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Medicina
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Advances of Artificial Intelligence in the Management of Patients with...
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Advances of Artificial Intelligence in the Management of Patients with Central Nervous System Tumors

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 8211

Special Issue Editors

Dr. Santiago Cepeda

E-Mail Website
Guest Editor
Department of Neurosurgery, Hospital Universitario Río Hortega, Valladolid, Spain
Interests: neuroimaging; brain tumors, gliomas; machine learning; intraoperative ultrasound; elastography
Dr. Sergio García-García

E-Mail Website
Guest Editor
Department of Neurosurgery, Hospital Universitario Río Hortega, Valladolid, Spain
Interests: neuroimaging; brain tumors; gliomas; machine learning; neurovascular; cerebral aneurysms; subarachnoid hemorrhage

Special Issue Information

Dear Colleagues,

We are living at the beginning of a new era in precision medicine. Central nervous system neoplasms represent a significant challenge due to their great heterogeneity and pathophysiological characteristics. Nevertheless, thanks to advances in data processing techniques in recent years, we are increasingly close to providing management adapted to particular circumstances of our patients.

In recent years, advanced neuroimaging techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced imaging (DCE), MR spectroscopy, and magnetic resonance elastography (MRE) provided valuable qualitative and quantitative information about morphology and function that can be used as a biomarkers in brain tumors.

On the other hand, the molecular characterization of tumors is enhanced with current genetic sequencing techniques and transcriptomic and proteomic analysis.

A large amount of data from advanced image analysis and new molecular techniques pose a challenge for processing and interpretation. Nevertheless, artificial intelligence, understood as any method that allows the imitation of human intelligence, allows significant advances in neuro-oncology. Its application allows the performance of complex tasks related to diagnosis, segmentation, histopathological diagnosis, tracking tumor development, classification, and predictions of outcome. Thus, the classification and prediction results generated by IA can be applied in daily clinical practice.

We welcome submissions of research papers providing evidence for the development and application of techniques based on artificial intelligence related to managing CNS tumors.

Dr. Santiago Cepeda
Dr. Sergio García-García
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Medicina is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • brain tumors
  • machine learning
  • deep learning

Published Papers (2 papers)

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Research

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19 pages, 10008 KiB  
Article
Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM
by Sarmad Maqsood, Robertas Damaševičius and Rytis Maskeliūnas
Medicina 2022, 58(8), 1090; https://doi.org/10.3390/medicina58081090 - 12 Aug 2022
Cited by 85 | Viewed by 6007
Abstract
Background and Objectives: Clinical diagnosis has become very significant in today’s health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosis [...] Read more.
Background and Objectives: Clinical diagnosis has become very significant in today’s health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosis of brain tumors can be improved by an early and precise diagnosis based on magnetic resonance imaging. For computer-aided diagnosis methods to assist radiologists in the proper detection of brain tumors, medical imagery must be detected, segmented, and classified. Manual brain tumor detection is a monotonous and error-prone procedure for radiologists; hence, it is very important to implement an automated method. As a result, the precise brain tumor detection and classification method is presented. Materials and Methods: The proposed method has five steps. In the first step, a linear contrast stretching is used to determine the edges in the source image. In the second step, a custom 17-layered deep neural network architecture is developed for the segmentation of brain tumors. In the third step, a modified MobileNetV2 architecture is used for feature extraction and is trained using transfer learning. In the fourth step, an entropy-based controlled method was used along with a multiclass support vector machine (M-SVM) for the best features selection. In the final step, M-SVM is used for brain tumor classification, which identifies the meningioma, glioma and pituitary images. Results: The proposed method was demonstrated on BraTS 2018 and Figshare datasets. Experimental study shows that the proposed brain tumor detection and classification method outperforms other methods both visually and quantitatively, obtaining an accuracy of 97.47% and 98.92%, respectively. Finally, we adopt the eXplainable Artificial Intelligence (XAI) method to explain the result. Conclusions: Our proposed approach for brain tumor detection and classification has outperformed prior methods. These findings demonstrate that the proposed approach obtained higher performance in terms of both visually and enhanced quantitative evaluation with improved accuracy. Full article
(This article belongs to the Special Issue Advances of Artificial Intelligence in the Management of Patients with Central Nervous System Tumors)
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Review

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17 pages, 2326 KiB  
Review
Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review
by Sergio García-García, Manuel García-Galindo, Ignacio Arrese, Rosario Sarabia and Santiago Cepeda
Medicina 2022, 58(12), 1746; https://doi.org/10.3390/medicina58121746 - 29 Nov 2022
Cited by 2 | Viewed by 1750
Abstract
Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence [...] Read more.
Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor’s biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM. Full article
(This article belongs to the Special Issue Advances of Artificial Intelligence in the Management of Patients with Central Nervous System Tumors)
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