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Cancers
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Development, Validation and Application of Advanced Biomarkers in Cerebral Tumours
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Development, Validation and Application of Advanced Biomarkers in Cerebral Tumours

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Biomarkers".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3552

Special Issue Editor

Prof. Dr. Alan Jackson

E-Mail Website
Guest Editor
Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
Interests: neuroradiology

Special Issue Information

Dear Colleagues,

Over the past 20 years the concept of the biomarker has become intrinsic to medical research. The identification of individual quantifiable features, related to tumour biology, tumour behaviour, prognosis and treatment response has been one of the largest areas of original research. Following the discovery of potentially novel biomarkers extensive research is needed to confirm their potential validity, validate their reproducibility and optimal methodology for measurement and to then explore and confirm their potential biological meaning. Biomarkers may be based on chemical, genetic or biological features, may be extracted from data such as liquid biopsy, genome and gene expression data, imaging data, mass spectroscopy, proteomics or metabolomics. Whatever the methodology for individual biomarkers they must be robust, reproducible and provide valuable information concerning tumour biology, behaviour, prognosis, treatment response or some other aspect of tumour behaviour.

This Special Issue invites submissions related to the discovery, validation and application of novel biomarkers for the study and clinical management of intra-cerebral tumours. The editorial team will consider applications relating to discovery of potential biomarkers, validation studies of existing biomarkers and studies providing clinical validation or evidence of application of relatively novel biomarkers.

We will also consider applications that describe novel data classification or artificial intelligence applications, which might optimise the use and application of existing biomarkers.

Prof. Dr. Alan Jackson
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 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.

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

Published Papers (3 papers)

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Research

12 pages, 1620 KiB  
Article
Deep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS)
by Santiago Cepeda, Olga Esteban-Sinovas, Vikas Singh, Prakash Shetty, Aliasgar Moiyadi, Luke Dixon, Alistair Weld, Giulio Anichini, Stamatia Giannarou, Sophie Camp, Ilyess Zemmoura, Giuseppe Roberto Giammalva, Massimiliano Del Bene, Arianna Barbotti, Francesco DiMeco, Timothy Richard West, Brian Vala Nahed, Roberto Romero, Ignacio Arrese, Roberto Hornero and Rosario Sarabiaadd Show full author list remove Hide full author list
Cancers 2025, 17(2), 315; https://doi.org/10.3390/cancers17020315 - 19 Jan 2025
Viewed by 1217
Abstract
Background: Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and [...] Read more.
Background: Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset. Methods: We retrospectively collected data from the BraTioUS and ReMIND datasets, including histologically confirmed gliomas with high-quality B-mode images. For each patient, the tumor was manually segmented on the 2D slice with its largest diameter. A CNN was trained using the nnU-Net framework. The dataset was stratified by center and divided into training (70%) and testing (30%) subsets, with external validation performed on two independent cohorts: the RESECT-SEG database and the Imperial College NHS Trust London cohort. Performance was evaluated using metrics such as the Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and 95th percentile Hausdorff distance (HD95). Results: The training cohort consisted of 197 subjects, 56 of whom were in the hold-out testing set and 53 in the external validation cohort. In the hold-out testing set, the model achieved a median DSC of 0.90, ASSD of 8.51, and HD95 of 29.08. On external validation, the model achieved a DSC of 0.65, ASSD of 14.14, and HD95 of 44.02 on the RESECT-SEG database and a DSC of 0.93, ASSD of 8.58, and HD95 of 28.81 on the Imperial-NHS cohort. Conclusions: This study supports the feasibility of CNN-based glioma segmentation in ioUS across multiple centers. Future work should enhance segmentation detail and explore real-time clinical implementation, potentially expanding ioUS’s role in neurosurgical resection. Full article
(This article belongs to the Special Issue Development, Validation and Application of Advanced Biomarkers in Cerebral Tumours)
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14 pages, 3304 KiB  
Article
Prognostic Modeling of Overall Survival in Glioblastoma Using Radiomic Features Derived from Intraoperative Ultrasound: A Multi-Institutional Study
by Santiago Cepeda, Olga Esteban-Sinovas, Vikas Singh, Aliasgar Moiyadi, Ilyess Zemmoura, Massimiliano Del Bene, Arianna Barbotti, Francesco DiMeco, Timothy Richard West, Brian Vala Nahed, Giuseppe Roberto Giammalva, Ignacio Arrese and Rosario Sarabia
Cancers 2025, 17(2), 280; https://doi.org/10.3390/cancers17020280 - 16 Jan 2025
Viewed by 752
Abstract
Background: Accurate prognostic models are essential for optimizing treatment strategies for glioblastoma, the most aggressive primary brain tumor. While other neuroimaging modalities have demonstrated utility in predicting overall survival (OS), intraoperative ultrasound (iUS) remains underexplored for this purpose. This study aimed to evaluate [...] Read more.
Background: Accurate prognostic models are essential for optimizing treatment strategies for glioblastoma, the most aggressive primary brain tumor. While other neuroimaging modalities have demonstrated utility in predicting overall survival (OS), intraoperative ultrasound (iUS) remains underexplored for this purpose. This study aimed to evaluate the prognostic potential of iUS radiomics in glioblastoma patients in a multi-institutional cohort. Methods: This retrospective study included patients diagnosed with glioblastoma from the multicenter Brain Tumor Intraoperative (BraTioUS) database. A single 2D iUS slice, showing the largest tumor diameter, was selected for each patient. Radiomic features were extracted and subjected to feature selection, and clinical data were collected. Using a fivefold cross-validation strategy, Cox proportional hazards models were built using radiomic features alone, clinical data alone, and their combination. Model performance was assessed via the concordance index (C-index). Results: A total of 114 patients met the inclusion criteria, with a mean age of 56.88 years, a median OS of 382 days, and a median preoperative tumor volume of 32.69 cm3. Complete tumor resection was achieved in 51.8% of the patients. In the testing cohort, the combined model achieved a mean C-index of 0.87 (95% CI: 0.76–0.98), outperforming the radiomic model (C-index: 0.72, 95% CI: 0.57–0.86) and the clinical model (C-index: 0.73, 95% CI: 0.60–0.87). Conclusions: Intraoperative ultrasound relies on acoustic properties for tissue characterization, capturing unique features of glioblastomas. This study demonstrated that radiomic features derived from this imaging modality have the potential to support the development of survival models. Full article
(This article belongs to the Special Issue Development, Validation and Application of Advanced Biomarkers in Cerebral Tumours)
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15 pages, 3222 KiB  
Article
An Injury-like Signature of the Extracellular Glioma Metabolome
by Yooree Ha, Karishma Rajani, Cecile Riviere-Cazaux, Masum Rahman, Ian E. Olson, Ali Gharibi Loron, Mark A. Schroeder, Moses Rodriguez, Arthur E. Warrington and Terry C. Burns
Cancers 2024, 16(15), 2705; https://doi.org/10.3390/cancers16152705 - 30 Jul 2024
Cited by 1 | Viewed by 1063
Abstract
Aberrant metabolism is a hallmark of malignancies including gliomas. Intracranial microdialysis enables the longitudinal collection of extracellular metabolites within CNS tissues including gliomas and can be leveraged to evaluate changes in the CNS microenvironment over a period of days. However, delayed metabolic impacts [...] Read more.
Aberrant metabolism is a hallmark of malignancies including gliomas. Intracranial microdialysis enables the longitudinal collection of extracellular metabolites within CNS tissues including gliomas and can be leveraged to evaluate changes in the CNS microenvironment over a period of days. However, delayed metabolic impacts of CNS injury from catheter placement could represent an important covariate for interpreting the pharmacodynamic impacts of candidate therapies. Intracranial microdialysis was performed in patient-derived glioma xenografts of glioma before and 72 h after systemic treatment with either temozolomide (TMZ) or a vehicle. Microdialysate from GBM164, an IDH-mutant glioma patient-derived xenograft, revealed a distinct metabolic signature relative to the brain that recapitulated the metabolic features observed in human glioma microdialysate. Unexpectedly, catheter insertion into the brains of non-tumor-bearing animals triggered metabolic changes that were significantly enriched for the extracellular metabolome of glioma itself. TMZ administration attenuated this resemblance. The human glioma microdialysate was significantly enriched for both the PDX versus brain signature in mice and the induced metabolome of catheter placement within the murine control brain. These data illustrate the feasibility of microdialysis to identify and monitor the extracellular metabolome of diseased versus relatively normal brains while highlighting the similarity between the extracellular metabolome of human gliomas and that of CNS injury. Full article
(This article belongs to the Special Issue Development, Validation and Application of Advanced Biomarkers in Cerebral Tumours)
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