Tumor Enhancement Research Articles

Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma

BackgroundGlioblastoma (GB) is among the most devastative brain tumors, which usually comprises sub-regions like enhancing tumor (ET), non-enhancing tumor (NET), edema (ED), and necrosis (NEC) as described on MRI. Semi-automated algorithms to extract these tumor subpart volumes and boundaries have been demonstrated using dynamic contrast-enhanced (DCE) perfusion imaging. We aim to characterize these sub-regions derived from DCE perfusion MRI using routine 3D post-contrast-T1 (T1GD) and FLAIR images with the aid of Radiomics analysis. We also explored the possibility of separating edema from tumor sub-regions by extracting the most influential radiomics features. MethodsA total of 89 patients with histopathological confirmed IDH wild type GB were considered, who underwent the MR imaging with DCE perfusion-MRI. Perfusion and kinetic indices were computed and further used to segment tumor sub-regions. Radiomics features were extracted from FLAIR and T1GD images with PyRadiomics tool. Statistical analysis of the features was carried out using two approaches as well as machine learning (ML) models were constructed separately, i) within different tumor sub-regions and ii) ED as one category and the remaining sub-regions combined as another category. ML based predictive feature maps was also constructed. ResultsSeven features found to be statistically significant to differentiate tumor sub-regions in FLAIR and T1GD images, with p-value < 0.05 and AUC values in the range of 0.72 to 0.93. However, the edema features stood out in the analysis. In the second approach, the ML model was able to categorize the ED from the rest of the tumor sub-regions in FLAIR and T1GD images with AUC of 0.95 and 0.89 respectively. ConclusionRadiomics-based specific feature values and maps help to characterize different tumor sub-regions. However, the GLDM_DependenceNonUniformity feature appears to be most specific for separating edema from the remaining tumor sub-regions using conventional FLAIR images. This may be of value in the segmentation of edema from tumors using conventional MRI in the future.

Safe administration of temozolomide in end-stage renal disease patients.

Glioblastoma multiforme is the most common and aggressive type of central nervous system tumor. We present a novel case of the challenges of dosing temozolomide in a patient with end stage renal disease on peritoneal hemodialysis with unpredictable clearance and toxicities. In this case, a 60-year-old male with a past medical history of hypertension and Stage V chronic kidney disease presented with worsening confusion and word-finding difficulty in the emergency department. Magnetic resonance imaging demonstrated a large intra-axial mass within the posterior left frontal lobe measuring 4.5 × 4.1 × 3.5 cm with irregularly, predominant peripheral tumoral enhancement. The patient underwent a surgical resection which confirmed the diagnosis of glioblastoma (grade 4). The standard treatment for glioblastoma is 6 weeks of radiation therapy and daily temozolomide. Given his history of renal dysfunction and limited data on the safety of temozolomide in patients on hemodialysis (HD), the patient was administered dose-reduced temozolomide and closely monitored for toxicities. Temozolomide was successfully up-titrated to the full dose. Renal replacement therapy is a life-saving treatment for end-stage kidney disease patients. A stepwise increase in the dosage of temozolomide did not increase the risk of toxicity with HD. There are no studies with patients on temozolomide and peritoneal dialysis. Our case transitioned to peritoneal dialysis from HD without significant toxicity from temozolomide. As a more substantial proportion of the population becomes dialysis-dependent in the coming years, we need further studies to understand the safety profiles of chemotherapeutic agents in this complex subset of patients.

Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer.

Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four breast radiologists (with 3-15 years of experience) blinded to whether the method of contrast was real or simulated assessed image quality (excellent, acceptable, good, poor, or unacceptable), presence of tumor enhancement, and maximum index mass size by using 22 pairs of real and simulated contrast-enhanced MRI scans. Quantitative comparison was performed using whole-breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap. Results Ninety-six MRI examinations in 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRI scans as having the appearance of a real MRI scan with tumor enhancement. Index mass sizes on real and simulated MRI scans demonstrated good to excellent agreement (intraclass correlation coefficient, 0.73-0.86; P < .001) without significant differences (mean differences, -0.8 to 0.8 mm; P = .36-.80). Almost all simulated MRI scans (84 of 88 [95%]) were considered of diagnostic quality (ratings of excellent, acceptable, or good). Quantitative analysis demonstrated strong similarity (structural similarity index, 0.88 ± 0.05), low voxel-wise error (symmetric mean absolute percent error, 3.26%), and Dice coefficient of enhancing tumor overlap of 0.75 ± 0.25. Conclusion It is feasible to generate simulated contrast-enhanced breast MRI scans with use of deep learning. Simulated and real contrast-enhanced MRI scans demonstrated comparable tumor sizes, areas of tumor enhancement, and image quality without significant qualitative or quantitative differences. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Slanetz in this issue. An earlier incorrect version appeared online. This article was corrected on January 17, 2023.

The IAP antagonist birinapant enhances chimeric antigen receptor T cell therapy for glioblastoma by overcoming antigen heterogeneity

Antigen heterogeneity that results in tumor antigenic escape is one of the major obstacles to successful chimeric antigen receptor (CAR) Tcell therapies in solid tumors including glioblastoma multiforme (GBM). To address this issue and improve the efficacy of CAR Tcell therapy for GBM, we developed an approach that combines CAR Tcells with inhibitor of apoptosis protein (IAP) antagonists, a new class of small molecules that mediate the degradation of IAPs, to treat GBM. Here, we demonstrated that the IAP antagonist birinapant could sensitize GBM cell lines and patient-derived primary GBM organoids to apoptosis induced by CAR Tcell-derived cytokines, such as tumor necrosis factor. Therefore, birinapant could enhance CAR Tcell-mediated bystander death of antigen-negative GBM cells, thus preventing tumor antigenic escape in antigen-heterogeneous tumor models invitro and invivo. In addition, birinapant could promote the activation of NF-κB signaling pathways in antigen-stimulated CAR Tcells, and with a birinapant-resistant tumor model we showed that birinapant had no deleterious effect on CAR Tcell functions invitro and invivo. Overall, we demonstrated the potential of combining the IAP antagonist birinapant with CAR Tcells as a novel and feasible approach to overcoming tumor antigen heterogeneity and enhancing CAR Tcell therapy forGBM.

NIMG-53. DELTA RELAXOMETRY WITH CONTRAST ENHANCED MAGNETIC RESONANCE FINGERPRINTING: INITIAL APPLICATION TO DIFFERENTIATE BRAIN METASTASES AND GLIOBLASTOMA

Abstract PURPOSE The utility of post-contrast magnetic resonance fingerprinting (MRF) for brain tumor assessment has not been assessed. The aim of this work is to report differences in MRF-derived delta-relaxometry metrics between brain metastases and glioblastoma, offering a new strategy for tumor differentiation. METHODS Post- and pre-contrast MRF (T1, T2 and proton density maps) were acquired from 29 patients (14 brain metastases (Met), 15 glioblastoma (GB)) along with conventional MRI (T1w, T1w-Gd, T2w, T2w-FLAIR, and ADC). Post-contrast MRF was skull-stripped and non-linearly co-registered with pre-contrast MRF. Delta relaxometry metrics (ΔR1/ΔR2 ratio, ΔR1, ΔR2, and normalized ΔR1/ΔR2) were calculated in the native image space (1.2x1.2x3.0 mm3). Tumor regions were segmented using DeepMedic into necrotic core (NC), enhancing tumor (ET), and peritumoral edema (ED). ROI-averaged means of delta relaxometry metrics were compared using paired t-test for ET and ED regions in Met vs GB. Voxel-wise ΔR1/ΔR2 ratios (log transformed) were compared using unpaired two-tailed t-test with Bonferroni correction to quantify distribution differences in delta relaxometry between Met and GB. RESULTS Across all voxels of all patients in each group, ΔR1/ΔR2 ratios between Met and GB were different (0.272±0.61 vs 0.247±0.66 in ED, p &amp;lt; .001; 0.278±0.69 vs 0.264±0.67 in ET, p &amp;lt; .001). On a per-patient basis, the median, 75th percentile, and 90th percentile of ΔR1/ΔR2 ratios were different between ET and ED regions (p &amp;lt; .001). Within-ROI (NC, ET, and ED) averaged mean ΔR1/R2 ratios were not significantly different between Met and GB. CONCLUSION Voxel-wise distribution of ΔR1/ΔR2 ratios was different between tumor types (Met and GB) in both ET and ED regions. ROI-averaged ΔR1/ΔR2 ratios were different between ET and ED regions, but not specific for tumor type. Delta relaxometry provides a unique tumor-specific contrast and shows potential for solid tumor as well as peritumoral edema differentiation between Met and GB.

NIMG-102. RAPNO-DEFINED SEGMENTATION AND VOLUMETRIC ASSESSMENT OF PEDIATRIC BRAIN TUMORS ON MULTI-PARAMETRIC MRI SCANS USING DEEP LEARNING; A ROBUST TOOL WITH POTENTIAL APPLICATION IN TUMOR RESPONSE ASSESSMENT

Abstract Volumetric measurements of whole tumor and its components on MRI scans, facilitated by automatic segmentation tools, are essential to reduce inter-observer variability in monitoring tumor progression and response assessment for pediatric brain tumors. Here, we present a fully automatic segmentation model based on deep learning that reliably delineates the tumor components recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group for evaluation of treatment response. Multi-parametric MRI (mpMRI) scans (T1-pre, T1-post, T2, and T2-FLAIR), acquired on multiple MRI scanners with different field strengths and vendors, for a cohort of 218 pediatric patients with a variety of histologically confirmed brain tumor subtypes were collected. The mpMRI scans were co-registered and manually segmented by experienced neuroradiologists in consensus to identify the tumor subregions including the enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED) regions. A convolutional neural network model based on DeepMedic architecture was trained using mpMRI scans as the inputs for segmentation of the whole tumor and subregions. The trained model showed excellent performance in segmentation of the whole tumor, as suggested by median dice of 0.90/0.85 for validation (n = 44)/independent test (n = 22) sets. ET and non-enhancing components (union of NET, CC, and ED) were segmented with median dice scores of 0.78/0.84 and 0.76/0.74 for validation/test sets, respectively. The automated and manual segmentations demonstrated strong agreement in estimating VASARI (Visually AcceSAble Rembrandt Images) MRI features with Pearson’s correlation coefficient R &amp;gt; 0.75 (p &amp;lt; 0.0001) for ET, NET, CC, and ED components. Our proposed automated segmentation method developed based on MRI scans acquired with different protocols, equipment, and from a variety of brain tumor subtypes, shows potential application for reliable and generalizable volumetric measurements which can be used for treatment response assessment in clinical trials.

NIMG-88. A TRANSFER LEARNING APPROACH FOR AUTOMATIC SEGMENTATION OF TUMOR SUB-COMPARTMENTS IN PEDIATRIC MEDULLOBLASTOMA USING MULTIPARAMETRIC MRI: PRELIMINARY FINDINGS

Abstract PURPOSE Superior outcomes for medulloblastoma (MB) requires precise surgical resection which can be guided by tumor segmentation. We present the first attempt at automatic segmentation of MB tumors via a hierarchical transfer-learning model that (1) segments the entire tumor habitat (enhancing tumor (ET), necrosis/non-enhancing tumor (NET), edema), followed by (2) training separate models for each of the sub-compartments. Transfer learning from adult brain tumors is used to optimize segmentation of tumor sub-compartments for pediatric MB. METHODS We evaluated 300 adult glioma studies (BRATS) and 49 pediatric MB studies (2-18 years old), both consisting of Gd-T1w, T2w, FLAIR sequences. The MB cohort was collected from Children's Hospital of Los Angeles (Nf19) and Cincinnati Children’s Hospital Medical Center (Nf30). Scans were registered to age-specific pediatric atlases, followed by bias correction and skull-stripping. Ground truth for the tumor sub-compartments was generated via consensus across two experts. We employed a 3D nn-Unet segmentation model on BRATS dataset using initial learning rate of 0.01, stochastic gradient descent as optimizer, and an average of dice loss and cross-entropy loss as the loss function. A hierarchical transfer learning model with Models Genesis was then applied, which allowed for fine tuning every layer on the pediatric MB dataset, across 5-fold cross validation. Dice score was used as performance metric, such that a perfect overlap between ground truth and prediction would yield a Dice score of 1. RESULTS Our 3D hierarchical segmentation model yielded mean dice scores of 0.85±0.03 for the entire tumor habitat; 0.77±0.048 for ET, 0.73±0.09 for edema, and 0.56±0.09 for NET + necrosis segmentation, across cross-validation runs. Overall, tumor outline and segmentation matched well with the ground truth, especially for the entire tumor, ET and enhancing tumor sub-compartments. CONCLUSIONS Our segmentation approach holds promise for accurate automated delineation of the tumor sub-compartments in pediatric Medulloblastoma.

DDEL-11. DETERMINING THE DOSE OF REGADENOSON MOST LIKELY TO TRANSIENTLY ALTER THE INTEGRITY OF THE BLOOD-BRAIN BARRIER IN PATIENTS WITH GLIOMAS

Abstract BACKGROUND The blood brain barrier (BBB) is a major obstacle to the delivery of chemotherapy to the CNS. Regadenoson is an FDA approved adenosine A2 agonist used for cardiac stress tests. In murine models, it transiently increases BBB permeability to a 70KD dextran. This multi-institutional, NIH funded, Adult Brain Tumor Consortium trial was designed to discover a dose of regadenoson that substantially increases vascular permeability in normal appearing white matter (NAWM) where drug delivery is particularly challenging. METHODS Adults ages 18-45 with supratentorial gliomas at low-risk for regadenoson complications were recruited (n = 7). One patient was treated at each of seven dose levels (from 0.05 to 1.4 mg) that are known to be safe in humans. The primary outcome measure is change in vascular permeability via dynamic contrast enhanced (DCE) perfusion MRI estimates of Ktrans. The primary outcome measure was a 10-fold higher Ktrans in NAWM than reported in literature (Ktrans &amp;gt; 0.04 min-1). Contrast-enhanced T1 subtraction map estimates of change in contrast enhancement and other measurements in normal brain and non-enhancing tumor were quantified. RESULTS Ktrans measures in NAWM averaged 1.13x10-3 ± 0.44x10-3 (SEM) min-1, lower than the target of 0.04 min-1. Normalized, contrast enhanced T1-weighted MR signal intensity in NAWM increased an average of 74.0 ± 22.4% min-1 (SEM) min-1, which was significantly higher than zero (P = 0.0163). Data available from this limited sample failed to meet the target goal in Ktrans increase or change in contrast enhancing signal intensity. CONCLUSION Administration of regadenoson at seven different doses did not significantly elevate Ktrans for gadolinium in NAWM. This data suggests that single doses of regadenoson are unlikely to substantially increase the delivery of therapeutic agents in non-enhancing brain tissue. This trial design is appropriate for further human testing of other regadenoson schedules and other novel approaches aimed at transiently modifying BBB permeability.

EXTH-28. INTRATUMORAL HETEROGENEITY OF THERAPEUTIC VULNERABILITIES ACROSS ENHANCING AND NON-ENHANCING TUMOR COMPONENTS OF GLIOBLASTOMA: A PARADIGM-SHIFTING PERSONALIZED MEDICINE APPROACH

Abstract Glioblastoma (GBM) is the most aggressive adult brain tumor with no effective therapy. Despite advances on its molecular characterization, targeted therapeutics remain out of reach. This lack-of-progress is largely attributed to molecular and spatial heterogeneity and absence of clinically applicable models to guide patient care. Moreover, as standard surgical resection is aimed at contrast-enhancing (CE) tumor, the molecular landscape and treatment response of the remaining non-contrast enhancing (nCE) component is largely unknown despite its active role in tumor progression and local recurrence. Here, we aimed to identify the functional impact of tumor heterogeneity and to explore therapeutic vulnerabilities of both CE and nCE components. Four tumor regions (two CE and two nCE) from each of five GBM patients were collected under neuronavigation. The resulting tissue was subjected to generation of 3D tumor models (a brain tumor initiating-enriched cell line and a spheroid-based mcancer model) with comparable success in both CE and nCE samples. A panel of 25 brain penetrant drugs (alone and in combination) targeting GBM drivers (i.e. RTK/Ras/PI3K, RB/cell cycle, p53/apoptosis, DNA damage repair, transcription addiction) was developed and used to test for therapeutic vulnerabilities. From each tumor region, 4.5 ± 3.7 (average ± SD) different targeting strategies were identified exhibiting at least 70% growth inhibition at Cmax. Drug response heterogeneity was relatively rare, varied among cases, and was independent of tumor enhancement status. To our knowledge, this is the first study to systemically assess treatment response from both CE and nCE patient-derived GBM models. Ongoing studies include comprehensive genome and transcriptome profiling and pathway analysis from these different tumor regions. We postulate that interrogation of molecular landscapes and vulnerability profiles of both CE and nCE tumor regions, will enable us to personalize and prioritize effective targeted interventions for GBM treatment.

Performance of cone-beam computed tomography (CBCT) renal arteriography for renal tumor embolization

ObjectivesTo evaluate tumor feeders, image quality, and performance of cone-beam computed tomography (CBCT) renal arteriography for renal tumor embolization. MethodsFifty-four patients with renal tumors were included in this study. The performance of CBCT renal arteriography was classified into three groups: group A, all tumor feeders could be confirmed solely based on the CBCT maximum intensity projection (MIP); group B, all feeders were detected in CBCT MIP, but there were some possible feeders which needed to be confirmed with selective digital subtraction angiography (DSA); and group C, tumor feeders were not detected in CBCT MIP, hence, the feeder was detected based on selective DSA. Tumor size, location, and enhancement on pre-procedure CT and tumor identification, overall image quality, breathing motion and opacification of the renal collecting system on CBCT MIP were also evaluated. ResultsThere were 32 (59.2%) patients in group A, 15 (27.8%) patients in group B, and 7 (13.0%) patients in group C. Significant determining factors for performance of CBCT renal arteriography were age, tumor identification, overall image quality, and breathing motion (all p < 0.05). In six out of seven cases in group C, overall image quality deteriorated due to breathing motion (significant blurring of renal artery branches with difficulty in identifying the interlobar artery level). ConclusionIn most cases, CBCT renal arteriography was sufficient to detect tumor feeders for renal tumor embolization. However, additional selective DSA is required when the overall image quality deteriorates owing to the patient’s motion.

Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics.

This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from &lt;0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from &lt;0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR &lt; 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p &lt; 0.05). The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.

The multimodal MRI brain tumor segmentation based on AD-Net

Multimodal glioma images provide different features of tumor boundaries based on magnetic resonance imaging (MRI), where multimodal features are often challenging to extract for deep learning segmentation methods. Disturbance between features of different modes is an important factor restricting multimodal learning. To efficiently extract multimodal features, we propose an automatic weighted dilated convolutional network (AD-Net) to learn multimodal brain tumor features through channel feature separation learning. Specifically, the auto-weight dilated convolutional unit (AD unit) utilizes dual-scale convolutional feature maps to acquire channel separation features. We employ two learnable parameters to fuse dual-scale convolutional feature maps in encoding layers, and the two learnable parameters are automatically adjusted with the back propagation of the gradient. We adopt the Jensen–Shannon divergence to constrain the distribution of its feature map, which in turn regularizes the weights of the entire down-sampling. In addition, we use the training technique of deep supervision to achieve fast fitting. Our proposed method got dice scores of 0.90, 0.80, and 0.76 for the whole tumor (WT), the tumor core (TC), and the enhancing tumor (ET) on the BraTS20 dataset. The experimental results showed good performance with the AD-Net network.

Texture and CT-features in differentiation of hypervascular pancreatic neuroendocrine tumors from renal cell carcinoma metastases: diagnostic model

Objective: to develop a diagnostic model that includes CT and radiomic features for the differential diagnosis of pancreatic neuroendocrine tumors (PNETs) G1 and G2 and pancreatic renal cell carcinoma (RCC) metastases.Material and Methods. 78 patients with 79 hypervascular PNETs and 17 patients with 24 pancreatic RCC metastases who underwent pancreatic resection and histological verification were selected in the study. All the patients underwent preoperative contrast enhanced CT (CECT). We assessed tumor attenuation, composition (cystic/solid), homogeneity (homogeneous/heterogeneous), calcification and presence of the main pancreatic duct (MPD) dilation. We calculated lesion-to-parenchyma contrast (LPC), relative tumor enhancement ratio (RTE) and extracted 52 texture features for arterial phase of CECT. Qualitative and texture features were compared between PNETs and pancreatic RCC metastasis. The selection of predictors for the logistic model was carried out in 2 successive stages: 1) selection of predictors based on one-factor logistic models, the selection criterion was p &lt; 0.2; 2) selection of predictors using L2 regularization (LASSO regression after standardization of independent variables). The selected predictors were included in a logistic regression model without interactions, the coefficients of which were estimated using the maximum likelihood method with a penalty of 0.8.Results. There was no difference in composition, homogeneity (homogeneous/heterogeneous) and presence of the MPD dilation between groups. We did not find calcification in pancreatic RCC metastasis, in contrast to the PNETs (9% contained calcifications). After selection, the LCR, CONVENTIONAL_HUmin, GLCM_Correlation, NGLDM_Coarseness were included in the final diagnostic model, which showed a sensitivity and specificity of 95.8%; 62% in the prediction of pancreatic RCC metastases.Conclusion. The diagnostic model developed on the basis of texture and CT-features has high sensitivity (95.8%) with moderate specificity (62%), which allows it to be used in complex diagnostic cases to determine the patient's treatment tactics.

Enc‐Unet: A novel method for Glioma segmentation

AbstractThe diagnosis' treatment planning, follow‐up and prognostication of Gliomas is significantly enhanced on Magnetic Resonance Imaging. In the present research, deep learning‐based variant of convolutional neural network methodology is proposed for glioma segmentation where pretrained autoencoder acts as backbone to the 3D‐Unet which performs the segmentation task as well as image restoration. Further, Unet accepts input as the combination of three non‐native MR images (T2, T1CE, and FLAIR) to extract maximum and superior features for segmenting tumor regions. Further, weighted dice loss employed, focusses on segregating tumor region into three regions of interest namely whole tumor with oedema (WT), enhancing tumor (ET), and tumor core (TC). The optimizer preferred in the proposed methodology is Adam and the learning rate is initially set to , progressively reduced by a cosine decay after 50 epochs. The learning parameters are reduced to a larger extent (up to 9.8 M as compared to 27 M). The experimental results show that the proposed model achieved Dice similarity coefficients: 0.77, 0.92, and 0.84; sensitivity: 0.90, 0.95, and 0.89; specificity: 0.97, 0.99, and 0.99; Hausdorff95: 5.74, 4.89, and 6.00, in the three regions including ET, WT, TC. This proposed Glioma segmentation method is efficient for segregation of tumors.

Virtual non-contrast reconstructions improve differentiation between vascular enhancement and calcifications in stereotactic planning CT scans of cystic intracranial tumors

PurposeTo assess the diagnostic value of spectral-detector CT (SDCT) derived virtual non-contrast images (VNC) for differentiation between vascular enhancement and wall calcifications of cystic intracranial tumors in contrast-enhanced stereotactic planning examinations. Method48 patients with cystic intracranial tumors who underwent stereotactic SDCT examinations between 02/2017 and 02/2020 were retrospectively included. In each patient, two separate hyperattenuating structures along the cyst wall were defined as either enhancement or calcification, respectively, using reference MRI examinations. Quantitative analysis was performed ROI-based in conventional images (CI) and VNC. In the subjective analysis, two radiologists diagnosed the predefined peri-cystic structures in binary decisions as either enhancement or calcification using CI and the combination of CI and VNC, and rated diagnostic confidence, image noise and removal of iodine in VNC. Moreover, a potential diagnostic benefit of VNC was indicated. ResultsAttenuation in CI was higher as compared to VNC across all assessed ROI (all p < 0.01). In VNC, CNR between calcification and white matter was significantly higher as compared to CNR between vascular enhancement and white matter (2.6 vs 1.3, p < 0.01), while there was no significant difference in CI. In the qualitative assessment, diagnostic accuracy was significantly higher using both VNC and CI compared to using CI alone. Raters reported less image noise in VNC as compared to CI. An additional diagnostic benefit of VNC was indicated in 84.4 % of all cases. ConclusionsSDCT-derived VNC images facilitate differentiation between peri-cystic contrast enhancement in blood vessels and calcifications in stereotactic planning scans of cystic intracranial tumors.

Prolonged Circulating Lipid Nanoparticles Enabled by High-Density Gd-DTPA-Bis(stearylamide) for Long-Lasting Enhanced Tumor Magnetic Resonance Imaging.

Porphysomes (PS) were explored to incorporate different types of diethylenetriaminepentaacetic-acid-gadolinium-(III) (Gd-DTPA)-lipids into their bilayer membrane to assess PS potential as an MRI contrast agent. The Gd-dPS-BSA by integration of over 30% Gd-DTPA-bis(stearylamide) (Gd-DTPA-BSA)-lipids in PS construction resulted in exceptional serum stability and T1 and T2 relaxivity measurements of 13 mM-1 s-1 and 19 mM-1 s-1, respectively. The Gd-dPS-BSA demonstrated significantly enhanced retention in blood circulation with a half-life of 13.6 h and high tumor accumulation up to 19.5%ID/g at 72 h post-injection in select cancer mouse models. Additionally, Gd-dPS-BSA displayed excellent MRI tumor enhancement over 24, 48, and 72 h with contrast enhancements from the baseline of 35.8%, 38.2%, and 38.3%, respectively. Results reported here highlight a high-density incorporation of Gd-DTPA-BSA-lipids within PS, and other liposome formulations can enhance circulatory longevity, independently of particles' concentration, suggesting effective MRI contrast agent potential for Gd-dPS-BSA and potential utility of Gd-DTPA-BSA-lipids to enhance other liposomal-influenced diagnostic and therapeutic functions.

Qualitative and quantitative parameters on hepatobiliary phase of gadoxetic acid-enhanced MR imaging for predicting pathological response to preoperative systemic therapy in colorectal liver metastases

PurposeTo explore the value of parameters of the on hepatobiliary phase (HBP) of pre-treatment gadoxetic acid-enhanced magnetic resonance imaging (MRI) for predicting pathological response to systemic therapy in colorectal liver metastases (CRLMs), compared with response evaluation criteria in solid tumors, version 1.1 (RECIST 1.1). MethodsA total of 96 patients with CRLMs who underwent gadoxetic acid-enhanced MRI prior to treatment and then liver resection from January 2017 to December 2021 were enrolled. The pathological response was assessed by the percentage of residual tumors (RTs), and CRLMs were classified into two groups according to the pathological response grade (PRG): (1) strong response (including PRG2 and PRG3, RTs ≤ 10%), and weak response (PRG1, RTs > 10%). Two radiologists evaluated the enhancement pattern and degree of CRLMs on the HBP. The diameter, mean and standard deviation (SD) value of signal intensity (SI) of CRLMs on pre-contrast and HBP images were recorded. Relative tumor enhancement (RTE) and the SD ratio (SDR) were calculated. These parameters were analyzed in terms of pathological response on a lesion-by-lesion basis. ResultsTotally, 263 CRLMs were classified into: the strong response group (PRG2, n = 57; PRG3, n = 7) and the weak response group (PRG1, n = 199). RTE and SDR values were significantly higher in the strong response group than in the weak response group (P < 0.001). RTE values (P < 0.001) and SDR values (P = 0.031) were independent factors for predicting strong response. The area under curve (AUC) of RTE and SDR values were 0.725 and 0.652, respectively. The combination of these parameters was 0.750, which performed better than RECIST 1.1 (0.750 vs 0.531; P < 0.001). ConclusionsRTE and SDR values on HBP are potential features in predicting pathological response to systemic therapy in CRLMs.

RETRACTED: MiR-211 plays a dual role in cancer development: From tumor suppressor to tumor enhancer

Cancer is a general term for more than 100 unique malignancies in different organs of the body. Each cancer type and subtype has its own unique genetic, epigenetic, and cellular factors accountable for malignant progression and metastasis. Small non-coding RNAs called miRNAs target mRNAs and play a vital part in the pathogenesis of human diseases, specifically cancer. Recent investigations provided knowledge of the deregulation of miR-211 in various cancer types and disclosed that miR-211 has an oncogenic or tumor-suppressive impact on tumourigenesis and cancer development. Moreover, recent discoveries which clarify the essential functions of miR-211 might provide proof for its prognosis, diagnostic and therapeutic impact on cancer. Thereby, this review will discuss recent findings regarding miR-211 expression level, target genes, and mechanisms in different cancers. In addition, the most recent results that propose miR-211 usefulness as a noninvasive biomarker and therapeutic factor for the diagnosis and treatment of cancer will be explained.

ORM 1 as a biomarker of increased vascular invasion and decreased sorafenib sensitivity in hepatocellular carcinoma.

This study aimed to clarify the role of Orosomucoid 1 (ORM1) in the development and therapy resistance in hepatocellular carcinoma (HCC). The mRNA expression level of ORM1 was analyzed via integrative analysis of Gene Express Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. The protein expression level of ORM1 in our cohort was determined using immunohistochemistry. Correlation analysis was used to investigate the relationship between ORM1 expression and clinical parameters. The Cell Counting Kit-8 assay was used to clarify the role of ORM1 in HCC malignant behaviors, including cell growth and sorafenib sensitivity, in vitro. The results indicated that ORM1 was significantly downregulated in the hepatic cancer cells compared to that in the non-cancerous cells. However, it was upregulated in microvascular invasion samples, especially in the cancer embolus compared to that in the surrounding tumor cells. Though Kaplan-Meier analysis did not show an association of ORM1 expression with the overall survival rates of HCC patients, univariate analysis indicated that ORM1 expression was highly correlated with tumor grade and stage. An in vitro assay also revealed that downregulation of ORM1 led to the suppression of tumor growth and enhancement of sorafenib sensitivity without epithelial-to-mesenchymal transition (EMT) alteration, which was consistent with our bioinformatic analysis. Hence, ORM1 played a key role in HCC tumorigenesis and may serve as a potential target for the development of therapeutics against HCC in the future.

Evaluation of a Sumoylation Inhibitor as an Intrinsic and Extrinsic Radiosensitizer

<h3>Purpose/Objective(s)</h3> Radiation therapy (RT) reduces tumor burden through both intrinsic initiation of tumor cell death and enhancement of anti-tumor immunity. However, radiation treatments are not universally efficacious due to variation in radiosensitivity and anti-tumor immunity. Low proliferative rate, hypoxia, and T cell effector dysfunction can significantly degrade response to RT. Here we evaluate a rational combination therapy pairing radiosensitization and immune activation in conjunction with standard of care RT. Recent studies demonstrated the sumoylation inhibitor, TAK-981, has intrinsic capacity to reduce the proliferation of tumor cells <i>in vitro</i> as a monotherapy and decrease tumor volumes in syngeneic murine colon carcinoma models (CT26 and MC38) in combination with PD-1 or CTLA-4 checkpoint blockade. In these models a dendritic cell dependent type I interferon pathway was implicated to mediate the anti-tumor immune response and enhanced in vivo tumor control in TAK-981 and checkpoint blockade combination therapy, similar to previously elucidated mechanisms of immune activation following hypofractionated radiotherapy. Additionally, multiple intrinsic and extrinsic protein mediators (Myc, IRF3, p21) of tumor cell response to radiation are regulated by the post-translational modification of sumoylation. Consequently, we evaluated whether combining RT with TAK-981 resulted in enhanced tumor radiosensitivity in vitro and improved local tumor control in vivo, consistent with enhanced anti-tumor immune response in murine tumor models. <h3>Materials/Methods</h3> We utilized a longitudinal real time microscopic proliferation assay to evaluate RT response. Titrations of TAK-981 were added to established in vitro tumor cell cultures 4 hours prior to increasing doses of single fraction photon RT. Impacts of RT, TAK-981 or the combination treatment were assessed based upon cell proliferation for 72 hours. We assessed efficacy of this novel combination in vivo using the MC38 colorectal carcinoma model. C57BL6 mice with established tumors (> 50mm2) received TAK981 (15mg/kg) alone three times weekly or in combination with single fraction 8 Gy RT and compared to RT alone to evaluate local tumor control and impacts on overall survival. <h3>Results</h3> Our data demonstrates a synergistic and dose dependent reduction in MC38 colon carcinoma in vitro proliferation in response to combination RT and TAK-981. In vivo MC38 tumor models demonstrate no effect of TAK-981 alone compared to vehicle treated mice while co-administration with a single 8 Gy fraction of radiotherapy synergistically enhanced tumor control and overall survival compared to RT alone. <h3>Conclusion</h3> Our preliminary data support potential dual synergistic roles for the sumoylation inhibitor TAK-981 with radiotherapy, functioning as both an intrinsic radiosensitizer and immunomodulator.