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Cancers
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Radiomics in Brain Tumor Imaging
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Radiomics in Brain Tumor Imaging

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 12610

Special Issue Editors

Dr. Julia Furtner

E-Mail Website
Guest Editor
Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
Interests: neuroimaging; radiomics/radiogenomics in context of brain tumors; MRI; PET; PET/MRI
Special Issues, Collections and Topics in MDPI journals
Dr. Georg Langs

E-Mail Website
Guest Editor
Medizinische Universitat Wien, Vienna, Austria
Interests: Machine learning; Medical image analysis; Functional brain imaging/perception/reorganization

Special Issue Information

Dear Colleagues, 

Brain tumors have a profound impact on a patient`s quality of life and on the healthcare system due to the substantial level of disability already apparent in the early stages of disease, which poses a major medical and socio-economical burden. Magnetic resonance (MR) imaging and positron emission tomography (PET) are the most commonly used techniques to diagnose and provide follow-up of brain tumors. The interpretation of these images is currently based on qualitative image interpretation. Radiomics provides a quantitative way of image analysis in which the purpose is to link radiological features with clinical information, patient’s outcome and treatment response assessment, as well as cellular or molecular tumor properties, and thus derive additional information about the entire tumor volume from routinely assessed non-invasive imaging techniques.

This special issue of Cancers, with a focus on “Radiomics in Brain Tumors,” aims to highlight the advantages of applying radiomics to MR or PET images to provide novel insights with regard to tumor detection, diagnosis, molecular profiling, prognosis, or prediction of response to tumor treatment in primary or secondary brain tumors.

Dr. Julia Furtner
Dr. Georg Langs
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

  • radiomics
  • radiogenomics
  • machine-learning
  • brain tumors
  • neuro-oncology
  • MRI
  • PET
  • PET/MRI

Published Papers (4 papers)

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Research

13 pages, 1806 KiB  
Article
Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases
by Chien-Yi Liao, Cheng-Chia Lee, Huai-Che Yang, Ching-Jen Chen, Wen-Yuh Chung, Hsiu-Mei Wu, Wan-Yuo Guo, Ren-Shyan Liu and Chia-Feng Lu
Cancers 2021, 13(16), 4030; https://doi.org/10.3390/cancers13164030 - 10 Aug 2021
Cited by 20 | Viewed by 2777
Abstract
The diagnosis of brain metastasis (BM) is commonly observed in non-small cell lung cancer (NSCLC) with poor outcomes. Accordingly, developing an approach to early predict BM response to Gamma Knife radiosurgery (GKRS) may benefit the patient treatment and monitoring. A total of 237 [...] Read more.
The diagnosis of brain metastasis (BM) is commonly observed in non-small cell lung cancer (NSCLC) with poor outcomes. Accordingly, developing an approach to early predict BM response to Gamma Knife radiosurgery (GKRS) may benefit the patient treatment and monitoring. A total of 237 NSCLC patients with BMs (for survival prediction) and 256 patients with 976 BMs (for prediction of local tumor control) treated with GKRS were retrospectively analyzed. All the survival data were recorded without censoring, and the status of local tumor control was determined by comparing the last MRI follow-up in patients’ lives with the pre-GKRS MRI. Overall 1763 radiomic features were extracted from pre-radiosurgical magnetic resonance images. Three prediction models were constructed, using (1) clinical data, (2) radiomic features, and (3) clinical and radiomic features. Support vector machines with a 30% hold-out validation approach were constructed. For treatment outcome predictions, the models derived from both the clinical and radiomics data achieved the best results. For local tumor control, the combined model achieved an area under the curve (AUC) of 0.95, an accuracy of 90%, a sensitivity of 91%, and a specificity of 89%. For patient survival, the combined model achieved an AUC of 0.81, an accuracy of 77%, a sensitivity of 78%, and a specificity of 80%. The pre-radiosurgical radiomics data enhanced the performance of local tumor control and survival prediction models in NSCLC patients with BMs treated with GRKS. An outcome prediction model based on radiomics combined with clinical features may guide therapy in these patients. Full article
(This article belongs to the Special Issue Radiomics in Brain Tumor Imaging)
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16 pages, 5486 KiB  
Article
Multicenter DSC–MRI-Based Radiomics Predict IDH Mutation in Gliomas
by Georgios C. Manikis, Georgios S. Ioannidis, Loizos Siakallis, Katerina Nikiforaki, Michael Iv, Diana Vozlic, Katarina Surlan-Popovic, Max Wintermark, Sotirios Bisdas and Kostas Marias
Cancers 2021, 13(16), 3965; https://doi.org/10.3390/cancers13163965 - 05 Aug 2021
Cited by 25 | Viewed by 3125
Abstract
To address the current lack of dynamic susceptibility contrast magnetic resonance imaging (DSC–MRI)-based radiomics to predict isocitrate dehydrogenase (IDH) mutations in gliomas, we present a multicenter study that featured an independent exploratory set for radiomics model development and external validation using two independent [...] Read more.
To address the current lack of dynamic susceptibility contrast magnetic resonance imaging (DSC–MRI)-based radiomics to predict isocitrate dehydrogenase (IDH) mutations in gliomas, we present a multicenter study that featured an independent exploratory set for radiomics model development and external validation using two independent cohorts. The maximum performance of the IDH mutation status prediction on the validation set had an accuracy of 0.544 (Cohen’s kappa: 0.145, F1-score: 0.415, area under the curve-AUC: 0.639, sensitivity: 0.733, specificity: 0.491), which significantly improved to an accuracy of 0.706 (Cohen’s kappa: 0.282, F1-score: 0.474, AUC: 0.667, sensitivity: 0.6, specificity: 0.736) when dynamic-based standardization of the images was performed prior to the radiomics. Model explainability using local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) revealed potential intuitive correlations between the IDH–wildtype increased heterogeneity and the texture complexity. These results strengthened our hypothesis that DSC–MRI radiogenomics in gliomas hold the potential to provide increased predictive performance from models that generalize well and provide understandable patterns between IDH mutation status and the extracted features toward enabling the clinical translation of radiogenomics in neuro-oncology. Full article
(This article belongs to the Special Issue Radiomics in Brain Tumor Imaging)
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17 pages, 4533 KiB  
Article
Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?
by Sarv Priya, Yanan Liu, Caitlin Ward, Nam H. Le, Neetu Soni, Ravishankar Pillenahalli Maheshwarappa, Varun Monga, Honghai Zhang, Milan Sonka and Girish Bathla
Cancers 2021, 13(11), 2568; https://doi.org/10.3390/cancers13112568 - 24 May 2021
Cited by 14 | Viewed by 2453
Abstract
Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model [...] Read more.
Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311–0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods. Full article
(This article belongs to the Special Issue Radiomics in Brain Tumor Imaging)
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15 pages, 5342 KiB  
Article
Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients
by Robin Gutsche, Jürgen Scheins, Martin Kocher, Khaled Bousabarah, Gereon R. Fink, Nadim J. Shah, Karl-Josef Langen, Norbert Galldiks and Philipp Lohmann
Cancers 2021, 13(4), 647; https://doi.org/10.3390/cancers13040647 - 05 Feb 2021
Cited by 16 | Viewed by 3085
Abstract
Amino acid PET using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) has attracted considerable interest in neurooncology. Furthermore, initial studies suggested the additional diagnostic value of FET PET radiomics in brain tumor patient management. However, the conclusiveness of radiomics models strongly depends on feature [...] Read more.
Amino acid PET using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) has attracted considerable interest in neurooncology. Furthermore, initial studies suggested the additional diagnostic value of FET PET radiomics in brain tumor patient management. However, the conclusiveness of radiomics models strongly depends on feature generalizability. We here evaluated the repeatability of feature-based FET PET radiomics. A test–retest analysis based on equivalent but statistically independent subsamples of FET PET images was performed in 50 newly diagnosed and histomolecularly characterized glioma patients. A total of 1,302 radiomics features were calculated from semi-automatically segmented tumor volumes-of-interest (VOIs). Furthermore, to investigate the influence of the spatial resolution of PET on repeatability, spherical VOIs of different sizes were positioned in the tumor and healthy brain tissue. Feature repeatability was assessed by calculating the intraclass correlation coefficient (ICC). To further investigate the influence of the isocitrate dehydrogenase (IDH) genotype on feature repeatability, a hierarchical cluster analysis was performed. For tumor VOIs, 73% of first-order features and 71% of features extracted from the gray level co-occurrence matrix showed high repeatability (ICC 95% confidence interval, 0.91–1.00). In the largest spherical tumor VOIs, 67% of features showed high repeatability, significantly decreasing towards smaller VOIs. The IDH genotype did not affect feature repeatability. Based on 297 repeatable features, two clusters were identified separating patients with IDH-wildtype glioma from those with an IDH mutation. Our results suggest that robust features can be obtained from routinely acquired FET PET scans, which are valuable for further standardization of radiomics analyses in neurooncology. Full article
(This article belongs to the Special Issue Radiomics in Brain Tumor Imaging)
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