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Cancers
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Clinical and Experimental Application of Artificial Intelligence in Neuro-Oncology Imaging
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Clinical and Experimental Application of Artificial Intelligence in Neuro-Oncology Imaging

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 1528

Special Issue Editors

Prof. Dr. Manoj Mannil

E-Mail Website
Guest Editor
1. Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany
2. Institute for Diagnostic and Interventional Radiology, Caritas-Hospital, DE-97980 Bad Mergentheim, Germany
Interests: radiomics; neuroradiology; artificial intelligence
Dr. Burak H. Akkurt

E-Mail Website
Guest Editor
Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany
Interests: artificial intelligence; machine learning; deep learning; radiomics; neuro-oncology; glioma; meningioma
Dr. Hermann Krähling

E-Mail Website
Guest Editor
Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany
Interests: artificial intelligence; machine learning; deep learning; radiomics; neuro-oncology; glioma; meningioma

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has the potential to make an enormous impact on medicine, especially in medical imaging. Although, until now, there have only been a few AI applications that have received approval for clinical use, various areas of research, including but not limited to image acquisition and processing and image interpretation tasks, are under investigation.

Since, according to the definition of radiomics, radiological data contain more information than is visible to human perception, novel AI methods are necessary to achieve deeper image analysis based on multidimensional image features. This Special Issue focuses on experimental and clinical applications of AI methods in neuro-oncology imaging and aims at collecting original research, reviews, meta-analyses, and letters analyzing the value of AI applications and their potential clinical implementation and discussing the pros and cons of advanced, computer-aided solutions in radiology. Thank you for considering publishing in this Special Issue.

Prof. Dr. Manoj Mannil
Dr. Burak H. Akkurt
Dr. Hermann Krähling
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 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. 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

  • artificial intelligence
  • machine learning
  • deep learning
  • radiomics
  • neuro-oncology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 5630 KiB  
Article
Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients
by Mart Wubbels, Marvin Ribeiro, Jelmer M. Wolterink, Wouter van Elmpt, Inge Compter, David Hofstede, Nikolina E. Birimac, Femke Vaassen, Kati Palmgren, Hendrik H. G. Hansen, Hiska L. van der Weide, Charlotte L. Brouwer, Miranda C. A. Kramer, Daniëlle B. P. Eekers and Catharina M. L. Zegers
Cancers 2025, 17(10), 1598; https://doi.org/10.3390/cancers17101598 - 8 May 2025
Abstract
Purpose: This study aims to create a deep learning (DL) model capable of accurately delineating the ventricles, and by extension, the periventricular space (PVS), following the 2021 EPTN Neuro-Oncology Atlas guidelines on T1-weighted contrast-enhanced MRI scans (T1CE). The performance of this DL model [...] Read more.
Purpose: This study aims to create a deep learning (DL) model capable of accurately delineating the ventricles, and by extension, the periventricular space (PVS), following the 2021 EPTN Neuro-Oncology Atlas guidelines on T1-weighted contrast-enhanced MRI scans (T1CE). The performance of this DL model was quantitatively and qualitatively compared with an off-the-shelf model. Materials and Methods: An nnU-Net was trained for ventricle segmentation using both CT and T1CE MRI images from 78 patients. Its performance was compared to that of a publicly available pretrained segmentation model, SynthSeg. The evaluation was conducted on both internal (N = 18) and external (n = 18) test sets, with each consisting of paired CT and T1CE MRI images and expert-delineated ground truths (GTs). Segmentation accuracy was assessed using the volumetric Dice Similarity Coefficient (DSC), 95th percentile Hausdorff distance (HD95), surface DSC, and added path length (APL). Additionally, a local evaluation of ventricle segmentations quantified differences between manual and automatic segmentations across both test sets. All segmentations were scored by radiotherapy technicians for clinical acceptability using a 4-point Likert scale. Results: The nnU-Net significantly outperformed the SynthSeg model on the internal test dataset in terms of median [range] DSC, 0.93 [0.86–0.95] vs. 0.85 [0.67–0.91], HD95, 0.9 [0.7–2.5] mm vs. 2.2 [1.7–4.8] mm, surface DSC, 0.97 [0.90–0.98] vs. 0.84 [0.70–0.89], and APL, 876 [407–1298] mm vs. 2809 [2311–3622] mm, all with p < 0.001. No significant differences in these metrics were found in the external test set. However clinical ratings favored nnU-Net segmentations on the internal and external test sets. In addition, the nnU-Net had higher clinical ratings than the GT delineation on the internal and external test set. Conclusions: The nnU-Net model outperformed the SynthSeg model on the internal dataset in both segmentation metrics and clinician ratings. While segmentation metrics showed no significant differences between the models on the external set, clinician ratings favored nnU-Net, suggesting enhanced clinical acceptability. This suggests that nnU-Net could contribute to more time-efficient and streamlined radiotherapy planning workflows. Full article
(This article belongs to the Special Issue Clinical and Experimental Application of Artificial Intelligence in Neuro-Oncology Imaging)
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15 pages, 3043 KiB  
Article
Analysis of the Predictability of Postoperative Meningioma Resection Status Based on Clinical Features
by Manfred Musigmann, Burak Han Akkurt, Hermann Krähling, Benjamin Brokinkel, Dorothee Cäcilia Spille, Walter Stummer, Walter Heindel and Manoj Mannil
Cancers 2024, 16(22), 3751; https://doi.org/10.3390/cancers16223751 - 6 Nov 2024
Viewed by 860
Abstract
Background: Our aim was to investigate the predictability of postoperative meningioma resection status based on clinical features. Methods: We examined 23 clinical features to assess their effectiveness in distinguishing gross total resections (GTR) from subtotal resections (STR). We analyzed whether GTR/STR cases are [...] Read more.
Background: Our aim was to investigate the predictability of postoperative meningioma resection status based on clinical features. Methods: We examined 23 clinical features to assess their effectiveness in distinguishing gross total resections (GTR) from subtotal resections (STR). We analyzed whether GTR/STR cases are better predictable if the classification is based on the Simpson grading or the postoperative operative tumor volume (POTV). Results: Using a study cohort comprising a total of 157 patients, multivariate models for the preoperative prediction of GTR/STR outcome in relation to Simpson grading and POTV were developed and subsequently compared. Including only two clinical features, our models showed a notable discriminatory power in predicting postoperative resection status. Our final model, a straightforward decision tree applicable in daily clinical practice, achieved a mean AUC of 0.885, a mean accuracy of 0.866, a mean sensitivity of 0.889, and a mean specificity of 0.772 based on independent test data. Conclusions: Such models can be a valuable tool both for surgical planning and for early planning of postoperative treatment, e.g., for additional radiotherapy/radiosurgery, potentially required in case of subtotal resections. Full article
(This article belongs to the Special Issue Clinical and Experimental Application of Artificial Intelligence in Neuro-Oncology Imaging)
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