Search Results (334)

Background: Clinical imaging is an important part of health care providing physicians with great assistance in patients treatment. In fact, segmentation and grading of tumors can help doctors assess the severity of the cancer at an early stage and increase the chances of cure. Despite that Deep Learning for cancer diagnosis has achieved clinically acceptable accuracy, there still remains challenging tasks, especially in the context of insufficient labeled data and the subsequent need for expensive computational ressources. Objective: This paper presents a lightweight classification and segmentation deep learning model to assist in the identification of cancerous tumors with high accuracy despite the scarcity of medical data. Methods: We propose a multi-task architecture for classification and segmentation of cancerous tumors in the Brain, Skin, Prostate and lungs. The model is based on the UNet architecture with different pre-trained deep learning models (VGG 16 and MobileNetv2) as a backbone. The multi-task model is validated on relatively small datasets (slightly exceed 1200 images) that are diverse in terms of modalities (IRM, X-Ray, Dermoscopic and Digital Histopathology), number of classes, shapes, and sizes of cancer pathologies using the accuracy and dice coefficient as statistical metrics. Results: Experiments show that the multi-task approach improve the learning efficiency and the prediction accuracy for the segmentation and classification tasks, compared to training the individual models separately. The multi-task architecture reached a classification accuracy of 86%, 90%, 88%, and 87% respectively for Skin Lesion, Brain Tumor, Prostate Cancer and Pneumothorax. For the segmentation tasks we were able to achieve high precisions respectively 95%, 98% for the Skin Lesion and Brain Tumor segmentation and a 99% precise segmentation for both Prostate cancer and Pneumothorax. Proving that the multi-task solution is more efficient than single-task networks. Full article

Background: CIC-rearranged sarcoma is a rare and aggressive type of undifferentiated round cell tumor characterized by CIC gene fusion, most commonly CIC::DUX4. This study presents a series of eleven cases, highlighting their clinicopathological features. Methods: Pathology records (2019 to 2024) were searched using “sarcoma with CIC”, identifying eleven cases, of which seven referred cases were initially misdiagnosed. Pathological and clinical analysis was conducted. Treatment was dictated upon multidisciplinary panel discussion based on tumor stage. Follow-up data (1–25 months) was available for all patients. Results: The cohort included six males and five females, with a median age of 43 years (range;14–53), with nine in soft tissue and two in bone. Tumor size ranged from 3.5 cm to 20.0 cm (mean: 9.8 cm). Most cases showed sheets of undifferentiated round- to oval-shaped cells. Two cases showed an Ewing-like pattern, and one case showed spindle cells in a fibrotic stroma transitioning to epithelioid cells. Necrosis was present in nine cases, and mitotic count ranged from 2 to 38/ 10HPFs (mean = 14.2). CD99 was positive in (10/11) cases and WT-1 in (6/9). NKX2.2, S100, and MDM2 were positive in rare cases. CIC::DUX4 fusion was detected in four cases. FISH for CIC gene rearrangement was positive in seven cases, two of them confirmed by methylation analysis. Metastasis at diagnosis was common (n = 8), primarily in the lungs, with later metastasis to the brain and bone. At time of final analysis, eight patients died within a median of 10 months (range: 1–19 months), while three were alive, two with stable disease (for a period of 6 and 25 months) and one with progression after 10 months. Significant correlation was seen between overall survival and the presence of metastasis at diagnosis (p value = 0.03). Conclusions: CIC-rearranged sarcomas are rare, high-grade tumors with predilection for soft tissue. Misdiagnosis is frequent, necessitating molecular confirmation. These tumors are treatment-resistant, often present with lung metastasis, and carry a poor prognosis, especially with initial metastasis. Full article

Pediatric high-grade gliomas (pHGGs), particularly diffuse midline gliomas (DMGs), are among the most lethal brain tumors due to poor survival and resistance to therapies. DMGs possess a distinct genetic profile, primarily driven by hallmark mutations such as H3K27M, ACVR1, and PDGFRA mutations/amplifications and TP53 inactivation, all of which contribute to tumor biology and therapeutic resistance. Developing physiologically relevant preclinical models that replicate both tumor biology and the tumor microenvironment (TME) is critical for advancing effective treatments. This review highlights recent progress in in vitro, ex vivo, and in vivo models, including patient-derived brain organoids, genetically engineered mouse models (GEMMs), and region-specific midline organoids incorporating SHH, BMP, and FGF2/8/19 signaling to model pontine gliomas. Key genetic alterations can now be introduced using lipofectamine-mediated transfection, PiggyBac plasmid systems, and CRISPR-Cas9, allowing the precise study of tumor initiation, progression, and therapy resistance. These models enable the investigation of TME interactions, including immune responses, neuronal infiltration, and therapeutic vulnerabilities. Future advancements involve developing immune-competent organoids, integrating vascularized networks, and applying multi-omics platforms like single-cell RNA sequencing and spatial transcriptomics to dissect tumor heterogeneity and lineage-specific vulnerabilities. These innovative approaches aim to enhance drug screening, identify new therapeutic targets, and accelerate personalized treatments for pediatric gliomas. Full article

Background: High-grade glioma (HGG) is the most common primary malignant brain tumor, with peak incidence in the fifth and sixth decades of life. Although HGG is rare in children, the prognosis remains poor, with a median overall survival (OS) of less than two years. Recently, TRK inhibitors have been approved for the treatment of tumors harboring NTRK gene fusions. In this review, we analyzed data from early clinical trials investigating the use of these agents in patients with HGG. Methods: A systematic literature search was performed in the PubMed database. Studies involving patients with HGG treated with TRK inhibitors were included. We analyzed progression-free survival (PFS), 24-week disease control rate, and complete or partial radiological responses according to the Response Assessment in Neuro-Oncology (RANO) criteria. Results: Sixteen studies comprising 55 patients with HGG harboring NTRK gene fusions (19 adults and 36 children) were included. A statistically significant difference in PFS was observed between pediatric and adult patients treated with TRK inhibitors (17 vs. 8.5 months; p < 0.001). Pediatric patients also exhibited a higher rate of complete or partial radiological response compared to adults (94% vs. 57%). Discussion: Although the available evidence on TRK inhibitors in HGG is limited, the findings of this review highlight a potentially promising role for these agents, particularly in the treatment of pediatric HGGs. Full article

Background: Pediatric high-grade glioma (pHGG) is a highly aggressive cancer with unique biology distinct from adult high-grade glioma, limiting the effectiveness of standard treatment protocols derived from adult research. Objective: The purpose of this report is to present preliminary results from an ongoing pilot study integrating spectroscopic magnetic resonance imaging (sMRI) to guide proton beam therapy and longitudinal imaging analysis in pediatric patients with high-grade glioma (pHGG). Methods: Thirteen pediatric patients under 21 years old with supratentorial WHO grade III-IV glioma underwent baseline and serial whole-brain spectroscopic MRI alongside standard structural MRIs. Radiation targets were defined using T1-weighted contrast enhanced, T2-FLAIR, and Cho/NAA ≥ 2X maps. Longitudinal analyses included voxel-level metabolic change maps and spatial overlap metrics comparing pre-proton therapy and post-. Results: Six patients had sufficient longitudinal data; five received sMRI-guided PBT. Significant positive correlation (R² = 0.89, p < 0.0001) was observed between T2-FLAIR and Cho/NAA ≥ 2X volumes. Voxel-level difference maps of Cho/NAA and Choline revealed dynamic metabolic changes across follow-up scans. Analyzing Cho/NAA and Cho changes over time allowed differentiation between true progression and pseudoprogression, which conventional MRI alone struggles to achieve. Conclusions: Longitudinal sMRI enhanced metabolic tracking in pHGG, detects early tumor changes, and refines RT targeting beyond structural imaging. This first in-kind study highlights the potential of sMRI biomarkers in tracking treatment effects and emphasizes the complementary roles of metabolic and radiographic metrics in evaluating therapy response in pHGG. Full article

Background: The occurrence (incidence or prevalence) of delirium in brain tumors is unknown, yet delirium is associated with increased morbidity and mortality and worse quality of life. We conducted a systematic review and meta-analysis to determine the occurrence of delirium in hospitalized patients with brain tumors. Methods: PubMed, Scopus, and Web of Science were systematically searched for papers from 1 January 1999 to 12 July 2024, including references from texts. Cross-sectional, prospective, and other cohort study designs were included, and individual case reports, case series, editorials, and reviews were excluded. The included papers were scored using a validated sensitivity analysis tool and tested for quality and bias using funnel plots and Egger’s test. We used random effects models for the summary estimates. We performed subgroup analyses by tumor type, tumor location, delirium subtype, and length of stay. Results: Of the 452 studies screened, 27 were included, representing 35,958 patients. The overall occurrence of delirium was 0.17 (95% CI [0.11–0.24]). Delirium occurrence in patients with low-grade gliomas, high-grade gliomas, and brain metastases was 0.10 [0.06–0.16], 0.21 [0.10–0.40], and 0.31 [0.16–0.50], respectively. Compared to the occipital lobe, there was a higher occurrence of delirium for tumors in the frontal (RR 3.08 [1.35–8.22]) and temporal lobes (RR 2.88 [1.22–7.93]). The patients were more likely to have hypoactive (RR 1.61 [1.30; 1.98]) than hyperactive delirium. Delirium was associated with 4.62 additional hospitalized days compared to those without delirium (CI [3.23–6.01]). Discussion: We confirmed high occurrence rates of delirium in patients hospitalized with brain tumors. Patients with brain metastases had a higher occurrence of delirium compared to patients with gliomas, and delirium occurrence rates were higher in patients with frontotemporal tumors. Delirium occurrence rates in the literature are very heterogeneous and point toward a need for tailored assessments in patients with brain tumors. Full article

A newly recognized fourth type of perivascular space has recently been described in the radiological literature. Despite its growing relevance, many radiologists are still unfamiliar with its imaging characteristics, often leading to misinterpretation as cystic neoplasms. Due to its potential for diagnostic confusion, further studies are necessary—particularly those incorporating high-quality imaging examples across various presentations—to facilitate accurate recognition and classification. Perivascular spaces (PVSs) of the brain are cystic, fluid-filled structures formed by the pia mater and located alongside cerebral blood vessels, particularly penetrating arterioles, venules, and capillaries. Under normal conditions, these spaces are small (typically <2 mm in diameter), but in rare instances, they may become markedly enlarged (>15 mm), exerting a mass effect on adjacent brain tissue. This newly identified fourth type of PVS is found in association with the M2 and M3 segments of the middle cerebral artery, typically within the anterior temporal lobe white matter. It may mimic low-grade cystic tumors on imaging due to its size and frequent presence of surrounding perifocal edema. We present two adult male patients with this rare PVS variant. The first patient, a 63-year-old, had a brain magnetic resonance imaging scan (MRI) that revealed a cystic lesion in the white matter of the right temporal lobe anterior pole, near the middle cerebral artery M2 segment, with perifocal vasogenic edema. The second patient, a 67-year-old, had a brain MRI that showed a cystic lesion in the white matter and subcortical region of the right temporal lobe anterior pole, with minimal surrounding gliosis or minimal edema. The cystic lesions in both patients remained unchanged over time on follow-up MRI. These cases illustrate the radiological complexity of this under-recognized entity and emphasize the importance of differential diagnosis to avoid unnecessary intervention. Full article

Gliomas are incurable, heterogeneous brain tumors, with rare forms often constituting diagnostic and treatment challenges. Molecular diagnostics, mainly implemented through the World Health Organization (WHO) 2021 guidelines, have refined the classification, but highlight difficulties in diagnosing rare gliomas remain. This case series analyzes four patients with rare gliomas treated at the University Hospital, Ulm, between 2002 and 2024. Patients were selected based on unique histopathological features and long-term clinical follow-up. Clinical records, imaging, and histological data were reviewed. Molecular diagnostics followed WHO 2021 guidelines. Quality of life was assessed using standardized tools including the EQ-5D-5L, EQ VAS, the Distress Thermometer, and the Montreal Cognitive Assessment (MoCA). In the first case, a 51-year-old male’s diagnosis evolved from pleomorphic xanthoastrocytoma to a high-grade glioma with pleomorphic and pseudopapillary features, later identified as a neuroepithelial tumor with a PATZ1 fusion over 12 years. Despite multiple recurrences, extensive surgical interventions led to excellent outcomes. The second case involved a young female with long-term survival of astroblastoma, demonstrating significant improvements in both longevity and quality of life through personalized care. The third case involved a patient with oligodendroglioma, later transforming into glioblastoma, emphasizing the importance of continuous diagnostic reevaluation and adaptive treatment strategies, contributing to prolonged survival and quality of life improvements. Remarkably, the patient has achieved over 20 years of survival, including 10 years of being both therapy- and progression-free. The fourth case presents a young woman with neurofibromatosis type 1, initially misdiagnosed with glioblastoma based on histopathological findings. Subsequent molecular diagnostics revealed a subependymal giant cell astrocytoma-like astrocytoma, highlighting the critical role of early advanced diagnostic techniques. These cases underscore the importance of precise molecular diagnostics, individualized treatments, and ongoing diagnostic reevaluation to optimize outcomes. They also address the psychological impact of evolving diagnoses, stressing the need for comprehensive patient support. Even in complex cases, extensive surgical interventions can yield favorable results, reinforcing the value of adaptive, multidisciplinary strategies based on evolving tumor characteristics. Full article

Background High-grade gliomas (HGGs) are aggressive brain tumors with poor prognoses. Maximizing the extent of resection (EOR) is a critical surgical goal. Fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA) has been proposed to enhance tumor visualization and resection. MethodsWe retrospectively analyzed 141 patients with histologically confirmed HGGs who underwent either 5-ALA-guided (n = 71) or conventional white-light (n = 70) resection between 2018 and 2023. Propensity score matching and multivariate Cox regression models were used to assess the impact of 5-ALA on surgical outcomes and survival. Results: Gross total resection (GTR) was significantly more common in the 5-ALA group than the conventional white-light group (28.17% vs. 12.86%, p = 0.0245). Kaplan–Meier analysis showed no statistically significant difference in overall survival between groups after matching (log-rank p = 0.6371). However, patients with GTR had significantly improved survival compared to those with subtotal resection (log-rank p = 0.0423). Multivariate Cox regression identified radiotherapy (HR = 0.291, 95% CI: 0.166–0.513, p < 0.001), higher Karnofsky Performance Status (HR = 0.962, 95% CI: 0.942–0.982, p = 0.0003), and GTR (HR = 0.476, 95% CI: 0.272–0.834, p = 0.0091) as independent predictors of improved survival. 5-ALA usage was not an independent predictor (HR = 0.885, 95% CI: 0.554–1.413, p = 0.612). Radiotherapy and chemotherapy were more frequently administered in the conventional white-light group (p = 0.0404 and p = 0.0085, respectively). Conclusions 5-ALA fluorescence-guided surgery significantly increases the rate of gross total resection in high-grade glioma patients but does not independently confer a survival advantage. Survival outcomes are primarily influenced by the extent of resection, adjuvant therapy, and functional status. Integration of 5-ALA within a comprehensive oncological framework may enhance its clinical utility. Full article

Background/Objectives: The accurate diagnosis and classification of brain tumors are critical for appropriate treatment planning and patient management. We evaluated the effectiveness of perfusion in differentiating glial tumors from metastases using dynamic susceptibility-weighted contrast enhanced perfusion MRI (DSC-MRI) Methods: A total of 95 consecutive patients with pathological diagnoses of brain tumors who underwent perfusion MRI between July 2021 and March 2023 were retrospectively recruited. Conventional and perfusion MRI were evaluated, and tumoral and peritumoral relative cerebral blood volume (rCBV) values were measured. Mann–Whitney U and Kruskal–Wallis tests were performed for non-parametric comparisons of continuous data. The optimal cut-off value of rCBV in differentiating tumor types was evaluated with the receiver operating characteristic (ROC) curve analysis. Results: Tumoral rCBV (p < 0.001) and peritumoral rCBV values (p = 0.001) were significantly higher in glial tumors than metastases. Further subgroup analyses showed that tumoral and peritumoral rCBV values of glial tumors were higher than those of non-small-cell lung cancers (p < 0.001 and p = 0.003, respectively) and those of breast cancer (p = 0.311 and p = 0.053, respectively) in discriminating high-grade glial tumors and metastases. ROC analyses showed that area under the curve values for tumoral and peritumoral rCBV were 0.816 and 0.725, respectively, for the optimal cut-off points 1.339 and 1.238 (87.5% and 58.33% sensitivity; 73.85% and 90.77% specificity, respectively). Multivariate analysis showed that increased tumoral rCBV and peritumoral rCBV values were independent risk factors for glial tumor occurrence. Conclusions: DSC-MRI is an effective method to differentiate glial tumors and metastases. Higher rCBV values may serve as a determinant for the diagnosis of glial tumors and metastatic brain tumors. Full article

Pediatric gliomas encompass the most common brain tumor in children and are subdivided into pediatric low-grade gliomas (pLGGs) and pediatric high-grade gliomas (pHGGs). The era of molecular diagnosis has shifted the treatment paradigms and management of these patients. RAS/MAPK pathway alterations serve as the driver in the majority of pLGGs, a subset of pHGG and NF1-related plexiform neurofibromas (PNs). The role of small molecule inhibitors in the treatment of these tumors has evolved in the past decade, facilitated through multiple clinical trials and moving into earlier stages of treatment. Although these developments hold promise, questions remain regarding targeted therapy, the long-term toxicities, the duration of treatment and the potential effects on the natural history of the tumor behavior. Full article

Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better survival outcomes compared to males. While sex differences in GBM have been observed, the underlying biological mechanisms remain poorly understood. Feature selection can lead to the identification of discriminative key biomarkers by reducing dimensionality from high-dimensional medical datasets to improve machine learning model performance, explainability, and interpretability. Feature selection can uncover unique sex-specific biomarkers, determinants, and molecular profiles in patients with GBM. We analyzed high-dimensional proteomic and metabolomic profiles from serum biospecimens obtained from 109 patients with pathology-proven glioblastoma (GBM) on NIH IRB-approved protocols with full clinical annotation (local dataset). Serum proteomic analysis was performed using Somalogic aptamer-based technology (measuring 7289 proteins) and serum metabolome analysis using the University of Florida’s SECIM (Southeast Center for Integrated Metabolomics) platform (measuring 6015 metabolites). Machine learning-based feature selection was employed to identify proteins and metabolites associated with male and female labels in high-dimensional datasets. Results were compared to publicly available proteomic and metabolomic datasets (CPTAC and TCGA) using the same methodology and TCGA data previously structured for glioma grading. Employing a machine learning-based and hybrid feature selection approach, utilizing both LASSO and mRMR, in conjunction with a rank-based weighting method (i.e., GLIO-Select), we linked proteomic and metabolomic data to clinical data for the purposes of feature reduction to identify molecular biomarkers associated with biological sex in patients with GBM and used a separate TCGA set to explore possible linkages between biological sex and mutations associated with tumor grading. Serum proteomic and metabolomic data identified several hundred features that were associated with the male/female class label in the GBM datasets. Using the local serum-based dataset of 109 patients, 17 features (100% ACC) and 16 features (92% ACC) were identified for the proteomic and metabolomic datasets, respectively. Using the CPTAC tissue-based dataset (8828 proteomic and 59 metabolomic features), 5 features (99% ACC) and 13 features (80% ACC) were identified for the proteomic and metabolomic datasets, respectively. The proteomic data serum or tissue (CPTAC) achieved the highest accuracy rates (100% and 99%, respectively), followed by serum metabolome and tissue metabolome. The local serum data yielded several clinically known features (PSA, PZP, HCG, and FSH) which were distinct from CPTAC tissue data (RPS4Y1 and DDX3Y), both providing methodological validation, with PZP and defensins (DEFA3 and DEFB4A) representing shared proteomic features between serum and tissue. Metabolomic features shared between serum and tissue were homocysteine and pantothenic acid. Several signals emerged that are known to be associated with glioma or GBM but not previously known to be associated with biological sex, requiring further research, as well as several novel signals that were previously not linked to either biological sex or glioma. EGFR, FAT4, and BCOR were the three features associated with 64% ACC using the TCGA glioma grading set. GLIO-Select shows remarkable results in reducing feature dimensionality when different types of datasets (e.g., serum and tissue-based) were used for our analyses. The proposed approach successfully reduced relevant features to less than twenty biomarkers for each GBM dataset. Serum biospecimens appear to be highly effective for identifying biologically relevant sex differences in GBM. These findings suggest that serum-based noninvasive biospecimen-based analyses may provide more accurate and clinically detailed insights into sex as a biological variable (SABV) as compared to other biospecimens, with several signals linking sex differences and glioma pathology via immune response, amino acid metabolism, and cancer hallmark signals requiring further research. Our results underscore the importance of biospecimen choice and feature selection in enhancing the interpretation of omics data for understanding sex-based differences in GBM. This discovery holds significant potential for enhancing personalized treatment plans and patient outcomes. Full article

Brain tumors are among the most common malignant tumors of the central nervous system, with high mortality and recurrence rates. Radiomics extracts quantitative features from medical images, converting them into predictive biomarkers for tumor diagnosis, prognosis, and survival analysis. Despite the invasiveness and heterogeneity of brain tumors, even with timely treatment, the overall survival time or survival probability is not necessarily favorable. Therefore, accurate prediction of brain tumor grade and survival outcomes is important for personalized treatment. In this study, we propose a radiomic model for the non-invasive prediction of brain tumor grade and patient survival outcomes. We used four magnetic resonance imaging (MRI) sequences from 159 patients with glioma. Four classifiers were employed based on whether feature selection was applied. The features were derived from regions of interest identified and corrected either manually or automatically. The extreme gradient boosting (XGB) model with 3860 radiomic features achieved the highest classification performance, with an AUC of 98.20%, in distinguishing LGG from GBM images using manually corrected labels. Similarly, the Random Forest (RF) model exhibits the best discrimination between short-term and long-term survival groups with a p-value < 0.0003, a hazard ratio (HR) value of 3.24, and a 95% confidence interval (CI) of 1.63–4.43 based on the ICC features. The experimental findings demonstrate strong classification accuracy and effectively predict survival outcomes in glioma patients. Full article

Background/Objectives: Glioblastoma IDH-wildtype CNS WHO grade 4 and astrocytoma IDH-mutant WHO grade 4 (together, high-grade gliomas: HGGs) are the most prevalent malignant brain tumors, carrying a poor prognosis despite multimodal treatment. Surgical site infections (SSIs) represent a relative frequent postoperative complication in HGG patients. Despite multimodal treatment protocols combining surgery, radiotherapy, and temozolomide chemotherapy, HGGs remain associated with a dismal prognosis, underscoring the need to evaluate how SSIs impact disease progression and survival outcomes. This study’s aim was to investigate the influence of SSIs on the clinical course of patients with HGGs. Methods: A comprehensive review of medical records for HGG patients treated at our institution between 2010 and 2024 identified 26 patients with SSIs. These patients were compared to an age-matched control group with the same histological diagnosis and treatment regimen. This study analyzed overall survival (OS), microbiological data, and pathological parameters to assess the impact of SSIs on patient outcomes. Survival differences between the infected and non-infected groups were evaluated using Kaplan–Meier survival curves. Remarkably, three patients with exceptionally long overall survival were highlighted in this study. Results: Among the cohort of 2008 patients with HGG surgery, 26 patients developed SSIs. An age-matched control group of 26 patients was identified, none of whom experienced SSIs. Comparing the OS between the infected and uninfected groups, a statistically significant improvement in OS was observed in the infected group (p = 0.049). The median OS in the infected group was 388 days, slightly shorter than the median OS of 422 days in the control group. However, the mean OS was markedly higher in the infected group (674 days) compared to the control group (442 days). The standard deviation of OS in the infected group was notably expansive, indicating substantial variability in survival outcomes. A cluster of infected patients with SSIs near the time of diagnosis had shorter OS, while other infected cases demonstrated significantly longer survival, exceeding both median and mean OS values. In contrast, the uninfected group showed limited standard deviation values, with uniformly distributed individual OS data around the median and mean values. Expectedly, IDH mutation status significantly influenced the survival in cohort patients. However, when stratified by infection status, no association between IDH mutation and improved infection-related survival was identified. The microbiological profile of SSIs was diverse, encompassing Gram-positive and Gram-negative bacteria as well as aerobic and anaerobic organisms. Conclusions: These findings underscore the heterogeneity of infection-related outcomes and their potential impact on survival in HGG patients. According to our knowledge, our study is one of the largest retrospective studies to date investigating and confirming the significant relationship between SSIs and HGG patients’ survival. Our results confirm the Janus Face phenomenon of infections, having both negative and positive effects depending on the context. Full article