Tumor Enhancement Research Articles

Multimodal brain tumor image segmentation based on DenseNet.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.

Diagnostic CT of colorectal cancer with artificial intelligence iterative reconstruction: A clinical evaluation

ObjectivesTo investigate the clinical value of a novel deep-learning based CT reconstruction algorithm, artificial intelligence iterative reconstruction (AIIR), in diagnostic imaging of colorectal cancer (CRC). MethodsThis study retrospectively enrolled 217 patients with pathologically confirmed CRC. CT images were reconstructed with the AIIR algorithm and compared with those originally obtained with hybrid iterative reconstruction (HIR). Objective image quality was evaluated in terms of the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Subjective image quality was graded on the conspicuity of tumor margin and enhancement pattern as well as the certainty in diagnosing organ invasion and regional lymphadenopathy. In patients with surgical pathology (n = 116), the performance of diagnosing visceral peritoneum invasion was characterized using receiver operating characteristic (ROC) analysis. Changes of diagnostic thinking in diagnosing hepatic metastases were assessed through lesion classification confidence. ResultsThe SNRs and CNRs on AIIR images were significantly higher than those on HIR images (all p < 0.001). The AIIR was scored higher for all subjective metrics (all p < 0.001) except for the certainty of diagnosing regional lymphadenopathy (p = 0.467). In diagnosing visceral peritoneum invasion, higher area under curve (AUC) of the ROC was found for AIIR than HIR (0.87 vs 0.77, p = 0.001). In assessing hepatic metastases, AIIR was found capable of correcting the misdiagnosis and improving the diagnostic confidence provided by HIR (p = 0.01). ConclusionsCompared to HIR, AIIR offers better image quality, improves the diagnostic performance regarding CRC, and thus has the potential for application in routine abdominal CT.

The Effect of Dose Enhancement in Tumor With Silver Nanoparticles on Surrounding Healthy Tissues: A Monte Carlo Study.

Objectives: Cancer-related death rates account for approximately one-third of all deaths, and this rate is increasing remarkably every year. In this study, we examined the dose enhancement factor (DEF) in the tumor and surrounding tissues by adding different concentrations of silver nanoparticles (AgNPs) to the brain tumor using the Monte Carlo (MC) technique. Methods: This study used MCNP6.2 simulation software. A Planning Target Volume (PTV) of 1 × 1 × 1 cm3 was placed in the center of a cubic cranial model with dimensions of 5 × 5 × 5 cm3. Five different simulations were initially generated using the simple method. These simulations included pure PTV and PTV consisting of 4 different silver concentrations (5, 10, 20, and 30 mg/g). Additionally, a model was created using the nanolattice method, considering the size, position, and distribution of the AgNPs. Irradiation was performed using a source with a 6 MV linac photon spectrum. Measurements were performed using the *f8 tally, and DEF values were calculated. Results: In the simulation study using the simple method, the DEF value of PTV increased linearly with concentration, whereas the DEF values were lower than the simulation results with the nanolattice model (1.9 vs 1.4 for 30 mg/g NP concentration). Performing the simple method, we observed no remarkable dose increase in lateral OARs surrounding PTV. While a remarkable dose decrease was observed in distal OARs, a dose increase in the proximal OAR was observed, which was consistent with that of PTV. However, according to the results obtained by performing the nanolattice method, the dose increase was observed in both the proximal OAR and the distal OAR and was similar to that of PTV. Conclusion: While enhancing the dose in the tumor by adding NPs into the tumor, it is essential to consider whether it also increases the OAR dose. In addition, simulation studies on NPs showed that the dose increase varied significantly with particle size, position, and distribution. Hence, these factors should be considered carefully.

Augmented Transformer network for MRI brain tumor segmentation

The Augmented Transformer U-Net (AugTransU-Net) is proposed to address limitations in existing transformer-related U-Net models for brain tumor segmentation. While previous models effectively capture long-range dependencies and global context, these works ignore the hierarchy to a certain degree and need more feature diversity as depth increases. The proposed AugTransU-Net integrates two advanced transformer modules into different positions within a U-shaped architecture to overcome these issues. The fundamental innovation lies in constructing improved augmentation transformer modules that incorporate Augmented Shortcuts into standard transformer blocks. These augmented modules are strategically placed at the bottleneck of the segmentation network, forming multi-head self-attention blocks and circulant projections, aiming to maintain feature diversity and enhance feature interaction and diversity. Furthermore, paired attention modules operate from low to high layers throughout the network, establishing long-range relationships in both spatial and channel dimensions. This allows each layer to comprehend the overall brain tumor structure and capture semantic information at critical locations. Experimental results demonstrate the effectiveness and competitiveness of AugTransU-Net in comparison to representative works. The model achieves Dice values of 89.7%/89.8%, 78.2%/78.6%, and 80.4%/81.9% for whole tumor (WT), enhancing tumor (ET) and tumor core (TC) segmentation on the BraTS2019-2020 validation datasets, respectively. The code for AugTransU-Net will be made publicly available at https://github.com/MuqinZ/AugTransUnet.

Clinical value of magnetic resonance spectroscopy in assessment of early curing impact of concurrent chemoradiotherapy after high-grade glioma surgery

Background/Aim. High-grade glioma (HGG) is an interstitial cell-derived primary tumor of the nervous system. The current guidelines for the diagnosis and treatment of glioma recommend the maximum safe range of tumor resection for treatment methods. Adjuvant concurrent chemoradiotherapy is recommended after surgery, followed by six cycles of single-drug chemotherapy, temozolomide. Evaluation of the early efficacy of concurrent chemoradiotherapy after HGG surgery, especially for patients with a high risk of recurrence, is a crucial step in enhancing the treatment efficiency for patients diagnosed with HGG. In this study, we investigated the clinical utility of magnetic resonance (MR) spectroscopy (MRS) in assessing the early curing impact of concurrent chemoradiotherapy following HGG surgery. Methods. A total of 50 patients with incomplete resection or suspected residual postoperative HGG, treated in the radiotherapy department of our hospital between January 2016 and June 2021, were selected for routine concurrent chemoradiotherapy. Conventional MR imaging and MRS were performed one week prior to treatment and one month after treatment to assess changes in specific brain metabolites. All 50 patients were followed up for 6 to 12 months. Based on the follow-up results, the patients were divided into two groups: the tumor recurrence group and the tumor suppression group. One month after the end of the treatment, the differences in levels of brain metabolites between the two groups were analyzed using MRS. Results. The levels of N-acetylaspartate (NAA) and creatine (Cr) increased after radiotherapy, while choline (Cho) peak value, and Cho/Cr, NAA/Cr, and Cho/NAA ratios decreased compared to pre-treatment levels. There were statistically significant differences in the NAA peak value, and Cho/Cr, and Cho/NAA ratios in the tumor enhancement area before and after treatment (p &lt; 0.05). There were also statistically significant differences in Cho/Cr ratio in the peritumoral edema area before and after treatment (p &lt; 0.05). Conclusion. After concurrent chemoradiotherapy, MRS can be used to detect early metabolic changes in the tumor enhancement and peritumoral edema areas of HGG.

Score based on contrast-enhanced ultrasound predict central lymph node metastasis in papillary thyroid cancer.

To investigate the association between contrast-enhanced ultrasound (CEUS) features of PTC and central lymph node metastasis (CLNM) and to develop a predictive model for the preoperative identification of CLNM. This retrospective study evaluated 750 consecutive patients with PTC from August 2020 to April 2023. Conventional ultrasound and qualitative CEUS features were analyzed for the PTC with or without CLNM using univariate and multivariate logistic regression analysis. A nomogram integrating the predictors was constructed to identify CLNM in PTC. The predictive nomogram was validated using a validation cohort. A total of 684 patients were enrolled. The 495 patients in training cohort were divided into two groups according to whether they had CLNM (pCLNM, n= 191) or not (nCLNM, n= 304). There were significant differences in terms of tumor size, shape, echogenic foci, enhancement direction, peak intensity, and score based on CEUS TI-RADS between the two groups. Independent predictive US features included irregular shape, larger tumor size (≥ 1.0cm), and score. Nomogram integrating these predictive features showed good discrimination and calibration in both training and validation cohort with an AUC of 0.72 (95% CI: 0.68, 0.77) and 0.79 (95% CI: 0.72, 0.85), respectively. In the subgroup with larger tumor size, age ≤ 35 years, irregular shape, and score > 6 were independent risk factors for CLNM. The score based on preoperative CEUS features of PTC may help to identify CLNM. The nomogram developed in this study provides a convenient and effective tool for clinicians to determine an optimal treatment regimen for patients with PTC.

Suggestion of Follow-Up Period in Nonfunctioning Pituitary Incidentaloma Based on MRI Characteristics.

For patients diagnosed with asymptomatic, non-functional pituitary incidentaloma (PI), periodic follow-up is generally proposed. However, the recommended follow-up period differs among existing guidelines and consensus is lacking. Thus, this study aimed to suggest follow-up periods for PI based on MRI characteristics. Between 2007 and 2023, 245 patients who were diagnosed with PI were retrospectively assessed. Their mean clinical and neuroradiological follow-up periods were 74.2 and 27.3 months, respectively. Their baseline clinical and neuroradiological characteristics were analyzed. These 245 patients were divided into two groups: those with PI size progression and those without PI size progression. Additionally, neuroradiological features of each group were analyzed according to presumptive diagnoses of PI. PI size increased in 33 of 245 patients. For the remaining 212 patients, PI size decreased or stayed unchanged. Of the 33 patients with PI size progression, ten underwent surgery. Stalk deviation (p<0.001) and lesion enhancement (p=0.001) were significantly more observed in those with PI size progression than in those without PI size progression. MRI morphological factors were not related to changes in PI size in the presumptive Rathke's cleft cyst group. In the presumptive pituitary adenoma group, absence of tumor enhancement (p<0.001) and stalk deviation (p<0.001) were significantly associated with tumor reduction and progression, respectively. Our findings support an additional guideline for patients with asymptomatic non-functional PI without stalk deviation and enhancement. For these patients, the clinical and neuroradiological follow-up periods could be reduced.

T2-Weighted Imaging and Apparent Diffusion Coefficient Histogram Parameters Predict Meningioma Consistency

Preoperative prediction of meningioma consistency is of great clinical value for risk stratification and surgical approach selection. However, to date, objective quantitative criteria for predicting meningioma consistency have not been developed. This study aimed to investigate the predictive value of magnetic resonance imaging (MRI) T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) histogram parameters for meningioma consistency. We retrospectively analyzed the clinical, preoperative MRI, and pathological data of 103 patients with histopathologically confirmed meningiomas. Histogram parameters (mean, variance, skewness, kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%) were calculated automatically on the whole tumor using MaZda software. Chi-square test, Mann-Whitney's U test, or independent samples t-test was used to compare clinical, conventional MRI features, and histogram parameters between soft and hard meningiomas. Receiver operating characteristic curve and binary logistic regression analysis were employed to assess the predictive performance of T2WI and ADC histogram parameters. Tumor enhancement was the only conventional MRI feature that was statistically different between soft and hard meningiomas. ADCmean, ADCp1, ADCp10, and ADCp50 among ADC histogram parameters, and T2mean, T2p1, T2p10, T2p50, T2p90, and T2p99 among T2WI histogram parameters showed statistically significant differences between soft and hard meningiomas (all P<0.05). We found that all combined variables (combinedall) had the best accuracy in predicting meningioma consistency, with area under the curve, sensitivity, specificity, accuracy, positive predictive, and negative predictive values of 0.873 (0.804-0.941), 88.89%, 67.50%, 80.58%, 81.20%, and 79.40%, respectively. Among them, combinedT2 is the most beneficial for predicting meningioma consistency. CombinedT2 demonstrated better predictive performance for meningioma consistency than combinedADC. T2WI and ADC histogram parameters may be imaging markers for predicting meningioma consistency.

Treatment evaluation by volumetric segmentation in pediatric optic pathway glioma: evaluation of the effect of bevacizumab on intra-tumor components.

Progressive pediatric optic pathway gliomas (OPGs) are treated by diverse systemic antitumor modalities. Refined insights on the course of intra-tumoral components are limited. We performed an exploratory study on the longitudinal volumetric course of different (intra-)tumor components by manual segmentation of MRI at the start and after 3, 6 and 12months of bevacizumab (BVZ) treatment. Thirty-one patients were treated with BVZ (median 12months, range: 2-39months). During treatment the total tumor volume decreased with median 19.9% (range: -62.3 to + 29.7%; n = 30) within the first 3 months, decreased 19.0% (range: -68.8 to + 96.1%; n = 28) between start and 6 months and 27.2% (range: -73.4 to + 36.0%; n = 21) between start and 12months. Intra-tumoral cysts were present in 12 OPGs, all showed a decrease of volume during treatment. The relative contrast enhanced volume of NF1 associated OPG (n = 11) showed an significant reduction compared to OPG with a KIAA1549-BRAF fusion (p < 0.01). Three OPGs progressed during treatment, but were not preceded by an increase of relative contrast enhancement. Treatment with BVZ of progressive pediatric OPGs leads to a decrease of both total tumor volume and cystic volume for the majority of OPGs with emphasis on the first three months. NF1 and KIAA1549-BRAF fusion related OPGs showed a different (early) treatment effect regarding the tumor enhancing component on MRI, which did not correlate with tumor volume changes. Future research is necessary to further evaluate these findings and its relevance to clinical outcome parameters.

A jack of all trades - ADAM8 as a signaling hub in inflammation and cancer.

As a member of the family of A Disintegrin And Metalloproteinases (ADAM) ADAM8 is preferentially expressed in lymphatic organs, immune cells, and tumor cells. The substrate spectrum for ADAM8 proteolytic activity is not exclusive but is related to effectors of inflammation and signaling in the tumor microenvironment. In addition, complexes of ADAM8 with extracellular binding partners such as integrin β-1 cause an extensive intracellular signaling in tumor cells, thereby activating kinase pathways with STAT3, ERK1/2, and Akt signaling, which causes increased cell survival and enhanced motility. The cytoplasmic domain of ADAM8 harbors five SRC homology-3 (SH3) domains that can potentially interact with several proteins involved in actin dynamics and cell motility, including Myosin 1F (MYO1F), which is essential for neutrophil motility. The concept of ADAM8 thus involves immune cell recruitment, in most cases leading to an enhancement of inflammatory (asthma, COPD) and tumor (including pancreatic and breast cancers) pathologies. In this review, we report on available studies that qualify ADAM8 as a therapeutic target in different pathologies. As a signaling hub, ADAM8 controls extracellular, intracellular, and intercellular communication, the latter one mainly mediated by the release of extracellular vesicles with ADAM8 as cargo. Here, we will dissect the contribution of different domains to these distinct ways of communication in several pathologies. We conclude that therapeutic targeting attempts for ADAM8 should consider blocking more than a single domain and that this requires a thorough evaluation of potent molecules targeting ADAM8 in an in vivo setting.

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI.

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

NnUnetFormer: an automatic method based on nnUnet and transformer for brain tumor segmentation with multimodal MR images

Objective. Both local and global context information is crucial semantic features for brain tumor segmentation, while almost all the CNN-based methods cannot learn global spatial dependencies very well due to the limitation of convolution operations. The purpose of this paper is to build a new framework to make full use of local and global features from multimodal MR images for improving the performance of brain tumor segmentation. Approach. A new automated segmentation method named nnUnetFormer was proposed based on nnUnet and transformer. It fused transformer modules into the deeper layers of the nnUnet framework to efficiently obtain both local and global features of lesion regions from multimodal MR images. Main results. We evaluated our method on BraTS 2021 dataset by 5-fold cross-validation and achieved excellent performance with Dice similarity coefficient (DSC) 0.936, 0.921 and 0.872, and 95th percentile of Hausdorff distance (HD95) 3.96, 4.57 and 10.45 for the regions of whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively, which outperformed recent state-of-the-art methods in terms of both average DSC and average HD95. Besides, ablation experiments showed that fusing transformer into our modified nnUnet framework improves the performance of brain tumor segmentation, especially for the TC region. Moreover, for validating the generalization capacity of our method, we further conducted experiments on FeTS 2021 dataset and achieved satisfactory segmentation performance on 11 unseen institutions with DSC 0.912, 0.872 and 0.759, and HD95 6.16, 8.81 and 38.50 for the regions of WT, TC, and ET, respectively. Significance. Extensive qualitative and quantitative experimental results demonstrated that the proposed method has competitive performance against the state-of-the-art methods, indicating its interest for clinical applications.

Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.

Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume (p = 0.012), edema volume (p = 0.004), enhancement (p = 0.001), margin (p < 0.001), and tumor-brain interface (p < 0.001). Five radiomics features were selected for model development. The prediction model for radiomics features demonstrated an average validation accuracy of 0.74, while the model for clinical imaging features showed an average validation accuracy of 0.69. When applied to external test data (Hospital 2), the radiomics model achieved an area under the receiver operating characteristics curve (AUC) of 0.72 and accuracy of 0.69, while the clinical imaging model achieved an AUC of 0.82 and accuracy of 0.81. An improved performance was obtained from the model constructed by combining radiomics and clinical imaging features. In the combined model, the AUC and accuracy for meningioma grading were 0.86 and 0.73, respectively. In conclusion, this study demonstrates the potential value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas. The combination of both radiomics and clinical imaging features achieved the highest AUC among the models. Therefore, the combined model of radiomics and clinical imaging features may offer a more effective tool for predicting clinical outcomes in meningioma patients.

Modular Self-Assembling Dendrimer Nanosystems for Magnetic Resonance and Multimodality Imaging of Tumors.

Bioimaging is a powerful tool for diagnosing tumors but remains limited in terms of sensitivity and specificity. Nanotechnology-based imaging probes able to accommodate abundant imaging units with different imaging modalities are particularly promising for overcoming these limitations. In addition, the nanosized imaging agents can specifically increase the contrast of tumors by exploiting the enhanced permeability and retention effect. A proof-of-concept study is performed on pancreatic cancer to demonstrate the use of modular amphiphilic dendrimer-based nanoprobes for magnetic resonance (MR) imaging (MRI) or MR/near-infrared fluorescence (NIRF) multimodality imaging. Specifically, the self-assembly of an amphiphilic dendrimer bearing multiple Gd3+ units at its terminals, generates a nanomicellar agent exhibiting favorable relaxivity for MRI with a good safety profile. MRI reveals an up to two-fold higher contrast enhancement in tumors than in normal muscle. Encapsulating the NIRF dye within the core of the nanoprobe yields an MR/NIRF bimodal imaging agent for tumor detection that is efficient both for MRI, at Gd3+ concentrations 1/10 the standard clinical dose, and for NIRF imaging, allowing over two-fold stronger fluorescence intensities. These self-assembling dendrimer nanosystems thus constitute effective probes for MRI and MR/NIRF multimodality imaging, offering a promising nanotechnology platform for elaborating multimodality imaging probes in biomedical applications.

Liver Tumor Enhancement at Hybrid Angio-CT and Comparison With Tumor and Hepatic Parenchymal Distribution of Yttrium-90 Microspheres by Positron Emission Tomography.

This single-center retrospective study evaluated patients who underwent treatment of a primary or secondary hepatic malignancy with injection of glass or resin yttrium-90 (90Y) microspheres with a corresponding hybrid angiography-computed tomography (angio-CT) and 90Y positron emission tomography (PET). Volumetric contours were defined by three independent observers and were used to calculate relative tumoral enhancement at angio-CT. This parameter was compared with the tumor-to-normal (T/N) activity ratio predicted by technetium-99m macro-aggregated albumin (99mTc-MAA) single photon emission computed tomography (SPECT) and microsphere activity distribution by 90Y PET. A similar correlation was observed for the enhancement ratio at angio-CT with observed microspheredistribution at 90Y PET (r=0.34) to that predicted by 99mTc-MAA SPECT (r=0.32). The enhancement ratio on angio-CT performed as well as 99mTc-MAA in the prediction of 90Y PET activity distribution. The technique could not be readily applied to tumors with large areas of hypoattenuation (necrosis) on angio-CT.With refinement and further study, this technique could be used as a quantitative adjunct to standard-of-care99mTc-MAA SPECT for dosimetry calculations and prediction of microsphere distribution to maximize tumor response and minimize hepatotoxicity.

Inhibition of EZH2 Improves CART19 Immunotherapy By Reprogramming Lymphoma Tumor Cells and Enhancing T-Cell Functionality

Introduction: Anti-CD19 chimeric antigen receptor T cell (CART19) immunotherapy provides a high response rate in diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) patients, but only ~30-40% of patients maintain a durable remission. The factors contributing to lymphoma resistance to CART19 are numerous, multifaceted, and coincidental, such as tumor intrinsic properties, CART dysfunction, the immunosuppressive tumor microenvironment (TME), and host factors. However, most strategies currently in development to increase CART efficacy only address one resistance mechanism at a time; therefore, it is unlikely that these approaches will be consistently effective in a group of heterogeneous patients. In this study, we sought to develop a strategy to overcome multiple resistance mechanisms simultaneously. Epigenetic modulation offers a promising approach to this goal, as it can reshape the tumor landscape, as well as immune cells. DLBCL/FL cells depend for their survival on the methyltransferase EZH2, which represses gene expression by generating histone 3 lysine 27 trimethylation. Interestingly, somatic gain-of-function mutations of EZH2 are frequently found in DLBCL/FL and drive lymphomagenesis and impair tumor B-cell to T-cell interactions, leading to an immune evasive phenotype. Therefore, we hypothesized that EZH2 inhibition could enhance CART anti-tumor effect, remodeling the entire tumor ecosystem by: a. inhibiting lymphoma growth; b. increasing lymphoma immunogenicity; and c. enhancing T cell function ( Fig. 1A). Methods and Results: Through short-term killing assays, we compared CART19 alone to CART19 combined with the EZH2 inhibitor tazemetostat (taz), and demonstrated that taz enhances CART19 tumor killing of DLBCL cell lines [OCI-Ly18 and Toledo (with wild-type EZH2) and SU-DHL-4 (with mutant EZH2)]. Interestingly, the highest effect was observed when tumor cells were pre-incubated with taz and taz was kept in the coculture with CART19. This finding underscores the importance of tumor priming to improve CART19 recognition and activation. Since EZH2 is also expressed by T cells, we tested if EZH2 inhibition impacted CART cell function. Co-treatment of lymphoma cell lines with taz and CART19 was associated with higher cytokine secretion over time (INFy and TNF) by CART cells and CART cell expansion. Most importantly, using the sub-cutaneous OCI-Ly18 xenograft lymphoma model in immunodeficient NSG mice, we demonstrated that taz administration improved tumor control ( Fig. 1B) and CART19 expansion and infiltration in vivo. Mechanistically, we studied the molecular changes in lymphoma cells (OCI-Ly18 and SU-DHL-4) induced by exposure to taz/CART19 or CART19 alone by performing RNA sequencing. We observed upregulation of genes involved in inflammatory response, B-cell activation/differentiation (e.g., BATCH2), adhesion (OX40L, CD80), and motility (CAECAM1, NRCAM1, EPHB2, CXCL16, CXCL10), sustaining the hypothesis that EZH2 inhibition changed lymphoma immunogenicity and the tumor:CART cell interaction. Indeed, functionally, we observed that taz exposure increased tumor:CART cell interaction in vitro as demonstrated by increased avidity (zMovi assay). Further, single-cell RNA sequencing performed on circulating CART19 cells from taz-treated-mice revealed enrichment in naïve and less exhausted CART cells. Lastly, we assessed whether increased activation of EZH2 in tumor cells reduces responses to CART19 in the clinic. We retrospectively analyzed a cohort of large B-cell lymphoma patients treated with commercial CART19 (tisagenlecleucel and axicabtagene ciloleucel) at the University of Pennsylvania between January 2018 and March 2023 for response (Lugano criteria) according to their EZH2 mutational status. Patients harboring EZH2 activating mutations (14/69) had a reduced complete response rate to CART19 as compared to wild-type patients (15.4% vs. 40.0%). Conclusions: Our data demonstrate that modulation of EZH2 enhances CART19 immunotherapy in preclinical models of lymphoma. EZH2 inhibition rewires lymphoma immunogenicity, making tumor cells more susceptible to CART19 killing and reducing CART19 terminal differentiation. By understanding and modulating the interplay between tumor and CART cells, we can unlock the full potential of CART19 immunotherapy in DLBCL/FL patients, aiming at long-lasting treatment outcomes.

Magnetic resonance imaging analysis of embryonal rhabdomyosarcoma of the prostate.

Rhabdomyosarcoma (RMS) is a highly malignant tumor that originates from myogenic progenitor cells. To investigate the magnetic resonance imaging (MRI) characteristics of prostate embryonal rhabdomyosarcoma (ERMS). We retrospectively analyzed the clinical and MRI imaging data of 9 cases of prostate ERMS that were confirmed pathologically. The patients were aged between 14∼49 years with a median age of 27 years, and they all underwent MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). The MRI scan of the lesions showed an irregular shape, mixed signals, uneven equal/long T1 signal and an equal/long T2 signal, cystic necrosis in 9 cases and hemorrhage in 6 cases; DWI and ADC images showed a mixed high/low signal, and the tumor parenchyma showed ADC low signal, with an average ADC value of 0.666 × 10-3 mm2/s. There were 5 cases of DCE-MRI TIC type II and 4 cases of DCE-MRI TIC type I. The average value of Tpeak was 120 s and the average value of MCER was 172.3%. After the enhancement, the signal of tumor enhancement was uneven, and showed patchy and reticular enhancement, however, the cyst degeneration, necrosis area, and hemorrhage focus were not enhanced. There were 3 cases with multiple pelvic lymph nodes and 1 case with multiple bone metastases. The MRI manifestations of prostate ERMS have certain characteristics, and the combination of DWI and DCE-MRI are helpful in the diagnosis.

NIMG-87. PEDIATRIC BRAIN TUMOR SEGMENTATION INITIATIVE AT CHILDREN’S BRAIN TUMOR NETWORK (CBTN); PROJECT UPDATE ON IMPROVED PERFORMANCE AND FUTURE PROSPECTS

Abstract Pediatric brain tumors (PBT) exhibit significant heterogeneity, and accurate assessment of tumor response is crucial for patient management. While bidimensional measurements of tumor size are commonly used, they may underestimate tumor dimensions. Our group previously developed a multi-institutional, and multi-histology model on the Children’s Brain Tumor Network (CBTN) imaging dataset to segment tumor subregions, recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. In the most recent update, we utilized a self-configuring deep learning architecture called nnU-Net on a large dataset of well-annotated PBTs (233 training, and 60 withheld internal and 46 external test sets). The model was trained and validated on multiparametric MRI sequences and differentiated various tumor subregions, including enhancing tumor (ET), nonenhancing tumor (NET), edema (ED), and cystic component (CC) and the whole tumor (WT) regions. Clinical validity was assessed by comparing the volumes of tumor subregions predicted by the model with expert manual segmentations, demonstrating strong agreement, with statistically significant Pearson’s correlation coefficients of 0.93/0.69 for ET, 0.94/0.93 for NET, 0.78/0.93 for CC, and 0.94/0.50 for ED subregions (p&amp;lt; 0.001) for the internal/external test cohorts. The trained model showed excellent performance for the withheld internal test set and decent accuracy for the external test set. Median Dice scores for internal/external test sets were 0.94±0.10/0.90±0.07 for WT, 0.85±0.33/0.84±0.30 for ET, 0.80±0.32/0.64±0.31 for NET, 0.79±0.37/0.67±0.33 for CC, 0.70 ±0.42/0.37±0.43 for ED, and 0.86±0.19/0.80±0.21 for all nonenhancing components (combination of NET, CC, and ED), respectively. The proposed automated segmentation method provides accurate and reproducible volumetric measurements of RAPNO-defined subregions in PBTs. It exhibits robust performance and potential generalization to external datasets and the accuracy is higher compared to the previous DeepMedic model. We will continue to improve the model by constantly adding new training data to further improve the performance and sustain model robustness.

EXTH-13. ENHANCING INDUCED NEURAL STEM CELL-BASED TRAIL THERAPY FOR GLIOBLASTOMA THROUGH COMBINATION WITH THE NOVEL SENSITIZING AGENT TR-107

Abstract Tumor-homing neural stem cell therapy (tNSC) is emerging as a promising treatment for aggressive cancers of the brain. Despite their success, developing tNSC therapies that maintain durable tumor suppression remains a challenge. Here, we report a new synergistic combination regimen where the novel agent TR107 augments induced NSC-TRAIL therapy (hiNeuroS-TRAIL) in models of the incurable brain cancer glioblastoma (GBM). We found that the combination of hiNeuroS-TRAIL and TR-107 synergistically upregulated caspase markers and restored the sensitivity to the intrinsic apoptotic pathway by significantly downregulating inhibitory pathways associated with chemoresistance and radioresistance in GBM. This combination regimen showed dramatic tumor suppression and enhancement of survival of mice bearing human xenografts of both solid GBM and established invasive GBM. The combination also synergistically suppressed tumor growth in low-passage patient derived GBM cells established with an ex vivo brain slice platform. These findings elucidate a novel combination regimen and suggest combination of these clinically relevant agents may represent a new therapeutic option with increased potency for GBM patients.

NIMG-31. AXONS INTERSECTING TUMOR ENHANCEMENT (AXITE) RADIOMICS: A NOVEL PREDICTOR FOR OVERALL SURVIVAL IN GLIOBLASTOMA

Abstract AIM The diffuse infiltration of glioblastoma (GBM) along the intricate network of white matter (WM) tracts poses a major challenge. Our aim is to investigate the potential of axons intersecting tumor enhancement (AXITE)-derived radiomic features in predicting one-year overall survival (OS), in comparison to conventional tumor-based radiomics. METHODS This study included data from the publicly available UCSF-PDGM-v2 (n=97). T1+C images and their corresponding tumor mask were rigidly registered to a common brain template (MNI152) using ANTs. WM tracts within the tumor-defined region of interest were generated using the HCP1065-1mm tractography atlas in DSI Studio Software, which mapped the AXITEs. Radiomic features were then extracted using PyRadiomics from subsequently masked AXITE maps as well as contrast-enhancing tumor masks for a comparative analysis. Four machine learning models, including a Random Forest, Support Vector Machine (SVM), Decision Tree, and Logistic Regression, were employed to predict OS using an 80/20 train/test split. RESULTS Among all models, SVM was the most effective in predicting OS. AXITE radiomics demonstrated remarkable precision (0.73 for OS &amp;lt; 1 yr and 0.78 for OS &amp;gt;1yr), accuracy (0.75), F1-Score (0.75), recall (0.75), and AUC of 0.84 in ROC analysis outclassing the performance of traditional tumor radiomics which had an accuracy (0.60), precision (0.66), F1-Score (0.56) and AUC of 0.60. CONCLUSIONS AXITE Radiomics holds promising potential in capturing tumoral heterogeneity and white matter tract infiltration of GBM, demonstrating superior prediction over traditional tumor radiomics. Our approach presents a more accurate representation of tumor invasiveness and tumor resistance associated with OS more accurately and promises a potential tool for improving patient prognosis and patient management.