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Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [article]

Luca Pasquini, Antonio Napolitano, Martina Lucignani, Emanuela Tagliente, Francesco Dellepiane, Maria Camilla Rossi-Espagnet, Matteo Ritrovato, Antonello Vidiri, Veronica Villani, Giulio Ranazzi, Antonella Stoppacciaro, Andrea Romano (+2 others)
2021 arXiv   pre-print
Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM). However, clinical implementation is limited by lack of parameters standardization.  ...  We aimed to compare nine machine learning classifiers, with different optimization parameters, to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase  ...  Discussion AI has proven to be an accurate tool in predicting survival and molecular profile of GBM.  ... 
arXiv:2102.06526v1 fatcat:me65sxqp3bahhbok2uculdxueq

Visualizing Molecular Profiles of Glioblastoma with GBM-BioDP

Orieta Celiku, Seth Johnson, Shuping Zhao, Kevin Camphausen, Uma Shankavaram, Tao Jiang
2014 PLoS ONE  
Validation of clinical biomarkers and response to therapy is a challenging topic in cancer research.  ...  These data enable investigation of genetic and epigenetic changes responsible for cancer onset and progression, response to cancer therapies, and discovery of the molecular profiles of various cancers.  ...  The patients with longest survival (4th quartile) exhibit significantly lower levels of mir-34a (t-test p-value 0.041 for comparison of 4th quartile to the 1-3 quartiles, and 0.033 for comparison of 4th  ... 
doi:10.1371/journal.pone.0101239 pmid:25010047 pmcid:PMC4091869 fatcat:2pgsi4a4cfep7n3k3rdhlpet2a

Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Madeleine M. Shaver, Paul A. Kohanteb, Catherine Chiou, Michelle D. Bardis, Chanon Chantaduly, Daniela Bota, Christopher G. Filippi, Brent Weinberg, Jack Grinband, Daniel S. Chow, Peter D. Chang
2019 Cancers  
We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities.  ...  This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/cancers11060829 pmid:31207930 pmcid:PMC6627902 fatcat:gc37sfqmr5ce7i535ig6jyszc4

Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine

Houman Sotoudeh, Omid Shafaat, Joshua D. Bernstock, Michael David Brooks, Galal A. Elsayed, Jason A. Chen, Paul Szerip, Gustavo Chagoya, Florian Gessler, Ehsan Sotoudeh, Amir Shafaat, Gregory K. Friedman
2019 Frontiers in Oncology  
Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds.  ...  Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients.  ...  Outcome Prediction There are many models to predict the overall survival rate in patients with glioma. Most of them are based on clinical data, genetic information, and imaging.  ... 
doi:10.3389/fonc.2019.00768 pmid:31475111 pmcid:PMC6702305 fatcat:mjg4ugf265akxhlgcrfcqce774

Current Advances and Challenges in Radiomics of Brain Tumors

Zhenjie Yi, Lifu Long, Yu Zeng, Zhixiong Liu
2021 Frontiers in Oncology  
Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find  ...  Imaging diagnosis is crucial for early detection and monitoring of brain tumors.  ...  ACKNOWLEDGMENTS We are grateful to all of those with whom we have had the pleasure to work with during this and other related projects.  ... 
doi:10.3389/fonc.2021.732196 pmid:34722274 pmcid:PMC8551958 fatcat:6n32ru7jlvaslhvrrkjjxbrzny

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Ahmad Chaddad, Michael Jonathan Kucharczyk, Paul Daniel, Siham Sabri, Bertrand J. Jean-Claude, Tamim Niazi, Bassam Abdulkarim
2019 Frontiers in Oncology  
These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response.  ...  Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility.  ...  Further development of pre-clinical models and correlation with clinical datasets will be essential to drive this field forward toward improving the utility of radiomics for diagnosis in GBM.  ... 
doi:10.3389/fonc.2019.00374 pmid:31165039 pmcid:PMC6536622 fatcat:hdwtwfw6qja7zattkgblorbeku

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

Niha Beig, Kaustav Bera, Pallavi Tiwari
2020 Neuro-Oncology Advances  
Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental  ...  of "hand-crafted" features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models  ...  a subset of features following feature selection, machine learning models are developed by categorizing and classifying various datasets according to defined labels (ie, GBM vs low-grade glioma [ LGG  ... 
doi:10.1093/noajnl/vdaa148 pmid:33521636 pmcid:PMC7829475 fatcat:2ntt5qxog5dnpkc62sne2b2uoi

An Overview of Mathematical Models for RNA Sequence-based Glioblastoma Subclassification

Yilin Wu, Eric Zander, Andrew Ardeleanu, Ryan Singleton, Barnabas Bede
2021 Artificial Intelligence in Oncology  
RNA-Sequence (RNA-Seq)-based molecular profiling of GBM is widely implemented and readily quantifiable.  ...  We found ML algorithms Support Vector Machines, Multi-Layer Perceptron s, and Voting Ensemble are best equipped in assigning GBM to correct molecular subgroups of GBM without histological studies.  ...  Leveraging genetic expression levels to predict GBM subtype or likely response to intensive treatment can be accomplished by employing one of many available machine learning (ML) classification algorithms  ... 
doi:10.52454/aio.v3i1.11 fatcat:3zu36d6l45gyhfzibvtz5xzjay

Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach

Fang-Ying Chiu, Yun Yen
2022 Cancers  
The aim of this study was to assess the potential of multi-parametric MR fingerprinting with volumetric tumor phenotype and radiomic features to underlie biological process and prognostic status of patients  ...  Glioblastoma (GBM) is a fast-growing and aggressive brain tumor of the central nervous system.  ...  for the semiautomatic annotation of GBM using MRI, ground truth, and machine learning [4] .  ... 
doi:10.3390/cancers14061475 pmid:35326626 pmcid:PMC8945893 fatcat:qqkgy77e35gwreickxdvps7ule

Unsupervised Analysis of Transcriptomic Profiles Reveals Six Glioma Subtypes

A. Li, J. Walling, S. Ahn, Y. Kotliarov, Q. Su, M. Quezado, J. C. Oberholtzer, J. Park, J. C. Zenklusen, H. A. Fine
2009 Cancer Research  
Gliomas are the most common type of primary brain tumors in adults and a significant cause of cancer-related mortality.  ...  Section 1734 solely to indicate this fact.  ...  The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C.  ... 
doi:10.1158/0008-5472.can-08-2100 pmid:19244127 pmcid:PMC2845963 fatcat:yd3kqmczlvbrzcv6r5kchu7jii

Glioma Survival Analysis Empowered with Data Engineering -A Survey

Navodini Wijethilake, Dulani Meedeniya, Charith Chitraranjan, Indika Perera, Mobarakol Islam, Hongliang Ren
2021 IEEE Access  
Survival analysis is a critical task in glioma patient management due to the inter and intra tumor heterogeneity.  ...  Besides, in the past years, machine learning techniques and deep learning have emerged into the field of survival analysis of glioma patients trading off the traditional statistical analysis-based survival  ...  ACKNOWLEDGMENT The authors acknowledge the support received from the Conference & Publishing grant, University of Moratuwa, Sri Lanka for publishing this paper.  ... 
doi:10.1109/access.2021.3065965 fatcat:ujfg37dbsvfoldz7skua6ngmj4

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Antonio Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene, Fiorella Guadagni, Patrizia Ferroni, Noemi Scarpato, Fabio Massimo Zanzotto, Andrés Bueno-Crespo, Horacio Pérez-Sánchez
2021 International Journal of Molecular Sciences  
However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor  ...  In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted.  ...  The models classify the patients into four groups, each with an estimated overall survival.  ... 
doi:10.3390/ijms22094394 pmid:33922356 fatcat:z3mxbx7fajge7pkyrp2odlauz4

PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data

Jie Hao, Youngsoon Kim, Tae-Kyung Kim, Mingon Kang
2018 BMC Bioinformatics  
PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning.  ...  PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet's performance was assessed by Wilcoxon signed-rank  ...  Acknowledgements We would like to thank Dr. Jung Hun Oh for his help and advice in this study. Funding Not applicable. Availability of data and materials  ... 
doi:10.1186/s12859-018-2500-z fatcat:dlf2fcmijfgqrnca3hwabbal5i

Estimating survival time of patients with glioblastoma multiforme and characterization of the identified microRNA signatures

Srinivasulu Yerukala Sathipati, Hui-Ling Huang, Shinn-Ying Ho
2016 BMC Genomics  
SVR-GBM had a mean absolute error of 0.63 years and a correlation coefficient of 0.76 between the real and predicted survival time.  ...  This work aims to identify the miRNA signatures related to survival of GBM patients for developing molecular therapies.  ...  for the Top University Program" of the National Chiao Tung University and Ministry of Education, Taiwan, R.O.C. for the project 105W962.  ... 
doi:10.1186/s12864-016-3321-y pmid:28155650 pmcid:PMC5260001 fatcat:hwoghul2zvhf3lwhjagnfmuxvy

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges

Jiaona Xu, Yuting Meng, Kefan Qiu, Win Topatana, Shijie Li, Chao Wei, Tianwen Chen, Mingyu Chen, Zhongxiang Ding, Guozhong Niu
2022 Frontiers in Oncology  
Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using  ...  Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management.  ...  ACKNOWLEDGMENTS The authors would like to thank Dr. Fanghui Qiu for his guidance and contribution in this work.  ... 
doi:10.3389/fonc.2022.892056 pmid:35965542 pmcid:PMC9363668 fatcat:3eao3uczojhc7gghduhdkibhny
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