Figure - available from: Journal of Neuro-Oncology
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Brain segmentation of one slice in one 76-year-old man with established diagnoses of glioblastoma multiforme (GBM). Left plot: original magnetic resonance image (input); right plot: automatically 5-class segmented image via ANN. The lesion is the abnormal substance that is highlighted in red
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Purpose
Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology.
Methods
We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neu...
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Background
The Mini-Mental Status Examination (MMSE) is routinely used in neuro-oncology clinics to rule out cognitive impairment. However, the MMSE is known to have poor sensitivity to mild cognitive impairment, raising concern regarding its continued use. More comprehensive cognitive screeners are available, such as the Repeatable Battery for the...
Citations
... One significant contribution of AI in neuro-oncology diagnosis is its ability to analyze neuroimaging data with enhanced speed and accuracy [14]. Actually, AI algorithms are able to process large volumes of medical images, such as MRI scans, CT scans, and PET scans, to identify subtle abnormalities and patterns that may be missed by human observers [15]. Therefore, these algorithms can aid in the early detection and classification of brain tumors, providing clinicians with invaluable insights into tumor characteristics and behavior [16]. ...
Artificial Intelligence (AI) is rapidly transforming various aspects of healthcare, including the field of diagnostics and treatment of diseases. This review article aimed to provide an in-depth analysis of the impact of AI, especially, radiomics in the diagnosis of neuro-oncology diseases. Indeed, it is a multidimensional task that requires the integration of clinical assessment, neuroimaging techniques, and emerging technologies like AI and radiomics. The advancements in these fields have the potential to revolutionize the accuracy, efficiency, and personalized approach to diagnosing neuro-oncology diseases, leading to improved patient outcomes and enhanced overall neurologic care. However, AI has some limitations, and ethical challenges should be addressed via future research.
... With the incremental inclusion of artificial intelligence [149], large-scale data sharing, and CNNs combined with clinical data, neuroradiologists' performances are likely to improve, allowing more precise, impactful diagnoses. ...
Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators’ efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.
Simple Summary
Within the rapidly evolving landscape of Machine Learning in the medical field, this paper focuses on the forefront advancements in neuro-oncological radiology. More specifically, it aims to provide the reader with an in-depth exploration of the latest advancements in employing Deep Learning methodologies for the classification of brain tumor radiological images. This review meticulously scrutinizes papers published from 2018 to 2023, unveiling ongoing topics of research while underscoring the main remaining challenges and potential avenues for future research identified by those studies. Beyond the review itself, the paper also underscores the importance of placing the image data modelling provided by Deep Learning techniques within the framework of analytical pipeline research. This means that data quality control and pre-processing should be correctly coupled with modelling itself, in a way that emphasizes the importance of responsible data utilization, as well as the critical need for transparency in data disclosure to ensure trustworthiness and reproducibility of findings.
Abstract
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.
Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.
To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs.
Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan–Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model.
Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning–based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively.
A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs.
A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time.
• A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma.
• Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time.
• The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.
Simple Summary
In the present review, we reported the main findings describing tumor-associated microenvironment in patients with IDH mutated and wild-type gliomas. We also focused on the main differences between microenvironment composition reporting data about the microenvironment of pilocytic astrocytomas and IDH wt H3 altered gliomas. This review is finally focused on novel potential treatments targeting the tumor microenvironment.
Abstract
Gliomas are the most frequent central nervous system (CNS) primary tumors. The prognosis and clinical outcomes of these malignancies strongly diverge according to their molecular alterations and range from a few months to decades. The tumor-associated microenvironment involves all cells and connective tissues surrounding tumor cells. The composition of the microenvironment as well as the interactions with associated neoplastic mass, are both variables assuming an increasing interest in these last years. This is mainly because the microenvironment can mediate progression, invasion, dedifferentiation, resistance to treatment, and relapse of primary gliomas. In particular, the tumor microenvironment strongly diverges from isocitrate dehydrogenase (IDH) mutated and wild-type (wt) tumors. Indeed, IDH mutated gliomas often show a lower infiltration of immune cells with reduced angiogenesis as compared to IDH wt gliomas. On the other hand, IDH wt tumors exhibit a strong immune infiltration mediated by several cytokines and chemokines, including CCL2, CCL7, GDNF, CSF-1, GM-CSF, etc. The presence of several factors, including Sox2, Oct4, PD-L1, FAS-L, and TGF β2, also mediate an immune switch toward a regulatory inhibited immune system. Other important interactions are described between IDH wt glioblastoma cells and astrocytes, neurons, and stem cells, while these interactions are less elucidated in IDH-mutated tumors. The possibility of targeting the microenvironment is an intriguing perspective in terms of therapeutic drug development. In this review, we summarized available evidence related to the glioma microenvironment, focusing on differences within different glioma subtypes and on possible therapeutic development.
