Necrotic Tumor Core Research Articles

Multiscale segmentation net for segregating heterogeneous brain tumors: Gliomas on multimodal MR images

In this research, the 3D volumetric segmentation of heterogeneous brain tumors such as Gliomas- anaplastic astrocytoma, and Glioblastoma Multiforme (GBM) is performed to extract enhancing tumor (ET), whole tumor (WT), and tumor core (TC) regions using T1, T2, and FLAIR images. Therefore, a deep learning-based encoder-decoder architecture named “MS-SegNet” using 3D multi-scale convolutional layers is proposed. The proposed architecture employs multi-scale feature extraction (MS-FE) block the filter size 3 × 3 × 3 to extract confined information like tumor boundary and edges of necrotic part. The filter of size 5 × 5 × 5 focuses on varied features like shape, size, and location of tumor region with edema. The local and global features from different MR modalities are extracted for segmenting thin and meshed boundaries of heterogeneous tumors between anatomical sub-regions like peritumoral edema, enhancing tumor, and necrotic tumor core. The learning parameters on introducing the MS-FE block are reduced to 10 million, which is much less than other architectures like 3D-Unet which takes into consideration 27 million features leading to the consumption of less computational power. A customized loss function is also prophesied based on a combination of dice loss and focal loss along with metrics such as accuracy and Intersection over Union (IoU) i.e. the overlapping of ground truth mask and predicted value for addressing the class imbalance problem. For evaluating the efficacy of the proposed method, four evaluation metrics such as Dice Coefficient (DSC), Sensitivity, Specificity, and Hausdorff95 distance (H95) are employed for analyzing the model's overall performance. It is observed that the proposed MS-SegNet architecture achieved the DSC of 0.81, 0.91, and 0.83 on BraTS 2020; 0.86, 0.92, and 0.84 on BraTS 2021 for ET, WT, and TC respectively. The developed model is also tested on a real-time dataset collected from the Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh. The DSC of 0.79, 0.76, and 0.68 for ET, WT, and TC respectively on the real-time dataset. These findings show that deep learning models with enhanced feature extraction capabilities can be readily trained to attain high accuracy in segmenting heterogeneous brain tumors and hold promising results. In the future, other tumor datasets will be explored for the detection and treatment planning of brain tumors to check the effectiveness of the model in real-world healthcare environments.

234 Predictive Radiomics in Glioblastoma: A Machine Learning Approach for Personalized Survival Outcomes and a Novel Web Application

INTRODUCTION: Diverse characteristics of glioblastomas (GBMs) complicate survival predictions, requiring personalized approaches. METHODS: From The Cancer Imaging Archive (TCIA) database, 611 patients from the UPENN-GBM collection and 500 patients from the UCSF-PDGM collections were included in this study. UPENN-GBM collection was used as the training set and for 5-fold internal cross-validation, whereas the UCSF-PDGM collection was used for external validation. Survival at 3, 6, 12, and 24 months were the outcomes of interest. Necrotic tumor core, enhancing tumor, and whole tumor segmentation masks for T1 with contrast, T1, T2, and FLAIR images were obtained automatically with a pre-trained Swin UNETR model. From these regions of interest, radiomic features were extracted using the PyRadiomicsLibrary. The LASSO (least absolute shrinkage and selection operator) method was employed for radiomic feature selection. The ML algorithms TabPFN, XGBoost, LightGBM, Support Vector Machine (SVM), and Random Forest (RF) were utilized to predict the survival outcomes of interest. RESULTS: The highlight of our study, our web application is available for use via the following link: https://huggingface.co/spaces/MSHSNeurosurgeryResearch/Radiomics_Based_Glioblastoma_Survival_Prediction_Tool. CONCLUSIONS: This study not only provides a radiomics pipeline for predicting the GBM survival outcomes, but it also contributes to the clinical adoption of these models by developing a fully automatic and user-friendly web application which make them available to clinicians worldwide.

Evaluating Maximum Diameters of Tumor Sub-regions for Survival Prediction in Glioblastoma Patients via Machine Learning, Considering Resection Status

In recent decades, there have been significant advancements in medical diagnosis and treatment techniques. However, there is still much progress to be made in effectively managing a wide range of diseases, particularly cancer. Timely diagnosis of cancer remains a critical step towards successful treatment, as it significantly impacts patients’ chances of survival. Among various types of cancer, glioma stands out as the most common primary brain tumor, exhibiting different levels of aggressiveness. One of the monitoring techniques is magnetic resonance imaging (MRI) that provides a precise visual representation of the tumor and its sub-regions (edema (ED), enhancing tumor (ET), and non-enhancing necrotic tumor core (NEC)), enabling monitoring of its location, shape, and sub- regional characteristics. In this study, we aim to investigate the underlying relationship between the maximumdiameters of tumor sub-regions and patients’ overall survival (OS) in glioblastoma cases. Using an MRI dataset of glioblastoma patients, we categorized them based on resection status: gross total resection (GTR) and unknown (NA). By employing the Euclidean distance algorithm, we estimated sub-regions’ maximum diameters. Machine learning algorithms were used to explore the correlation between sub-regions’ maximum diameters and survival outcomes. The results of the univariate prediction models showed that tumor sub-regions’ maximum diameters have a noticeable correlation with the survival rates among patients with unknown resection status with the average spearman correlation of -0.254. Also, addition of the sub-regions’ maximum diameter feature to the radiomics increased the accuracy of ML algorithms in predicting the survival rates with an average of 4.58%.

Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data.

The goal of this study was to get optimal brain tumor features from magnetic resonance imaging (MRI) images and classify them based on the three groups of the tumor region: Peritumoral edema, enhancing-core, and necrotic tumor core, using machine learning classification models. This study's dataset was obtained from the multimodal brain tumor segmentation challenge. A total of 599 brain MRI studies were employed, all in neuroimaging informatics technology initiative format. The dataset was divided into training, validation, and testing subsets online test dataset (OTD). The dataset includes four types of MRI series, which were combined together and processed for intensity normalization using contrast limited adaptive histogram equalization methodology. To extract radiomics features, a python-based library called pyRadiomics was employed. Particle-swarm optimization (PSO) with varying inertia weights was used for feature optimization. Inertia weight with a linearly decreasing strategy (W1), inertia weight with a nonlinear coefficient decreasing strategy (W2), and inertia weight with a logarithmic strategy (W3) were different strategies used to vary the inertia weight for feature optimization in PSO. These selected features were further optimized using the principal component analysis (PCA) method to further reducing the dimensionality and removing the noise and improve the performance and efficiency of subsequent algorithms. Support vector machine (SVM), light gradient boosting (LGB), and extreme gradient boosting (XGB) machine learning classification algorithms were utilized for the classification of images into different tumor regions using optimized features. The proposed method was also tested on institute test data (ITD) for a total of 30 patient images. For OTD test dataset, the classification accuracy of SVM was 0.989, for the LGB model (LGBM) was 0.992, and for the XGB model (XGBM) was 0.994, using the varying inertia weight-PSO optimization method and the classification accuracy of SVM was 0.996 for the LGBM was 0.998, and for the XGBM was 0.994, using PSO and PCA-a hybrid optimization technique. For ITD test dataset, the classification accuracy of SVM was 0.994 for the LGBM was 0.993, and for the XGBM was 0.997, using the hybrid optimization technique. The results suggest that the proposed method can be used to classify a brain tumor as used in this study to classify the tumor region into three groups: Peritumoral edema, enhancing-core, and necrotic tumor core. This was done by extracting the different features of the tumor, such as its shape, grey level, gray-level co-occurrence matrix, etc., and then choosing the best features using hybrid optimal feature selection techniques. This was done without much human expertise and in much less time than it would take a person.

Abstract 5813: Glycogen synthesis as potential novel target in triple negative breast cancer: Glycogen synthase 1 expression in human breast cancers and the impact of downregulation on proliferation of preclinical models

Abstract Background: Triple-negative breast cancer (TNBC) has a poor clinical prognosis and is characterized by a lack of druggable targets and a hypoxic tumor microenvironment. Hypoxia-induced glycogen accumulation and utilization are involved in cancer proliferation and therapy resistance, making modulation of glycogen metabolism of therapeutic interest. Therefore, we studied expression of glycogen synthase 1 (GYS1, the key regulator of glycogen synthesis) and glycogen stores in publicly available expression data and human breast tumors including TNBC. Also, we studied downregulation of GYS1 in preclinical breast cancer models, focusing on TNBC. Methods: GYS1 mRNA expression and correlations with survival per clinical subtype were assessed in the METABRIC dataset. A tissue micro-array was constructed from primary tumors of 396 breast cancer patients with long-term follow-up, including normal breast control tissue. Triplicate tissue cores were stained immunohistochemically for GYS1, glycogen and the hypoxic marker carbonic anhydrase 9, and with periodic acid-Schiff staining for glycogen. In four TNBC cell lines and an MDA-MB-231 xenograft model, GYS1 protein expression, glycogen content and cell proliferation in normoxia and hypoxia were evaluated +/- GYS1 knockdown by siRNA or shRNA. Sensitivity of shGYS1 cell lines to drugs targeting mitochondria was tested. Results: In the METABRIC dataset (n = 1904), overexpression of GYS1 mRNA was associated with poor overall survival (HR 1.20 [95% CI 1.05 - 1.38]), which was mainly driven by the TNBC patients (n = 299, HR 1.52 [95% CI 1.09 - 2.14]). Immunohistochemically, most primary breast tumors had elevated GYS1 levels and glycogen content compared to normal breast tissue, with subtype specific analyses ongoing. In all breast cancer cell lines, hypoxia induced GYS1 protein expression and increased glycogen content. Acute siRNA-mediated GYS1 knockdown decreased proliferation of (TN)BC cell lines and MDA-MB-231 spheroids in both hypoxia and normoxia. shGYS1 MDA-MB-231 cells had decreased glycogen levels and shGYS1 MDA-MB-231 xenograft growth was impaired, especially in the first six weeks after inoculation. In control xenografts, immunohistochemical GYS1 expression was most pronounced adjacent to the necrotic tumor core, whereas shGYS1 xenografts lacked GYS1 expression. Finally, shGYS1 MDA-MB-231 cells were more sensitive to inhibitors of mitochondrial protein homeostasis, suggesting a potential synergistic approach to overcome eventual metabolic adaptation. Conclusions: GYS1 is overexpressed in primary breast tumors and high mRNA levels correlate with poor survival in TNBC. GYS1 downregulation impairs TNBC proliferation in vitro and in vivo, highlighting glycogen synthesis as potential novel therapeutic target in TNBC. Citation Format: Ellen C. De Heer, Christos E. Zois, Esther Bridges, Mieke C. Zwager, Tineke van der Sluis, Bert van der Vegt, Carolien P. Schröder, Steven de Jong, Adrian L. Harris, Mathilde Jalving. Glycogen synthesis as potential novel target in triple negative breast cancer: Glycogen synthase 1 expression in human breast cancers and the impact of downregulation on proliferation of preclinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5813.

OKN-007 decreases tumor necrosis and tumor cell proliferation and increases apoptosis in a preclinical F98 rat glioma model.

Glioblastoma is a malignant World Health Organization (WHO) grade IV glioma with a poor prognosis in humans. New therapeutics are desperately required. The nitrone OKN-007 (2,4-disulfophenyl-PBN) has demonstrated effective anti-glioma properties in several rodent models and is currently being used as a clinical investigational drug for recurrent gliomas. We assessed the regional effects of OKN-007 in the tumor necrotic core and non-necrotic tumor parenchyma. An F98 rat glioma model was evaluated using proton magnetic resonance spectroscopy ((1) H-MRS), diffusion-weighted imaging (DWI), morphological T2-weighted imaging (T2W) at 7 Tesla (30 cm-bore MRI), as well as immunohistochemistry and microarray assessments, at maximum tumor volumes (15-23 days following cell implantation in untreated (UT) tumors, and 18-35 days in OKN-007-treated tumors). (1) H-MRS data indicates that Lip0.9/Cho, Lip0.9/Cr, Lip1.3/Cho, and Lip1.3/Cr ratios are significantly decreased (all P < 0.05) in the OKN-007-treated group compared with UT F98 gliomas. The Cho/Cr ratio is also significantly decreased in the OKN-007-treated group compared with UT gliomas. In addition, the OKN-007-treated group demonstrates significantly lower ADC values in the necrotic tumor core and the nonnecrotic tumor parenchyma (both P < 0.05) compared with the UT group. There was also an increase in apoptosis following OKN-007 treatment (P < 0.01) compared with UT. OKN-007 reduces both necrosis and tumor cell proliferation, as well as seems to mediate multiple effects in different tumor regions (tumor necrotic core and nonnecrotic tumor parenchyma) in F98 gliomas, indicating the efficacy of OKN-007 as an anti-cancer agent and its potential clinical use.

Isolation and Expansion of Regionally Defined Human Glioblastoma Cells In Vitro

This unit describes a protocol for the surgical collection, isolation, and expansion of regionally defined human glioblastoma multiforme (GBM)-derived cells. Given the important role played by microenvironmental hypoxia in defining cell phenotype and molecular signaling activation, it is important to distinguish between tumor tissues that were originally localized within the partially necrotic tumor core and those located along the peripheral and vascularized areas. The procedures for enzymatic dissociation of GBM tissues and cell culturing under hypoxia described here are optimized to obtain an efficient single-cell suspension and subsequent growth, in an effort to avoid the spontaneous induction of cell commitment normally occurring in long-term cell culture.

Lactate imaging with Hadamard‐encoded slice‐selective multiple quantum coherence chemical‐shift imaging

The ability to generate in vivo maps of lactate may have significant diagnostic utility in staging and treatment planning of a wide variety of cancers. The double selective multiple quantum filter technique (SelMQC) has been shown to be effective for nonlocalized detection of lactate with little or no interference from other signals. Here the SelMQC technique has been combined with longitudinal Hadamard slice selection and chemical shift imaging (CSI) to yield slice-selective images of lactate. The technique is shown to be effective in phantoms and in WSU-DLCL2 xenografts implanted in flanks of SCID mice. Tumors exhibited an annulus of elevated lactate concentration surrounding a necrotic tumor core.

Infratentorial pediatric brain tumors: the value of new imaging modalities

The correct assessment of the four most frequent infratentorial brain tumors in children (medulloblastoma, ependymoma, pilocytic astrocytoma and infiltrating glioma) has always been problematic. They are known to often resemble one another on conventional magnetic resonance (MR) imaging. We tested the hypothesis whether the combined strength of diffusion-weighted imaging (DWI) and proton MR spectroscopy (MRS) could help differentiate these tumors. Seventeen children with untreated posterior fossa tumors were investigated between January 2005 and January 2006 with conventional MR imaging and combined DWI and MR spectroscopy using a single-voxel technique at short and long echo time (TE) of 30 ms and 135 ms respectively. Apparent diffusion coefficient (ADC) values were retrieved after regions of interest were manually positioned within non necrotic tumor core. Water signal was quantified and metabolite signals were compared and analyzed using linear discriminant analysis. When a combination of ADC values and normalized metabolites was used, all tumors could be discriminated against one other. This could only be achieved when metabolites were normalized using water as an internal standard. They could not be discriminated when using metabolite ratios or ADC values alone, nor could they be differentiated using creatine (Cr) as an internal reference even in combination with ADC values. In conclusion, linear discriminant analysis and multiparametric combination of DWI and MRS, although not replacing histology, fully discriminates the four most frequent posterior fossa tumors in children, but metabolites have to be normalized using water and not Cr signal as an internal reference.

A Predictive Model of Therapeutic Monoclonal Antibody Dynamics and Regulation by the Neonatal Fc Receptor (FcRn)

We constructed a novel physiologically-based pharmacokinetic (PBPK) model for predicting interactions between the neonatal Fc receptor (FcRn) and anti-carcinoembryonic antigen (CEA) monoclonal antibodies (mAbs) with varying affinity for FcRn. Our new model, an integration and extension of several previously published models, includes aspects of mAb-FcRn dynamics within intracellular compartments not represented in previous PBPK models. We added mechanistic structure that details internalization of class G immunoglobulins by endothelial cells, subsequent FcRn binding, recycling into plasma of FcRn-bound IgG and degradation of free endosomal IgG. Degradation in liver is explicitly represented along with the FcRn submodel in skin and muscle. A variable tumor mass submodel is also included, used to estimate the growth of an avascular, necrotic tumor core, providing a more realistic picture of mAb uptake by tumor. We fitted the new multiscale model to published anti-CEA mAb biodistribution data, i.e. concentration-time profiles in tumor and various healthy tissues in mice, providing new estimates of mAb-FcRn related kinetic parameters. The model was further validated by successful prediction of F(ab')2 mAb fragment biodistribution, providing additional evidence of its potential value in optimizing intact mAb and mAb fragment dosing for clinical imaging and immunotherapy applications.

Quantitative analysis of antibody localization in human metastatic colon cancer: a phase I study of monoclonal antibody A33.

A33 is a mouse immunoglobulin G2a (IgG2a) monoclonal antibody (mAb) that detects a heat-stable, protease- and neuraminidase-resistant epitope. The antigen is homogeneously expressed by virtually all colon cancers and in the colon mucosa but not other epithelial tissues. The biodistribution and imaging characteristics of iodine-131 (131I)-mAbA33 were studied in colorectal carcinoma patients with hepatic metastases. Antibody labeled with 2 to 5 mCi of 131I was administered intravenously (IV) 7 to 8 days before surgery at five dose levels, ranging from 0.2 mg to 50 mg, with three or more patients entered at each dose level. In addition, three patients received 2 mg 131I-mAbTA99 (an isotype-matched control mAb) together with 125I-mAbA33. Evaluation included whole-body imaging with a gamma camera, technetium-99 (99mTc)-human serum albumin blood pool scans, liver/spleen scans, abdominal computed tomographic (CT) scans, hepatic arteriograms, antibody pharmacokinetics, and assessment of antibody distribution in biopsied malignant and normal tissues. Selective mAbA33 localization to tumor tissue was demonstrated in 19 of 20 patients, and external imaging correlated with surgical inspection, pathologic examination, and tissue radioactivity. One week after antibody administration, tumor:liver ratios ranged from 6.9:1 to 100:1 and tumor:serum ratios from 4.1:1 to 25.2:1. 99mTc-albumin blood pool studies showed that liver metastases were hypovascular, emphasizing the selective localization of mAbA33 despite poor tumor-blood flow. Control mAbTA99 studies showed mAbA33 localization was antigen-specific; tumor:liver ratios were 2.3- to 45-fold higher for specific antibody. In metastatic lesions, radioisotope was localized primarily in the viable periphery; however, even the necrotic tumor core concentrated specific antibody. External imaging showed isotope visualization in some patients' large bowel; whether this represents specific antibody uptake or gastric iodine secretion is unclear.

Thermal modeling in cylindrical coordinates using effective conductivity

Predictive thermometry, utilizing minimally invasive sampling techniques, is an essential ingredient in the development of hyperthermia treatment planning capabilities. The authors demonstrate a powerful, but simple approach toward predicting temperature distributions in tissues, based on analytic solution, using in cylindrical symmetry, of the heat diffusion equation. Conduction and localized perfusion effects are combined as an effective conductivity term, readily measurable, and parametrized in a general exponential form. The proposed approach allows a first-order approximation to modeling three typical situations: hypoxic or necrotic tumor core with homogeneously perfused periphery; highly perfused periphery (in rapidly growing tumors); or perfused central cover with a less well-supplied periphery (such as for some invasive tumors). The utility and strength of this approach is that it provides a rapid, accurate model of directly observing the technical quality to be expected for different heating methods, making it possible to optimally configure source distributions in a treatment planning setting.