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Cancers
Special Issues
Pediatric Brain Tumors: Symptoms, Diagnosis and Treatments
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Pediatric Brain Tumors: Symptoms, Diagnosis and Treatments

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

Deadline for manuscript submissions: 30 July 2024 | Viewed by 559

Special Issue Editors

Dr. Christian Ott

E-Mail Website
Guest Editor
Department of Neurosurgery, University of Regensburg, 93053 Regensburg, Germany
Interests: glioblastoma; brain tumor; neurosurgery
Dr. Julius Höhne

E-Mail Website
Guest Editor
Department of Neurosurgery, University Hospital Nuremberg, 93053 Regensburg, Germany
Interests: brain tumor; pediatric cancer; glioma

Special Issue Information

Dear Colleagues,

Pediatric brain tumors are abnormal growths of cells in a child's brain. Symptoms of these tumors can include headaches, seizures, changes in behavior, and motor skill difficulties, and are essential to ensure early diagnosis, which often involves imaging tests such as MRI or CT scans, followed by a biopsy to confirm the tumor type.

Treatment options vary depending on the tumor's type, location, and size. Common approaches include surgery to remove the tumor, radiation therapy, and chemotherapy. Treatment plans are tailored to the individual child's needs and may involve a combination of these therapies.

Parents and caregivers must work closely with a healthcare team to ensure the best possible outcome for children diagnosed with brain tumors. Early detection and advances in medical technology have improved the prognosis and quality of life for many young patients.

Dr. Christian Ott
Dr. Julius Höhne
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. 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

  • MRI/CT scans
  • radiation therapy
  • chemotherapy
  • combined therapy

Published Papers (1 paper)

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Research

12 pages, 1055 KiB  
Article
Human-Level Differentiation of Medulloblastoma from Pilocytic Astrocytoma: A Real-World Multicenter Pilot Study
by Benedikt Wiestler, Brigitte Bison, Lars Behrens, Stefanie Tüchert, Marie Metz, Michael Griessmair, Marcus Jakob, Paul-Gerhardt Schlegel, Vera Binder, Irene von Luettichau, Markus Metzler, Pascal Johann, Peter Hau and Michael Frühwald
Cancers 2024, 16(8), 1474; https://doi.org/10.3390/cancers16081474 - 11 Apr 2024
Viewed by 401
Abstract
Medulloblastoma and pilocytic astrocytoma are the two most common pediatric brain tumors with overlapping imaging features. In this proof-of-concept study, we investigated using a deep learning classifier trained on a multicenter data set to differentiate these tumor types. We developed a patch-based 3D-DenseNet [...] Read more.
Medulloblastoma and pilocytic astrocytoma are the two most common pediatric brain tumors with overlapping imaging features. In this proof-of-concept study, we investigated using a deep learning classifier trained on a multicenter data set to differentiate these tumor types. We developed a patch-based 3D-DenseNet classifier, utilizing automated tumor segmentation. Given the heterogeneity of imaging data (and available sequences), we used all individually available preoperative imaging sequences to make the model robust to varying input. We compared the classifier to diagnostic assessments by five readers with varying experience in pediatric brain tumors. Overall, we included 195 preoperative MRIs from children with medulloblastoma (n = 69) or pilocytic astrocytoma (n = 126) across six university hospitals. In the 64-patient test set, the DenseNet classifier achieved a high AUC of 0.986, correctly predicting 62/64 (97%) diagnoses. It misclassified one case of each tumor type. Human reader accuracy ranged from 100% (expert neuroradiologist) to 80% (resident). The classifier performed significantly better than relatively inexperienced readers (p < 0.05) and was on par with pediatric neuro-oncology experts. Our proof-of-concept study demonstrates a deep learning model based on automated tumor segmentation that can reliably preoperatively differentiate between medulloblastoma and pilocytic astrocytoma, even in heterogeneous data. Full article
(This article belongs to the Special Issue Pediatric Brain Tumors: Symptoms, Diagnosis and Treatments)
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