Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Kalasauskas, Darius; Kosterhon, Michael; Keric, Naureen; Korczynski, Oliver; Kronfeld, Andrea; Ringel, Florian; Othman, Ahmed E.; Brockmann, Marc A.

doi:10.3390/cancers14030836

Cited by 5 publications

(2 citation statements)

References 139 publications

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“…The first one is typically localized in the posterior cranial fossa and, in adults, they could develop in both hemispheres of the cerebellum, while in the pediatric age group, they are often found in the cerebellar worm [ 19 ].…”

Section: Primary Brain Tumors Hallmarks and Canonical Therapiesmentioning

confidence: 99%

Recent Emerging Immunological Treatments for Primary Brain Tumors: Focus on Chemokine-Targeting Immunotherapies

Ardizzone

Basilotta

Filippone

et al. 2023

Cells

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Primary brain tumors are a leading cause of death worldwide and are characterized by extraordinary heterogeneity and high invasiveness. Current drug and radiotherapy therapies combined with surgical approaches tend to increase the five-year survival of affected patients, however, the overall mortality rate remains high, thus constituting a clinical challenge for which the discovery of new therapeutic strategies is needed. In this field, novel immunotherapy approaches, aimed at overcoming the complex immunosuppressive microenvironment, could represent a new method of treatment for central nervous system (CNS) tumors. Chemokines especially are a well-defined group of proteins that were so named due to their chemotactic properties of binding their receptors. Chemokines regulate the recruitment and/or tissue retention of immune cells as well as the mobilization of tumor cells that have undergone epithelial–mesenchymal transition, promoting tumor growth. On this basis, this review focuses on the function and involvement of chemokines and their receptors in primary brain tumors, specifically examining chemokine-targeting immunotherapies as one of the most promising strategies in neuro-oncology.

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Section: Primary Brain Tumors Hallmarks and Canonical Therapiesmentioning

confidence: 99%

Recent Emerging Immunological Treatments for Primary Brain Tumors: Focus on Chemokine-Targeting Immunotherapies

Ardizzone

Basilotta

Filippone

et al. 2023

Cells

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“…Numerous studies have demonstrated that radiomics exhibit superior diagnostic capabilities compared to visual analysis in the diagnosis, classification, and outcome prediction of brain lesions (Rudie et al 2019 ; Abdel Razek et al 2021 ; Forghani 2020 ; Kalasauskas et al 2022 ). Radiomics has the potential to better differentiate cerebral cystic metastases and abscesses.…”

Section: Introductionmentioning

confidence: 99%

Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses

Cui,

Qin,

Sun

et al. 2024

J Cancer Res Clin Oncol

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Objectives To develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses. Materials and methods A total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model’s performance. Results Seven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity. Conclusion The DWI-based radiomics model established by TPOT exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.

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Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?

Martín-Noguerol,

López-Úbeda,

Pons-Escoda

et al. 2023

Eur Radiol

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Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Cited by 5 publications

References 139 publications

Recent Emerging Immunological Treatments for Primary Brain Tumors: Focus on Chemokine-Targeting Immunotherapies

Recent Emerging Immunological Treatments for Primary Brain Tumors: Focus on Chemokine-Targeting Immunotherapies

Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses

Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?

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