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Artificial Intelligence in Brain Cancer
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Artificial Intelligence in Brain Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2593

Special Issue Editor

Dr. Andra Valentina Krauze

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Guest Editor
Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
Interests: rare tumors; data science and computational biology; brain and spine cancer; head and neck cancer; genitourinary tumors; re-irradiation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain malignancies are comprised of primary brain tumors that arise in the brain and secondary tumors that arise elsewhere in the body and metastasize to the brain. Both types of lesions are associated with devastating neurological symptoms and often poor prognosis. While not all brain malignancies are associated with poor survival, they are all often associated with significant life-altering neurological symptoms and sequelae that are the result of alterations in the functionality of normal tissues either due to tumor presence or the side effects of disease management, be it surgical intervention, systemic management or radiation therapy. Artificial intelligence methods have been applied to malignancies of the brain with respect to diagnosis, management and prognosis and have aimed to leverage imaging and pathology features as the most common sources of data available. The progress in large-scale sophisticated omic data and the rise of real-world clinical data have added a new dimension of data depth and the integration and interpretable application of artificial intelligence to these domains. In-parallel novel imaging and pathology techniques have revolutionized the diagnosis of brain malignancy in terms of molecular classification. Nonetheless, very few patients benefit from personalized treatment as a result of these advances, with management in clinics largely unchanged. The integration of data types that address imaging, pathology, molecular and clinical data is now the new frontier, with the aim being to improve, validate and advance diagnosis, management and outcomes in brain malignancies.

Dr. Andra Valentina Krauze
Guest Editor

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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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.

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Published Papers (2 papers)

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17 pages, 1379 KiB  
Article
Diagnosing Progression in Glioblastoma—Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma
by Mason J. Belue, Stephanie A. Harmon, Shreya Chappidi, Ying Zhuge, Erdal Tasci, Sarisha Jagasia, Thomas Joyce, Kevin Camphausen, Baris Turkbey and Andra V. Krauze
Diagnostics 2024, 14(13), 1374; https://doi.org/10.3390/diagnostics14131374 - 28 Jun 2024
Cited by 1 | Viewed by 1239
Abstract
Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an [...] Read more.
Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology. Full article
(This article belongs to the Special Issue Artificial Intelligence in Brain Cancer)
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18 pages, 2838 KiB  
Systematic Review
Technological Frontiers in Brain Cancer: A Systematic Review and Meta-Analysis of Hyperspectral Imaging in Computer-Aided Diagnosis Systems
by Joseph-Hang Leung, Riya Karmakar, Arvind Mukundan, Wen-Shou Lin, Fathima Anwar and Hsiang-Chen Wang
Diagnostics 2024, 14(17), 1888; https://doi.org/10.3390/diagnostics14171888 - 28 Aug 2024
Viewed by 853
Abstract
Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with [...] Read more.
Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks’ funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability. Full article
(This article belongs to the Special Issue Artificial Intelligence in Brain Cancer)
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