detection. Notably, BRCA1 and BRCA2 are key genes implicated in HRD, and their pathogenic germline mutations are crucial criteria for the use of poly (ADP-ribose) polymerase inhibitors (PARPi). Although germline BRCA1/2 mutations are detectable in only a small fraction (1-5%) of breast cancers, recent whole-genome sequencing (WGS) studies have revealed that up to 22% of breast cancers also exhibit HRD-like genomic characteristics, suggesting a broader potential for HRD-targeted therapy. This has spurred the development of genomic-feature-based HRD identification methods. However, the invasive and costly nature of these methods poses a challenge. This has prompted our investigation into non-invasive image biomarkers for HRD identification. Methods: Invasive breast cancer patients were recruited from March 2021 to August 2022. Eligibility criteria included availability of fresh-frozen tumor tissue for WGS and suitability for dynamic contrast-enhanced (DCE) MRI to facilitate radiomics analysis. The association between HRD and radiomic features as well as clinicopathologic factors was investigated through rigorous genomic and statistical analysis. Results: This study encompassed 145 patients aged 20 to 69 years, of whom 16.6% (24 out of 145) were genomically identified as HRD. All patients harboring pathogenic germline mutations in BRCA1 (n=3) or BRCA2 (n=2), along with concomitant somatic LOH, exhibited HRD. A comprehensive analysis of 214 radiomic features revealed that tumor sphericity, informational measure of correlation 1 (IMC1), size-zone non-uniformity normalized (SZNN), and small area emphasis (SAE) derived from subtracted early dynamic T1-weighted imaging were significantly correlated with HRD. Moreover, these radiomic features demonstrated substantial diagnostic value, as evidenced by their ROC-AUC and PR-AUC performance against baseline models: Sphericity (ROC: 0.62, p=0.055; PR: 0.22, p=0.053), SAE (ROC: 0.63, p=0.052; PR: 0.32, p=0.007), SZNN (ROC: 0.63, p=0.04; PR: 0.30, p=0.007), and IMC1 (ROC: 0.62, p=0.067; PR: 0.29, p=0.02). Additionally, our findings suggest that these radiomic features are more directly related to HRD than to the triple-negative phenotype, which is commonly linked to HRD. Conclusions: We found that four radiomic features from MRI have significant predictive value for identifying HRD in breast cancer. Given their fair predictability and immediate availability, these features are ideally suited for a screening approach, potentially identifying more patients eligible for targeted treatment. This finding represents a significant advancement in the fields of radiology and precision oncology, opening new avenues for patient-specific treatment strategies.

Prediction of pCR with pretreatment MRI radiomics in triple negative breast cancer treated with neoadjuvant chemo-immunotherapy.

589 Background: Neoadjuvant chemo-immunotherapy (NACI) has become the new standard of care for stage II-III triple-negative breast cancers (TNBC). This approach is linked to high rates of pathological complete responses (pCR), yet factors influencing responses to this regimen remain poorly characterized. Our objective was to investigate whether pretreatment MRI perfusion-based radiomics could serve as a predictive tool for pCR to NACI in patients with TNBC. Methods: This prospective study enrolled women with early-stage TNBC undergoing NACI at two different centers from August 2021 to July 2023. Tumor segmentation and pharmacokinetic analyses of pretreatment multi-vendor dynamic-contrast-enhanced (DCE)-MRI were conducted using the standard perfusion Tofts model. Radiomics features were extracted from the Ktrans, Ve, and Slopemax maps. Unsupervised correlation analysis of reproducible features and least absolute shrinkage and selector operation were employed to construct a radiomics score. The performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC). A multivariable logistic regression model was employed to establish a multimodal nomogram. Performance metrics included discrimination, calibration, and clinical usefulness, assessed on the external validation set. Results: The training set comprised 112 patients from center 1 (mean age, 52 years ± 11 [SD]), and the external validation set included 83 participants from center 2 (mean age, 49 years ± 11 [SD]). Overall, the pCR rate was 70% (137/195). In the validation set, the radiomic score achieved an AUC of 0.80 (95% CI: 0.70, 0.89) for predicting pCR. A nomogram incorporating the radiomics score, tumor grade, and Ki67 yielded an AUC of 0.86 (95% CI: 0.78, 0.94) and demonstrated superior predictive efficacy compared to the histological prediction model (AUC, 0.76 [95% CI: 0.66, 0.87]; P = .02) in the validation set. Calibration curves of the nomogram exhibited good concordance between predicted and observed pCR in both sets. The radiomic score correlated with tumor size, washout enhancement curve, androgen receptor (AR), and combined positive score (CPS) PD-L1 levels (all P < 0.05). Conclusions: PretreatmentMRI-based radiomics is a reliable predictor of pCR to NACI and might help customizing treatment choice for early-stage TNBC patients.

Evaluating the association between radiomic features on breast MRI and dormant tumor cell presence in the SURMOUNT trial.

561 Background: While breast cancer treatment is effective at eradicating primary disease, approximately 20% of patients will ultimately recur at a distant site. Recurrence has been linked to the presence of minimal residual disease (MRD) after treatment, which presents as circulating micrometastases and dormant tumor cells (DTCs) located primarily in the bone marrow. Radiomic features on MRI are imaging biomarkers that have been linked with lesion heterogeneity, which in turn has been associated with disease aggressiveness. Here, we examine the association between radiomic features from the primary lesion on MRI and the presence of post-treatment DTCs. Noninvasively identifying patients at high risk of MRD would facilitate optimized treatment strategies to reduce recurrence risk. Methods: Dynamic contrast-enhanced MRI data from breast cancer survivors enrolled in the SURMOUNT trial (N=104) were retrospectively analyzed. Participants with MRIs collected within 90 days of diagnosis who consented to provide a bone marrow aspirate (BMA) sample were eligible. Working with a board-certified radiologist, we successfully identified and segmented the primary lesion for N=70 patients with BMA outcomes. Using the publicly available CaPTk software, we computed 33 radiomic features for each segmented lesion based on pixel intensity, morphology, and grey level textures. To reduce the dimensionality of the feature space, we used these feature values to group the participants into clusters—termed radiomic phenotypes — via agglomerative hierarchical clustering with significance testing. Fisher’s exact test was applied to test the statistical significance of the association between radiomic phenotype and BMA outcome (± DTC). For all statistical tests, p<0.05 was considered significant. Results: Of the N=70 participants analyzed, 58 were negative and 12 were positive for DTCs present in the bone marrow. Two statistically significant radiomic phenotypes ( p<0.05) were identified that were strongly associated with BMA outcome ( p=0.0251). 11 out of 12 positive outcomes were grouped into the second radiomic phenotype (Table). Conclusions: We found a statistically significant association between DTC presence and radiomic phenotypes derived from MRI data. Given the large percentage of positive outcomes assigned to the second radiomic phenotype, the radiomic phenotypes may indicate low and high risk of MRD, respectively. Importantly, this analysis shows promise for the ability to predict MRD from noninvasive imaging features. These results motivate continued data collection to increase sample size and identify a robust set of radiomic features, which may ultimately be used as predictors in potential models of MRD risk. [Table: see text]

Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored

ObjectivesTo compare the diagnostic performance of intratumoral and peritumoral features from different contrast phases of breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building radiomics models for differentiating molecular subtypes of breast cancer.MethodsThis retrospective study included 377 patients with pathologically confirmed breast cancer. Patients were divided into training set (n = 202), validation set (n = 87) and test set (n = 88). The intratumoral volume of interest (VOI) and peritumoral VOI were delineated on primary breast cancers at three different DCE-MRI contrast phases: early, peak, and delayed. Radiomics features were extracted from each phase. After feature standardization, the training set was filtered by variance analysis, correlation analysis, and least absolute shrinkage and selection (LASSO). Using the extracted features, a logistic regression model based on each tumor subtype (Luminal A, Luminal B, HER2-enriched, triple-negative) was established. Ten models based on intratumoral or/plus peritumoral features from three different phases were developed for each differentiation.ResultsRadiomics features extracted from delayed phase DCE-MRI demonstrated dominant diagnostic performance over features from other phases. However, the differences were not statistically significant. In the full fusion model for differentiating different molecular subtypes, the most frequently screened features were those from the delayed phase. According to the Shapley additive explanation (SHAP) method, the most important features were also identified from the delayed phase.ConclusionsThe intratumoral and peritumoral radiomics features from the delayed phase of DCE-MRI can provide additional information for preoperative molecular typing. The delayed phase of DCE-MRI cannot be ignored.Critical relevance statementRadiomics features extracted and radiomics models constructed from the delayed phase of DCE-MRI played a crucial role in molecular subtype classification, although no significant difference was observed in the test cohort.Key PointsThe molecular subtype of breast cancer provides a basis for setting treatment strategy and prognosis.The delayed-phase radiomics model outperformed that of early-/peak-phases, but no differently than other phases or combinations.Both intra- and peritumoral radiomics features offer valuable insights for molecular typing.Graphical

Computer-aided detection of prostate cancer in early stages using multi-parameter MRI: A promising approach for early diagnosis.

Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate. In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images. We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected. Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73. The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer.

Dynamic processes of positive/negative immune-vascular crosstalk loop via functional MRI-guided monitor

Immunotherapy holds significant promise to completely eliminate tumor, yet lack of timely evaluation of immune responses, which challenged by inherent lagging and pseudoprogression during immunotherapy. Immune-vascular crosstalk (IVC), an enhancement ring for the mutual regulation linking tumor vascular normalization with immune remodeling forms, emerging as a systematic indicator for immunotherapy outcomes, necessitates timely, non-invasive, and early detection of its status to enhance clinical benefits. Herein, positive and negative loop models of tumor IVC were established using different degree of photothermal therapy (PTT) based on NaBiF4:Gd@PDA@PEG nanoprobes. Multi-functional magnetic resonance imaging, including fast spoiled gradient-recalled (FSPGR), time-resolved imaging of contrast kinetics (TRICKs) and dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI), was applied to dynamically reflect normalized/ablated tumor vasculature, well-matched increased/decreased immune response corresponding to different status of IVC, confirmed by pathology results. This study proposes a novel biological method to noninvasively detect the positive/negative IVC loop, paving the visualized way for immunotherapy assessed benefits, which is expected to be extended to clinical translation.

Artificial Intelligence Quantification of Enhanced Synovium Throughout the Entire Hand in Rheumatoid Arthritis on Dynamic Contrast-Enhanced MRI.

Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast-enhanced (DCE) MRI in rheumatoid arthritis (RA) patients. To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels. Retrospective. Twelve RA patients underwent DCE-MRI with 27 phases for creating the AI model and tested on images with a variable number of phases from 35 RA patients. 3.0 T/DCE T1-weighted gradient echo sequence (mDixon, water image). The model was trained with various DCE-MRI time-intensity number of phases. Evaluations were conducted for similarity between AI segmentation and manual outlining in 51 ROIs with synovitis. The relationship between synovial volume via AI segmentation with rheumatoid arthritis magnetic resonance imaging scoring (RAMRIS) across whole hands was then evaluated. The reference standard was determined by an experienced musculoskeletal radiologist. Area under the curve (AUC) of receiver operating characteristic (ROC), Dice and Spearman's rank correlation coefficients, and interclass correlation coefficient (ICC). A P-value <0.05 was considered statistically significant. A minimum of 15 phases (acquisition time at least 2.5 minutes) was found to be necessary. AUC ranged from 0.941 ± 0.009 to 0.965 ± 0.009. The Dice score was 0.557-0.615. Spearman's correlation coefficients between the AI model and ground truth were 0.884-0.927 and 0.736-0.831, for joint ROIs and whole hands, respectively. The Spearman's correlation coefficient for the additional test set between the model trained with 15 phases and RAMRIS was 0.768. The AI-based classification model effectively identified synovitis pixels while excluding arteries. The optimal performance was achieved with at least 15 phases, providing a quantitative assessment of inflammatory activity in RA while minimizing acquisition time. 3 TECHNICAL EFFICACY: Stage 2.

TIST-Net: style transfer in dynamic contrast enhanced MRI using spatial and temporal information

Objective. Training deep learning models for image registration or segmentation of dynamic contrast enhanced (DCE) MRI data is challenging. This is mainly due to the wide variations in contrast enhancement within and between patients. To train a model effectively, a large dataset is needed, but acquiring it is expensive and time consuming. Instead, style transfer can be used to generate new images from existing images. In this study, our objective is to develop a style transfer method that incorporates spatio-temporal information to either add or remove contrast enhancement from an existing image. Approach. We propose a temporal image-to-image style transfer network (TIST-Net), consisting of an auto-encoder combined with convolutional long short-term memory networks. This enables disentanglement of the content and style latent spaces of the time series data, using spatio-temporal information to learn and predict key structures. To generate new images, we use deformable and adaptive convolutions which allow fine grained control over the combination of the content and style latent spaces. We evaluate our method, using popular metrics and a previously proposed contrast weighted structural similarity index measure. We also perform a clinical evaluation, where experts are asked to rank images generated by multiple methods. Main Results. Our model achieves state-of-the-art performance on three datasets (kidney, prostate and uterus) achieving an SSIM of 0.91 ± 0.03, 0.73 ± 0.04, 0.88 ± 0.04 respectively when performing style transfer between a non-enhanced image and a contrast-enhanced image. Similarly, SSIM results for style transfer from a contrast-enhanced image to a non-enhanced image were 0.89 ± 0.03, 0.82 ± 0.03, 0.87 ± 0.03. In the clinical evaluation, our method was ranked consistently higher than other approaches. Significance. TIST-Net can be used to generate new DCE-MRI data from existing images. In future, this may improve models for tasks such as image registration or segmentation by allowing small training datasets to be expanded.

Primary brain lymphoma and glioblastoma: evaluation of DCE T1 and DSC T2 MRI perfusion findings.

The accurate differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma multiforme (GBM) is clinically crucial due to the different treatment strategies between them. To define magnetic resonance imaging (MRI) perfusion findings in PCNSL to make a safe distinction from GBM with dynamic contrast-enhanced (DCE) T1 and DSC T2 MRI perfusion findings. This retrospective analysis included 19 patients with histopathologically diagnosed PCNSL and 21 individuals with GBM. DCE T1 vascular permeability perfusion values including K-trans, Ve, Kep, IAUGC, and DSC T2 perfusion values including cerebral blood volume (CBV) and cerebral blood flow (CBF) in axial sections from the pathological lesion and contralateral normal brain parenchyma were measured quantitatively using region of interest analysis. The study observed no statistically significant difference between patients with PCNSL (T/B cell) and GBM in the median values of DCE T1 perfusion ratios (P > 0.05). Nevertheless, the DSC T2 perfusion ratios showed a substantial distinction between the two groups. In contrast to patients with PCNSL (1.185 vs. 1.224, respectively), those with GBM had higher median levels of r-CBV and r-CBF (2.898 vs. 2.467, respectively; P 0.01). A cutoff value of ≤1.473 for r-CBV (Lesion/N) and ≤1.6005 for r-CBF (Lesion/N) was found to estimate the positivity of PCNSL. DSC T2 MRI perfusion values showed lower r-CBV and r-CBF values in PCNSL patients compared to GBM patients. According to the findings, r-CBV and r-CBF are the most accurate MRI perfusion parameters for distinguishing between PCSNL and GBM.

Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.

A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland-Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were -9.46 mm3 and -50.87 mm3, respectively. Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

E.2 Proof of concept for liquid biopsy: positive correlation between extracellular vesicles shed by high grade gliomas and volume of hypervascular tumour tissue on MRI

Background: High grade gliomas (HGGs) shed extracellular vesicles (EVs) into the bloodstream. EV-derived RNA (EV-RNA) can be detected in plasma, making it a potential biomarker for HGG recurrence after treatment. We sought to establish a baseline relationship between EV-RNA in plasma and hypervascular HGG tissue on MRI. Methods: Eight patients with a new diagnosis of HGG had measurements of plasma EV-RNA and contemporaneous dynamic susceptibility contrast (DSC) MRI. Patient-specific median signal intensity of corpus callosum (mSI-CC) was determined from 10 measurements on the relative cerebral blood volume (rCBV) map. Tumour tissue with signal intensity &gt; mSI-CC and &gt; 2x, &gt; 3x, &gt; 4x and &gt; 5x mSI-CC was segmented on the rCBV map. EV-RNA plasma concentration was correlated with tissue volumes. Results: Pearson correlation showed a significant positive relationship between EV-RNA plasma concentration and tissue volume with signal intensity &gt; mSI-CC (r(6) = 0.899, p = 0.002). No significant relationship could be detected for progressively smaller tissue volumes with signal intensity &gt; 2x, &gt; 3x, &gt; 4x and &gt; 5x mSI-CC. Conclusions: EV-RNA plasma concentration correlates strongly with the total volume of hypervascular HGG tissue on DSC MRI at baseline and merits further evaluation as a biomarker of tumour behaviour in longitudinal imaging studies.

Utility of combining multiple parameters of 123I-IMP SPECT and voxel-based morphometry MRI using a multiparametric scoring system for differentiating dementia with Lewy bodies from Alzheimer's disease.

Brain magnetic resonance imaging voxel-based morphometry (VBM) and perfusion single-photon emission computed tomography (SPECT) are useful for differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD). To determine whether combining multiple parameters of VBM and SPECT using a multiparametric scoring system (MSS) improves diagnostic accuracy in differentiating DLB from AD. In total, 23 patients with DLB and 57 patients with AD underwent imaging using a voxel-based specific regional analysis system for AD (VSRAD), an easy Z-score imaging system, and a Z-Graph using three-dimensional stereotactic surface projection. The cutoff values were determined using the receiver operating characteristic curve to differentiate DLB from AD for all parameters. Patients were scored 1 (DLB) or 0 (AD) for each statistically significant parameter, according to a threshold. The total score was determined for each case to obtain a cutoff value for the MSS. The mean Z-scores in the medial temporal lobes using the VSRAD were significantly lower in patients with DLB than in those with AD. Each Z-score of the summed Z-scores in all four segmented regions of the occipital lobes using the Z-Graph was significantly higher in patients with DLB than in those with AD. Among the five parameters, the highest accuracy was 80% for the Z-score of the summed Z-scores in the left medial occipital lobe. For the MSS, a cutoff value of four improved the diagnostic accuracy to 82%. MSS was more accurate than any single parameter of VBM or SPECT in differentiating DLB from AD.

Differentiating Low-Grade from High-Grade Intracranial Ependymomas: Comparison of Dynamic Contrast-Enhanced MRI and Diffusion-Weighted Imaging.

The aim of this study was to determine the diagnostic value of fractional plasma volume derived from dynamic contrast-enhanced perfusion MR imaging versus ADC, obtained from DWI in differentiating between grade 2 (low-grade) and grade 3 (high-grade) intracranial ependymomas. A hospital database was created for the period from January 2013 through June 2022, including patients with histologically-proved ependymoma diagnosis with available dynamic contrast-enhanced MR imaging. Both dynamic contrast-enhanced perfusion and DWI were performed on each patient using 1.5T and 3T scanners. Fractional plasma volume maps and ADC maps were calculated. ROIs were defined by a senior neuroradiologist manually by including the enhancing tumor on every section and conforming a VOI to obtain the maximum value of fractional plasma volume (Vpmax) and the minimum value of ADC (ADCmin). A Mann-Whitney U test at a significance level of corrected P = .01 was used to evaluate the differences. Additionally, receiver operating characteristic curve analysis was applied to assess the sensitivity and specificity of Vpmax and ADCmin values. A total of 20 patients with ependymomas (10 grade 2 tumors and 10 grade 3 tumors) were included. Vpmax values for grade 3 ependymomas were significantly higher (P < .002) than those for grade 2. ADCmin values were overall lower in high-grade lesions. However, no statistically significant differences were found (P = .12114). As a dynamic contrast-enhanced perfusion MR imaging metric, fractional plasma volume can be used as an indicator to differentiate grade 2 and grade 3 ependymomas. Dynamic contrast-enhanced perfusion MR imaging plays an important role with high diagnostic value in differentiating low- and high-grade ependymoma.

The impact of diffusion and perfusion-weighted imaging on glioma grading

Abstract Objectives Determining the grade of a glioma is extremely important for treatment planning and prognosis prediction. The study aimed to evaluate the usefulness of multiparametric perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) in preoperative glioma grading. Methods In this retrospective study, 63 individuals with brain tumors histologically confirmed, of which 23 had low-grade gliomas (LGGs) and 40 had high-grade gliomas (HGGs) were involved. We conducted this paper on apparent diffusion coefficient (ADC) maps using the entire tumor volume method, allowing us to use all ADC values of the tumor. Small-sample regions of interest (ROIs) were drawn to collect parameters of relative cerebral blood flow (rCBF), cerebral blood flow (CBF), and relative cerebral blood volume (rCBV), from both the tumor core and peritumoral edema. The PWI and DWI metrics were compared to identify the most accurate distinguishing HGGs and LGGs, analyze receiver operating characteristics (ROC), and evaluate the diagnostic performance using solitary parameters and combined. Results In diffusion MRI, there were significant differences in minimum ADC and mean ADC between LGGs and HGGs (p&lt;0.05), with the larger area under the curve (AUC) of 0.898 found for mean ADC at a cut-off value of 1.275, with sensitivity of 82.6 % and specificity of 90 %. The maximum ADC value did not differ significantly (p&gt;0.05). All perfusion parameters in both the tumor core and peritumoral edema area were significantly greater values in cases of HGG compared to LGG (p&lt;0.001), with the highest AUC of 0.946 found for solid tumor rCBV value (rCBVt), the cut-off is 3.585, sensitivity of 85 % and specificity of 100 %. Combining mean ADC and rCBVt provided an excellent AUC of 0.975, a sensitivity of 92.5 %, and a specificity of 91.3 % for differentiating between HGGs and LGGs. Conclusions Perfusion and diffusion MRI are valuable in discriminating between high-grade and low-grade gliomas, with the major criterion in the decision-making process being the combined mean ADC and rCBVt parameters.