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1. A novel paper MAP method for rapid high resolution histological analysis.

Author

Lee M, Woo J, Kim DH, Yang YM, Lee EY, Kim JH, Kang SG, Shim JK, and Park JY

Subjects
Alzheimer Disease metabolism, Animals, Apoptosis, Brain Neoplasms metabolism, Cell Proliferation, Glioblastoma metabolism, Humans, Male, Mice, Mice, Nude, Microscopy, Confocal, Proteome analysis, Spinal Cord Injuries metabolism, Tumor Cells, Cultured, Xenograft Model Antitumor Assays, Alzheimer Disease pathology, Brain metabolism, Brain Neoplasms pathology, Glioblastoma pathology, Imaging, Three-Dimensional methods, Proteome metabolism, Spinal Cord Injuries pathology
Abstract

Three-dimensional visualization of cellular and subcellular-structures in histological-tissues is essential for understanding the complexities of biological-phenomena, especially with regards structural and spatial relationships and pathologlical-diagnosis. Recent advancements in tissue-clearing technology, such as Magnified Analysis of Proteome (MAP), have significantly improved our ability to study biological-structures in three-dimensional space; however, their wide applicability to a variety of tissues is limited by long incubation-times and a need for advanced imaging-systems that are not readily available in most-laboratories. Here, we present optimized MAP-based method for paper-thin samples, Paper-MAP, which allow for rapid clearing and subsequent imaging of three-dimensional sections derived from various tissues using conventional confocal-microscopy. Paper-MAP successfully clear tissues within 1-day, compared to the original-MAP, without significant differences in achieved optical-transparency. As a proof-of-concept, we investigated the vasculature and neuronal-networks of a variety of human and rodent tissues processed via Paper-MAP, in both healthy and diseased contexts, including Alzheimer's disease and glioma., (© 2021. The Author(s).)

Published
2021
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2. Design of Treatments for Overcoming Drug Resistance in Glioblastoma Cultures with a Mathematical Model of Cellular Adaptation

Author

Pérez-Aliacar, Marina, Ayensa-Jiménez, Jacobo, Doblaré, Manuel, Tavares, João Manuel R. S., Series Editor, Jorge, Renato Natal, Series Editor, Cohen, Laurent, Editorial Board Member, Doblare, Manuel, Editorial Board Member, Frangi, Alejandro, Editorial Board Member, Garcia-Aznar, Jose Manuel, Editorial Board Member, Holzapfel, Gerhard A., Editorial Board Member, Hughes, Thomas J.R., Editorial Board Member, Kamm, Roger, Editorial Board Member, Li, Shuo, Editorial Board Member, Löhner, Rainald, Editorial Board Member, Nithiarasu, Perumal, Editorial Board Member, Oñate, Eugenio, Editorial Board Member, Perales, Francisco J., Editorial Board Member, Prendergast, Patrick J., Editorial Board Member, Tamma, Kumar K., Editorial Board Member, Vilas-Boas, Joao Paulo, Editorial Board Member, Weiss, Jeffrey, Editorial Board Member, Zhang, Yongjie Jessica, Editorial Board Member, Skalli, Wafa, editor, Laporte, Sébastien, editor, and Benoit, Aurélie, editor

Published
2024
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3. 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors

Author

Abderezaei, Javid, Pionteck, Aymeric, Chopra, Agamdeep, Kurt, Mehmet, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bakas, Spyridon, editor, Crimi, Alessandro, editor, Baid, Ujjwal, editor, Malec, Sylwia, editor, Pytlarz, Monika, editor, Baheti, Bhakti, editor, Zenk, Maximilian, editor, and Dorent, Reuben, editor

Published
2023
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4. Brain Tumor Segmentation Using Neural Ordinary Differential Equations with UNet-Context Encoding Network

Author

Sadique, M. S., Rahman, M. M., Farzana, W., Temtam, A., Iftekharuddin, K. M., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bakas, Spyridon, editor, Crimi, Alessandro, editor, Baid, Ujjwal, editor, Malec, Sylwia, editor, Pytlarz, Monika, editor, Baheti, Bhakti, editor, Zenk, Maximilian, editor, and Dorent, Reuben, editor

Published
2023
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6. Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI

Author

Zeineldin, Ramy A., Karar, Mohamed E., Mathis-Ullrich, Franziska, Burgert, Oliver, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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7. Adaptive Unsupervised Learning with Enhanced Feature Representation for Intra-tumor Partitioning and Survival Prediction for Glioblastoma

Author

Li, Yifan, Li, Chao, Wei, Yiran, Price, Stephen, Schönlieb, Carola-Bibiane, Chen, Xi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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8. Simple and Fast Convolutional Neural Network Applied to Median Cross Sections for Predicting the Presence of MGMT Promoter Methylation in FLAIR MRI Scans

Author

Chen, Daniel Tianming, Chen, Allen Tianle, Wang, Haiyan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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10. Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging Using Model Ensembling and Super-resolution

Author

Jiang, Zhifan, Zhao, Can, Liu, Xinyang, Linguraru, Marius George, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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11. Brain Tumor Segmentation Using UNet-Context Encoding Network

Author

Rahman, Md. Monibor, Sadique, Md. Shibly, Temtam, Ahmed G., Farzana, Walia, Vidyaratne, L., Iftekharuddin, Khan M., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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13. A Video Data Based Transfer Learning Approach for Classification of MGMT Status in Brain Tumor MR Images

Author

Lang, D. M., Peeken, J. C., Combs, S. E., Wilkens, J. J., Bartzsch, S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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14. Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments

Author

Thakur, Siddhesh P., Pati, Sarthak, Panchumarthy, Ravi, Karkada, Deepthi, Wu, Junwen, Kurtaev, Dmitry, Sako, Chiharu, Shah, Prashant, Bakas, Spyridon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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16. MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks

Author

Yan, Benjamin B., Wei, Yujia, Jagtap, Jaidip Manikrao M., Moassefi, Mana, Garcia, Diana V. Vera, Singh, Yashbir, Vahdati, Sanaz, Faghani, Shahriar, Erickson, Bradley J., Conte, Gian Marco, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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17. Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized Hyperparameters

Author

Farzana, Walia, Temtam, Ahmed G., Shboul, Zeina A., Rahman, M. Monibor, Sadique, M. Shibly, Iftekharuddin, Khan M., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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18. Modified MobileNet for Patient Survival Prediction

Author

Akbar, Agus Subhan, Fatichah, Chastine, Suciati, Nanik, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
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19. Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and Priors

Author

Suter, Yannick, Knecht, Urspeter, Wiest, Roland, Reyes, Mauricio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
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20. Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression

Author

Pati, Sarthak, Sharma, Vaibhav, Aslam, Heena, Thakur, Siddhesh P., Akbari, Hamed, Mang, Andreas, Subramanian, Shashank, Biros, George, Davatzikos, Christos, Bakas, Spyridon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
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21. Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics

Author

Kazerooni, Anahita Fathi, Davatzikos, Christos, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
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22. Immunotherapy of pediatric brain tumor patients should include an immunoprevention strategy: a medical hypothesis paper.

Author

Driggers L, Zhang JG, Newcomb EW, Ge L, Hoa N, and Jadus MR

Subjects
Adult, Antigens, Neoplasm immunology, Child, Humans, Brain Neoplasms immunology, Brain Neoplasms therapy, Glioblastoma immunology, Glioblastoma therapy, Immunotherapy methods
Abstract

Adults diagnosed with Glioblastoma multiforme (GBM) are frequently faced with a 7% chance of surviving 2 years compared with pediatric patients with GBM who have a 26% survival rate. Our recent screen of possible glioma-associated antigen precursor protein (TAPP) profiles displayed from different types of pediatric brain tumors showed that pediatric patients contained a subset of the tumor antigens displayed by adult GBM patients. Adult GBM possess at least 27 tumor antigens that can potentially stimulate T cell immune responses, suggesting that these tumors are quite antigenic. In contrast, pediatric brain tumors only expressed nine tumor antigens with mRNA levels that were equivalent to those displayed by adult GBM. These tumor-associated antigens could be used as possible targets of therapeutic immunization for pediatric brain cancer patients. Children have developing immune systems that peak at puberty. An immune response mounted by these pediatric patients might account for their extended life spans, even though the pediatric brain tumors express far fewer total tumor-associated antigens. Here we present a hypothesis that pediatric brain tumor patients might be the best patients to show that immunotherapy can be used to successfully treat established cancers. We speculate that immunotherapy should include a panel of tumor antigens that might prevent the out-growth of more malignant tumor cells and thereby prevent the brain tumor relapse. Thus, pediatric brain tumor patients might provide an opportunity to prove the concept of immunoprevention.

Published
2010
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23. Using Separated Inputs for Multimodal Brain Tumor Segmentation with 3D U-Net-like Architectures

Author

Boutry, N., Chazalon, J., Puybareau, E., Tochon, G., Talbot, H., Géraud, T., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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25. The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

Author

Pati, Sarthak, Singh, Ashish, Rathore, Saima, Gastounioti, Aimilia, Bergman, Mark, Ngo, Phuc, Ha, Sung Min, Bounias, Dimitrios, Minock, James, Murphy, Grayson, Li, Hongming, Bhattarai, Amit, Wolf, Adam, Sridaran, Patmaa, Kalarot, Ratheesh, Akbari, Hamed, Sotiras, Aristeidis, Thakur, Siddhesh P., Verma, Ragini, Shinohara, Russell T., Yushkevich, Paul, Fan, Yong, Kontos, Despina, Davatzikos, Christos, Bakas, Spyridon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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26. Brain Tumor Segmentation Based on 3D Residual U-Net

Author

Bhalerao, Megh, Thakur, Siddhesh, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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27. A Baseline for Predicting Glioblastoma Patient Survival Time with Classical Statistical Models and Primitive Features Ignoring Image Information

Author

Kofler, Florian, Paetzold, Johannes C., Ezhov, Ivan, Shit, Suprosanna, Krahulec, Daniel, Kirschke, Jan S., Zimmer, Claus, Wiestler, Benedikt, Menze, Bjoern H., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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28. Towards Population-Based Histologic Stain Normalization of Glioblastoma

Author

Grenko, Caleb M., Viaene, Angela N., Nasrallah, MacLean P., Feldman, Michael D., Akbari, Hamed, Bakas, Spyridon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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29. Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning

Author

Thakur, Siddhesh P., Doshi, Jimit, Pati, Sarthak, Ha, Sung Min, Sako, Chiharu, Talbar, Sanjay, Kulkarni, Uday, Davatzikos, Christos, Erus, Guray, Bakas, Spyridon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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30. No New-Net

Author

Isensee, Fabian, Kickingereder, Philipp, Wick, Wolfgang, Bendszus, Martin, Maier-Hein, Klaus H., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Keyvan, Farahani, editor, Reyes, Mauricio, editor, and van Walsum, Theo, editor

Published
2019
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31. Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction

Author

Gates, Evan, Pauloski, J. Gregory, Schellingerhout, Dawid, Fuentes, David, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Keyvan, Farahani, editor, Reyes, Mauricio, editor, and van Walsum, Theo, editor

Published
2019
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32. Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns

Author

Elsheikh, Samar S. M., Bakas, Spyridon, Mulder, Nicola J., Chimusa, Emile R., Davatzikos, Christos, Crimi, Alessandro, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Keyvan, Farahani, editor, Reyes, Mauricio, editor, and van Walsum, Theo, editor

Published
2019
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33. Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

Author

Isensee, Fabian, Kickingereder, Philipp, Wick, Wolfgang, Bendszus, Martin, Maier-Hein, Klaus H., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Menze, Bjoern, editor, and Reyes, Mauricio, editor

Published
2018
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34. Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

Author

Rathore, Saima, Bakas, Spyridon, Pati, Sarthak, Akbari, Hamed, Kalarot, Ratheesh, Sridharan, Patmaa, Rozycki, Martin, Bergman, Mark, Tunc, Birkan, Verma, Ragini, Bilello, Michel, Davatzikos, Christos, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Menze, Bjoern, editor, and Reyes, Mauricio, editor

Published
2018
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35. Glioblastoma and Survival Prediction

Author

Shboul, Zeina A., Vidyaratne, Lasitha, Alam, Mahbubul, Iftekharuddin, Khan M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Menze, Bjoern, editor, and Reyes, Mauricio, editor

Published
2018
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36. A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images

Author

Chen, Andrew X., Rabadán, Raúl, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Holzinger, Andreas, editor, Goebel, Randy, editor, Ferri, Massimo, editor, and Palade, Vasile, editor

Published
2017
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37. Rotation Clustering: A Consensus Clustering Approach to Cluster Gene Expression Data

Author

Galdi, Paola, Serra, Angela, Tagliaferri, Roberto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Petrosino, Alfredo, editor, Loia, Vincenzo, editor, and Pedrycz, Witold, editor

Published
2017
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38. Use of a neural network-based prediction method to calculate the therapeutic dose in boron neutron capture therapy of patients with glioblastoma.

Author

Tian F, Zhao S, Geng C, Guo C, Wu R, and Tang X

Subjects
Humans, Neural Networks, Computer, Radiotherapy Dosage, Neutrons, Glioblastoma diagnostic imaging, Glioblastoma radiotherapy, Boron Neutron Capture Therapy methods
Abstract

Background: Boron neutron capture therapy (BNCT) is a binary radiotherapy based on the 10 B(n, α) 7 Li capture reaction. Nonradioactive isotope 10 B atoms which selectively concentrated in tumor cells will react with low energy neutrons (mainly thermal neutrons) to produce secondary particles with high linear energy transfer, thus depositing dose in tumor cells. In clinical practice, an appropriate treatment plan needs to be set on the basis of the treatment planning system (TPS). Existing BNCT TPSs usually use the Monte Carlo method to determine the three-dimensional (3D) therapeutic dose distribution, which often requires a lot of calculation time due to the complexity of simulating neutron transportation., Purpose: A neural network-based BNCT dose prediction method is proposed to achieve the rapid and accurate acquisition of BNCT 3D therapeutic dose distribution for patients with glioblastoma to solve the time-consuming problem of BNCT dose calculation in clinic., Methods: The clinical data of 122 patients with glioblastoma are collected. Eighteen patients are used as a test set, and the rest are used as a training set. The 3D-UNET is constructed through the design optimization of input and output data sets based on radiation field information and patient CT information to enable the prediction of 3D dose distribution of BNCT., Results: The average mean absolute error of the predicted and simulated equivalent doses of each organ are all less than 1 Gy. For the dose to 95% of the GTV volume (D 95 ), the relative deviation between predicted and simulated results are all less than 2%. The average 2 mm/2% gamma index is 89.67%, and the average 3 mm/3% gamma index is 96.78%. The calculation takes about 6 h to simulate the 3D therapeutic dose distribution of a patient with glioblastoma by Monte Carlo method using Intel Xeon E5-2699 v4, whereas the time required by the method proposed in this study is almost less than 1 s using a Titan-V graphics card., Conclusions: This study proposes a 3D dose prediction method based on 3D-UNET architecture in BNCT, and the feasibility of this method is demonstrated. Results indicate that the method can remarkably reduce the time required for calculation and ensure the accuracy of the predicted 3D therapeutic dose-effect. This work is expected to promote the clinical development of BNCT in the future., (© 2023 American Association of Physicists in Medicine.)

Published
2023
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39. Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

Author

Randhawa, Ramandeep S., Modi, Ankit, Jain, Parag, Warier, Prashant, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Crimi, Alessandro, editor, Menze, Bjoern, editor, Maier, Oskar, editor, Reyes, Mauricio, editor, Winzeck, Stefan, editor, and Handels, Heinz, editor

Published
2016
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40. Brain Tumor Segmentation by Variability Characterization of Tumor Boundaries

Author

Rios Piedra, Edgar A., Ellingson, Benjamin M., Taira, Ricky K., El-Saden, Suzie, Bui, Alex A. T., Hsu, William, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Crimi, Alessandro, editor, Menze, Bjoern, editor, Maier, Oskar, editor, Reyes, Mauricio, editor, Winzeck, Stefan, editor, and Handels, Heinz, editor

Published
2016
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41. An Online Platform for the Automatic Reporting of Multi-parametric Tissue Signatures: A Case Study in Glioblastoma

Author

Juan-Albarracín, Javier, Fuster-Garcia, Elies, García-Gómez, Juan M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Crimi, Alessandro, editor, Menze, Bjoern, editor, Maier, Oskar, editor, Reyes, Mauricio, editor, Winzeck, Stefan, editor, and Handels, Heinz, editor

Published
2016
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42. Allergic airway inflammation delays glioblastoma progression and reinvigorates systemic and local immunity in mice.

Author

Poli A, Oudin A, Muller A, Salvato I, Scafidi A, Hunewald O, Domingues O, Nazarov PV, Puard V, Baus V, Azuaje F, Dittmar G, Zimmer J, Michel T, Michelucci A, Niclou SP, and Ollert M

Subjects
Mice, Animals, Microglia pathology, Mice, Inbred C57BL, Glioblastoma genetics, Glioblastoma pathology, Brain Neoplasms pathology, Glioma genetics, Glioma pathology, Hypersensitivity pathology
Abstract

Background: Numerous patient-based studies have highlighted the protective role of immunoglobulin E-mediated allergic diseases on glioblastoma (GBM) susceptibility and prognosis. However, the mechanisms behind this observation remain elusive. Our objective was to establish a preclinical model able to recapitulate this phenomenon and investigate the role of immunity underlying such protection., Methods: An immunocompetent mouse model of allergic airway inflammation (AAI) was initiated before intracranial implantation of mouse GBM cells (GL261). RAG1-KO mice served to assess tumor growth in a model deficient for adaptive immunity. Tumor development was monitored by MRI. Microglia were isolated for functional analyses and RNA-sequencing. Peripheral as well as tumor-associated immune cells were characterized by flow cytometry. The impact of allergy-related microglial genes on patient survival was analyzed by Cox regression using publicly available datasets., Results: We found that allergy establishment in mice delayed tumor engraftment in the brain and reduced tumor growth resulting in increased mouse survival. AAI induced a transcriptional reprogramming of microglia towards a pro-inflammatory-like state, uncovering a microglia gene signature, which correlated with limited local immunosuppression in glioma patients. AAI increased effector memory T-cells in the circulation as well as tumor-infiltrating CD4 + T-cells. The survival benefit conferred by AAI was lost in mice devoid of adaptive immunity., Conclusion: Our results demonstrate that AAI limits both tumor take and progression in mice, providing a preclinical model to study the impact of allergy on GBM susceptibility and prognosis, respectively. We identify a potentiation of local and adaptive systemic immunity, suggesting a reciprocal crosstalk that orchestrates allergy-induced immune protection against GBM., (© 2022 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.)

Published
2023
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44. Glioblastoma biomarkers in urinary extracellular vesicles reveal the potential for a 'liquid gold' biopsy.

Author

Hallal SM, Tűzesi Á, Sida LA, Xian E, Madani D, Muralidharan K, Shivalingam B, Buckland ME, Satgunaseelan L, and Alexander KL

Subjects
Humans, Chromatography, Liquid methods, Proteomics methods, Tandem Mass Spectrometry, Biomarkers metabolism, Liquid Biopsy, Glioblastoma pathology, Extracellular Vesicles metabolism
Abstract

Background: Biomarkers that reflect glioblastoma tumour activity and treatment response are urgently needed to help guide clinical management, particularly for recurrent disease. As the urinary system is a major clearance route of circulating extracellular vesicles (EVs; 30-1000 nm nanoparticles) we explored whether sampling urinary-EVs could serve as a simple and non-invasive liquid biopsy approach for measuring glioblastoma-associated biomarkers., Methods: Fifty urine specimens (15-60 ml) were collected from 24 catheterised glioblastoma patients immediately prior to primary (n = 17) and recurrence (n = 7) surgeries, following gross total resection (n = 9), and from age/gender-matched healthy participants (n = 14). EVs isolated by differential ultracentrifugation were characterised and extracted proteomes were analysed by high-resolution data-independent acquisition liquid chromatography tandem mass spectrometry (DIA-LC-MS/MS)., Results: Overall, 6857 proteins were confidently identified in urinary-EVs (q-value ≤ 0.01), including 94 EV marker proteins. Glioblastoma-specific proteomic signatures were determined, and putative urinary-EV biomarkers corresponding to tumour burden and recurrence were identified (FC ≥ | 2 | , adjust p-val≤0.05, AUC > 0.9)., Conclusion: In-depth DIA-LC-MS/MS characterisation of urinary-EVs substantiates urine as a viable source of glioblastoma biomarkers. The promising 'liquid gold' biomarker panels described here warrant further investigation., (© 2024. The Author(s).)

Published
2024
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45. Nomograms for prognostic risk assessment in glioblastoma multiforme: Applications and limitations.

Author

Zheng H, Yan T, Han Y, Wang Q, Zhang G, Zhang L, Zhu W, Xie L, and Guo X

Subjects
Biomarkers, Humans, Nomograms, Prognosis, Risk Assessment, Glioblastoma diagnosis, Glioblastoma genetics, Glioblastoma therapy
Abstract

Glioblastoma multiforme (GBM) is the most common and aggressive form of brain cancer. Prognosis evaluation is of great significance in guiding individualized treatment and monitoring of GBM. By integrating different prognostic variables, nomograms simplify the statistical risk prediction model into numerical estimates for death or recurrence, and are hence widely applied in prognosis prediction. In the past two decades, the application of high-throughput profiling technology and the establishment of TCGA database and other public data deposits have provided opportunities to identify cancer-related molecules and prognostic biomarkers. As a result, both molecular features and clinical characteristics of cancer have been reported to be the key factors in nomogram model construction. This article comprehensively reviewed 35 studies of GBM nomograms, analyzed the present situation of GBM nomograms, and discussed the role and significance of nomograms in personalized risk assessment and clinical treatment decision-making. To facilitate the application of nomograms in the prognostic prediction of GBM patients, a website has been established for the online access of nomograms based on the studies of this review, which is called Consensus Nomogram Spectrum for Glioblastoma (CNSgbm) and is accessible through https://bioinfo.henu.edu.cn/nom/NomList.jsp., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Published
2022
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46. Position paper: new insights into the immunobiology and dynamics of tumor-host interactions require adaptations of clinical studies

Author

Stefaan Van Gool, Volker Schirrmacher, Wilfried Stücker, and Tobias Sprenger

Subjects
0301 basic medicine, medicine.medical_treatment, Antineoplastic Agents, Guidelines as Topic, Bioinformatics, law.invention, Reimbursement Mechanisms, Translational Research, Biomedical, 03 medical and health sciences, 0302 clinical medicine, Cancer immunotherapy, Randomized controlled trial, law, Neoplasms, Medicine, Humans, Pharmacology (medical), Molecular Targeted Therapy, Precision Medicine, Randomized Controlled Trials as Topic, business.industry, Evidence-based medicine, 030104 developmental biology, Oncology, 030220 oncology & carcinogenesis, Position paper, business, Glioblastoma, Tumor immunology, Host (network)
Abstract

Prospective double-blind placebo-controlled randomized clinical trials (RCTs) are considered standard for the proof of the efficacy of oncologic therapies. Molecular methods have provided new insights into tumor biology and led to the development of targeted therapies. Due to the increasing complexity of molecular tumor characteristics and of the individuality of specific anti-tumor immune reactivity, RCTs are unfortunately only of limited use.The historical methods of drug research and approval and the related practices of reimbursement by statutory and private health insurance companies are being questioned. New, innovative methods for the documentation of evidence in personalized medicine will be addressed. Possible perspectives and new approaches are discussed, in particular with regard to glioblastoma.Highly specialized translational oncology groups like the IOZK can contribute to medical progress and quick transfer 'from bench to bedside.' Their contribution should be acknowledged and taken into account more strongly in the development of guidelines and the reimbursement of therapy costs. Methodological plurality should be encouraged.

Published
2020

47. Fluoxetine enhances the antitumor effect of olfactory ensheathing cell-thymidine kinase/ganciclovir gene therapy in human glioblastoma multiforme cells through upregulation of Connexin43 levels.

Author

Hosseindoost S, Dehpour AR, Dehghan S, Javadi SAH, Arjmand B, Fallah A, and Hadjighassem M

Subjects
Humans, Connexin 43 genetics, Connexin 43 metabolism, Connexin 43 therapeutic use, Thymidine Kinase genetics, Thymidine Kinase metabolism, Thymidine Kinase therapeutic use, Fluoxetine pharmacology, Fluoxetine therapeutic use, Up-Regulation, Genetic Therapy, Antiviral Agents pharmacology, Ganciclovir pharmacology, Ganciclovir therapeutic use, Glioblastoma drug therapy, Glioblastoma genetics
Abstract

Glioblastoma multiforme (GBM) is the most invasive form of primary brain astrocytoma, resulting in poor clinical outcomes. Herpes simplex virus thymidine kinase/ganciclovir (HSV-TK/GCV) gene therapy is considered a promising strategy for GBM treatment. Since Connexin43 (Cx43) expression is reduced in GBM cells, increasing Cx43 levels could enhance the effectiveness of gene therapy. The present study aims to examine the impact of fluoxetine on HSV-TK/GCV gene therapy in human GBM cells using human olfactory ensheathing cells (OECs) as vectors. The effect of fluoxetine on Cx43 levels was assessed using the western blot technique. GBM-derived astrocytes and OECs-TK were Cocultured, and the effect of fluoxetine on the Antitumor effect of OEC-TK/GCV gene therapy was evaluated using MTT assay and flow cytometry. Our results showed that fluoxetine increased Cx43 levels in OECs and GBM cells and augmented the killing effect of OECs-TK on GBM cells. Western blot data revealed that fluoxetine enhanced the Bax/Bcl2 ratio and the levels of cleaved caspase-3 in the coculture of OECs-TK and GBM cells. Moreover, flow cytometry data indicated that fluoxetine increased the percentage of apoptotic cells in the coculture system. This study suggests that fluoxetine, by upregulating Cx43 levels, could strengthen the Antitumor effect of OEC-TK/GCV gene therapy on GBM cells., (© 2023 Wiley Periodicals LLC.)

Published
2023
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48. Engineering a tunable micropattern-array assay to sort single extracellular vesicles and particles to detect RNA and protein in situ.

Author

Zhang J, Rima XY, Wang X, Nguyen LTH, Huntoon K, Ma Y, Palacio PL, Nguyen KT, Albert K, Duong-Thi MD, Walters N, Kwak KJ, Yoon MJ, Li H, Doon-Ralls J, Hisey CL, Lee D, Wang Y, Ha J, Scherler K, Fallen S, Lee I, Palmer AF, Jiang W, Magaña SM, Wang K, Kim BYS, Lee LJ, and Reátegui E

Subjects
Humans, Lipopolysaccharides, RNA, Messenger, Lipoproteins, Extracellular Vesicles genetics, Extracellular Vesicles metabolism, MicroRNAs genetics, MicroRNAs metabolism, Glioblastoma diagnosis, Glioblastoma genetics
Abstract

The molecular heterogeneity of extracellular vesicles (EVs) and the co-isolation of physically similar particles, such as lipoproteins (LPs), confounds and limits the sensitivity of EV bulk biomarker characterization. Herein, we present a single-EV and particle (siEVP) protein and RNA assay ( siEVP PRA) to simultaneously detect mRNAs, miRNAs, and proteins in subpopulations of EVs and LPs. The siEVP PRA immobilizes and sorts particles via positive immunoselection onto micropatterns and focuses biomolecular signals in situ. By detecting EVPs at a single-particle resolution, the siEVP PRA outperformed the sensitivities of bulk-analysis benchmark assays for RNA and protein. To assess the specificity of RNA detection in complex biofluids, EVs from various glioma cell lines were processed with small RNA sequencing, whereby two mRNAs and two miRNAs associated with glioblastoma multiforme (GBM) were chosen for cross-validation. Despite the presence of single-EV-LP co-isolates in serum, the siEVP PRA detected GBM-associated vesicular RNA profiles in GBM patient siEVPs. The siEVP PRA effectively examines intravesicular, intervesicular, and interparticle heterogeneity with diagnostic promise., (© 2023 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals LLC on behalf of International Society for Extracellular Vesicles.)

Published
2023
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49. A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas.

Author

Le VH, Minh TNT, Kha QH, and Le NQK

Subjects
Humans, Retrospective Studies, Magnetic Resonance Imaging, Machine Learning, Brain Neoplasms diagnostic imaging, Brain Neoplasms genetics, Glioma diagnostic imaging, Glioblastoma
Abstract

Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. In the context of the transfer learning approach, we aimed to train and test an MRI-based radiomics model for predicting survival in GBM patients and validate it in LGG patients. From each patient's 704 MRI-based radiomics features, we chose seventeen optimal radiomics signatures in the GBM training set (n = 71) and used these features in both the GBM testing set (n = 31) and LGG validation set (n = 107) for further analysis. Each patient's risk score, calculated based on those optimal radiomics signatures, was chosen to represent the radiomics model. We compared the radiomics model with clinical, gene status models, and combined model integrating radiomics, clinical, and gene status in predicting survival. The average iAUCs of combined models in training, testing, and validation sets were respectively 0.804, 0.878, and 0.802, and those of radiomics models were 0.798, 0.867, and 0.717. The average iAUCs of gene status and clinical models ranged from 0.522 to 0.735 in all three sets. The radiomics model trained in GBM patients can effectively predict the overall survival of GBM and LGG patients, and the combined model improved this ability., (© 2023. International Federation for Medical and Biological Engineering.)

Published
2023
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50. A novel paper MAP method for rapid high resolution histological analysis

Author

Seok Gu Kang, Eunice Yoojin Lee, Mirae Lee, Jin-Kyung Shim, Jiwon Woo, Jung Hee Kim, Doh-Hee Kim, Jeong Yoon Park, and Yu-Mi Yang

Subjects
Male, Proteome, Computer science, Science, Mice, Nude, High resolution, Apoptosis, Diseases, Computational biology, Article, Mice, Imaging, Three-Dimensional, Alzheimer Disease, Tumor Cells, Cultured, Animals, Humans, Spinal Cord Injuries, Cell Proliferation, Microscopy, Confocal, Multidisciplinary, Molecular medicine, Brain Neoplasms, Biological techniques, Brain, Xenograft Model Antitumor Assays, Visualization, Neurology, Medicine, Glioblastoma, Biotechnology, Neuroscience
Abstract

Three-dimensional visualization of cellular and subcellular-structures in histological-tissues is essential for understanding the complexities of biological-phenomena, especially with regards structural and spatial relationships and pathologlical-diagnosis. Recent advancements in tissue-clearing technology, such as Magnified Analysis of Proteome (MAP), have significantly improved our ability to study biological-structures in three-dimensional space; however, their wide applicability to a variety of tissues is limited by long incubation-times and a need for advanced imaging-systems that are not readily available in most-laboratories. Here, we present optimized MAP-based method for paper-thin samples, Paper-MAP, which allow for rapid clearing and subsequent imaging of three-dimensional sections derived from various tissues using conventional confocal-microscopy. Paper-MAP successfully clear tissues within 1-day, compared to the original-MAP, without significant differences in achieved optical-transparency. As a proof-of-concept, we investigated the vasculature and neuronal-networks of a variety of human and rodent tissues processed via Paper-MAP, in both healthy and diseased contexts, including Alzheimer’s disease and glioma.

Published
2021