Applied AI for Real-Time Detection of Lesions and Tumors Following Severe Head Injuries DOI
Attila Biró, Antonio Cuesta‐Vargas, László Szilágyi

и другие.

Опубликована: Сен. 21, 2023

Early detection and intervention of injuries, lesions, or brain anomalies can significantly improve athletes recovery process, reducing long-term impact unexpected health side effects. AI-supported anomaly systems provide high accuracy consistency in real-time image analysis, out-performing human counterparts, especially high-throughput situations. Moreover the scalability AI allows rapid processing large amounts data, making comprehensive screening feasible. Motivation-wise, AI's ability to integrate multiple data sources, like game statistics wearable sensor offers a holistic approach understanding managing, even preventing head injury risks time. The novelty this field lies application Neural Architecture Search for optimizing model architectures, transfer learning enhancement, multimodal as well explainable intelligible insights, thereby building confidence applied ecosystems. We conducted study find feasible, machine learning-based pipeline sports safety, which could identify detect injuries tumors early on help doctors reduce risk serious complications disorders caused by severe collisions concussions.

Язык: Английский

The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives DOI Creative Commons
Ondřej Uher, Katerina Hadrava Vanova, David Taïeb

и другие.

Endocrine Reviews, Год журнала: 2024, Номер 45(4), С. 521 - 552

Опубликована: Фев. 20, 2024

Abstract Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from neural crest cells adrenal medullary chromaffin tissues extra-adrenal paraganglia, respectively. Although the current treatment for PPGLs is surgery, optimal options advanced metastatic cases have been limited. Hence, understanding role of immune system in PPGL tumorigenesis can provide essential knowledge development better therapeutic tumor management strategies, especially those with PPGLs. The first part this review outlines fundamental principles microenvironment, their cancer immunoediting, particularly emphasizing We focus on how unique pathophysiology PPGLs, such as high molecular, biochemical, imaging heterogeneity production several oncometabolites, creates a tumor-specific microenvironment immunologically “cold” tumors. Thereafter, we discuss recently published studies related to reclustering based signature. second discusses future perspectives management, including immunodiagnostic promising immunotherapeutic approaches converting into active or “hot” known immunotherapy response patient outcomes. Special emphasis placed potent immune-related strategies signatures that could be used reclassification, prognostication, these improve care prognosis. Furthermore, introduce currently available immunotherapies possible combinations other therapies an emerging targets hostile environments.

Язык: Английский

Процитировано

11
The interplay between metal ions and immune cells in glioma: pathways to immune escape DOI Creative Commons
Jinwei Li,

Yi-ming Mao,

Shiliang Chen

и другие.

Discover Oncology, Год журнала: 2024, Номер 15(1)

Опубликована: Авг. 12, 2024

This review explores the intricate roles of metal ions—iron, copper, zinc, and selenium—in glioma pathogenesis immune evasion. Dysregulated ion metabolism significantly contributes to progression by inducing oxidative stress, promoting angiogenesis, modulating cell functions. Iron accumulation enhances DNA damage, copper activates hypoxia-inducible factors stimulate zinc influences proliferation apoptosis, selenium modulates tumor microenvironment through its antioxidant properties. These ions also facilitate escape upregulating checkpoints secreting immunosuppressive cytokines. Targeting pathways with therapeutic strategies such as chelating agents metalloproteinase inhibitors, particularly in combination conventional treatments like chemotherapy immunotherapy, shows promise improving treatment efficacy overcoming resistance. Future research should leverage advanced bioinformatics integrative methodologies deepen understanding ion-immune interactions, ultimately identifying novel biomarkers targets enhance management patient outcomes.

Язык: Английский

Процитировано

4
Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment DOI Creative Commons
Vincenza Granata, Roberta Fusco,

Maria Chiara Brunese

и другие.

Diagnostics, Год журнала: 2024, Номер 14(2), С. 152 - 152

Опубликована: Янв. 9, 2024

Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in pre-surgical setting, predict tumor budding liver metastases. Methods: Patients MRI setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, 851 features extracted as median values PyRadiomics Python package. Balancing performed inter- intraclass correlation coefficients calculated between observer within reproducibility all features. A Wilcoxon–Mann–Whitney nonparametric test receiver operating characteristics (ROC) carried out. feature selection procedures performed. Linear non-logistic regression models (LRM NLRM) different learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) support vector (SVM) considered. Results: The internal training set included 49 patients 119 validation cohort consisted total 28 single lesion patients. best predictor classify original_glcm_Idn obtained T1-W VIBE sequence arterial phase an accuracy 84%; wavelet_LLH_firstorder_10Percentile portal 92%; wavelet_HHL_glcm_MaximumProbability hepatobiliary excretion 88%; wavelet_LLH_glcm_Imc1 T2-W SPACE sequences 88%. Considering linear analysis, statistically significant increase 96% weighted combination 13 radiomic from phase. Moreover, classifier KNN trained sequence, obtaining 95% AUC 0.96. reached 94%, sensitivity 86% specificity 95%. Conclusions: Machine are promising tools predicting budding. there compared feature.

Язык: Английский

Процитировано

3
Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics DOI Open Access

Piero Trovato,

Igino Simonetti,

Alessio Morrone

и другие.

Journal of Clinical Medicine, Год журнала: 2024, Номер 13(2), С. 547 - 547

Опубликована: Янв. 18, 2024

Background: Small renal masses (SRMs) are defined as contrast-enhanced lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a cell carcinomas (RCCs). Currently, 50–61% of all tumors found incidentally. Methods: The characteristics the lesion influence choice type management, include several methods SRM including nephrectomy, partial ablation, observation, and also stereotactic body radiotherapy. Typical imaging available for differentiating benign from malignant ultrasound (US), (CEUS), computed tomography (CT), magnetic resonance (MRI). Results: Although is first technique used detect small lesions, it has limitations. CT main most widely characterization. advantages MRI compared better contrast resolution tissue characterization, use functional sequences, possibility performing examination patients allergic iodine-containing medium, absence exposure ionizing radiation. For a correct evaluation during follow-up, necessary reliable method assessment represented by Bosniak classification system. This was initially developed based on findings, 2019 revision proposed inclusion features; however, latest not yet received widespread validation. Conclusions: radiomics an emerging increasingly central field applications such characterizing masses, distinguishing RCC subtypes, monitoring response targeted therapeutic agents, prognosis metastatic context.

Язык: Английский

Процитировано

3
Pharmacological and Therapeutic Innovation to Mitigate Radiation-Induced Cognitive Decline (RICD) in Brain Tumor Patients DOI

Jemema Agnes Tripena Raj,

Janmay Shah,

Smita V. Ghanekar

и другие.

Cancer Letters, Год журнала: 2025, Номер unknown, С. 217700 - 217700

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Treatments and cancer: implications for radiologists DOI Creative Commons
Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola

и другие.

Frontiers in Immunology, Год журнала: 2025, Номер 16

Опубликована: Апрель 16, 2025

This review highlights the critical role of radiologists in personalized cancer treatment, focusing on evaluation treatment outcomes using imaging tools like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound. Radiologists assess effectiveness complications therapies such as chemotherapy, immunotherapy, ablative treatments. Understanding mechanisms consistent protocols are essential for accurate evaluation, especially managing complex cases liver cancer. Collaboration between oncologists is key to optimizing patient through precise assessments.

Язык: Английский

Процитировано

0
Artificial Intelligence in Radiology DOI
Alireza Mohseni, Elena Ghotbi, Foad Kazemi

и другие.

Radiologic Clinics of North America, Год журнала: 2024, Номер 62(6), С. 935 - 947

Опубликована: Апрель 24, 2024

Язык: Английский

Процитировано

1
Applied AI for Real-Time Detection of Lesions and Tumors Following Severe Head Injuries DOI
Attila Biró, Antonio Cuesta‐Vargas, László Szilágyi

и другие.

Опубликована: Сен. 21, 2023

Early detection and intervention of injuries, lesions, or brain anomalies can significantly improve athletes recovery process, reducing long-term impact unexpected health side effects. AI-supported anomaly systems provide high accuracy consistency in real-time image analysis, out-performing human counterparts, especially high-throughput situations. Moreover the scalability AI allows rapid processing large amounts data, making comprehensive screening feasible. Motivation-wise, AI's ability to integrate multiple data sources, like game statistics wearable sensor offers a holistic approach understanding managing, even preventing head injury risks time. The novelty this field lies application Neural Architecture Search for optimizing model architectures, transfer learning enhancement, multimodal as well explainable intelligible insights, thereby building confidence applied ecosystems. We conducted study find feasible, machine learning-based pipeline sports safety, which could identify detect injuries tumors early on help doctors reduce risk serious complications disorders caused by severe collisions concussions.

Язык: Английский

Процитировано

0