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Integrating Imaging AI into Glioma Clinical Workflows: Deployment, Technical Challenges...
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Integrating Imaging AI into Glioma Clinical Workflows: Deployment, Technical Challenges and Real-World Impact

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1012

Special Issue Editors

Prof. Dr. Sotirios Bisdas

E-Mail Website
Guest Editor
1. Department of Translational Neuroscience and Stroke, University College London, London WC1N 3BG, UK
2. School of Medicine, European University Cyprus, 1516 Nicosia, Cyprus
Interests: neuro-oncology; neurodegeneration; neuroinflammation; glioma
Special Issues, Collections and Topics in MDPI journals
Dr. Loizos Siakallis

E-Mail Website
Guest Editor Assistant
Department of Translational Neuroscience and Stroke, University College London, London WC1N 3BG, UK
Interests: neuroradiology; neuroimaging; cluster headache; sphenopalatine ganglion block; nerve block; endocrine ophthalmopathy; eye diseases; exophthalmos; 2-(4′-(methylamino)phenyl)-6-hydroxybenzothiazole; Alzheimer’s disease; dementia

Special Issue Information

Dear Colleagues,

Despite significant advances in machine-learning approaches to imaging analysis, their integration into everyday clinical workflows remains limited. The focus of this Special Issue is the practical integration of imaging AI tools into real-world clinical workflows for gliomas. Such applications encompass a range of clinical utilities, from diagnosis and classification to prognostication, surgical planning and radiation oncology. Relevant studies would focus on the deployment of AI tools in real clinical workflow and hospital environments, addressing technical challenges such as software interoperability, workflow integration and clinician adoption. Emphasis is placed on demonstrated clinical impact: for example, AI-assisted tumor segmentations embedded in PACS to streamline radiology workflows or AI-guided tools that enhance surgical resection and radiotherapy planning. Such implementations illustrate how integrating AI can improve efficiency and decision-making, ultimately leading to improved patient outcomes in neuro-oncologic care.

Prof. Dr. Sotirios Bisdas
Guest Editor

Dr. Loizos Siakallis
Guest Editor Assistant

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 communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Cancers 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 2900 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

  • gliomas
  • glioblastomas
  • imaging
  • MRI
  • CT
  • PET
  • AI
  • clinical workflow integration
  • neuro-oncologic care
  • radiation therapy

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Published Papers (1 paper)

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Research

38 pages, 15512 KB  
Article
Improving Brain Tumor Detection by Cortical Surface and Vessels Segmentation Through RGB-to-HSI Transfer Learning
by Guillermo Vazquez, Alberto Martín-Pérez, Angel Perez-Nuñez, Alfonso Lagares, Eduardo Juarez and Cesar Sanz
Cancers 2026, 18(5), 857; https://doi.org/10.3390/cancers18050857 - 6 Mar 2026
Viewed by 700
Abstract
Background: Accurate in vivo brain tumor detection using hyperspectral imaging (HSI), a non-invasive technique that captures spectral information beyond the visible range, is challenging due to the complexity of biological tissues and the difficulty in distinguishing malignant from healthy areas. Conventional neural-network-based [...] Read more.
Background: Accurate in vivo brain tumor detection using hyperspectral imaging (HSI), a non-invasive technique that captures spectral information beyond the visible range, is challenging due to the complexity of biological tissues and the difficulty in distinguishing malignant from healthy areas. Conventional neural-network-based methods often misclassify tumor tissue as blood vessels, largely due to high vascularization and the scarcity of annotated data. Method: To address this issue, this work proposes an underexplored approach that decomposes the problem into two tasks: (1) segmentation of the brain cortical surface and its blood vessels, and (2) segmentation of biological tissues within the segmented craniotomy site. The cortical segmentation task is addressed independently of the segmentation model used in the second stage. To achieve this, a set of pseudo-labels is generated from RGB and HSI captures acquired during in vivo brain surgeries. These pseudo-labels support a multimodal training strategy that leverages both imaging domains, yielding a model capable of segmenting the craniotomy site and the blood vessels contained in it. The model is further refined on HSI using weakly supervised fine-tuning with sparse ground truth annotations. Results: The final segmentation map combines cortical and tissue segmentation outputs, considering only cortex pixels not overlapped by vessels as potential tumor regions. This simplifies the HSI tissue segmentation task, reframing it as a binary segmentation of healthy vs. other tissues, while still enabling a comprehensive multiclass output. Conclusions: The proposed method achieves up to a 15.48% increase in F1 score for the tumor class, while segmenting the brain cortex with a mean Dice similarity coefficient (DSC) of 92.08% and accurately detecting 95.42% of labeled blood vessel samples in the HSI dataset. Full article
(This article belongs to the Special Issue Integrating Imaging AI into Glioma Clinical Workflows: Deployment, Technical Challenges and Real-World Impact)
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