Radiomic profiles in diffuse glioma reveal distinct subtypes with prognostic value

Lin, Peng; Peng, Yung-Hsing; Gao, Ruizhi; Wei, Yan; Li, Xiao‐Jiao; Huang, Su-Hua; Fang, Ye‐Ying; Wei, Zhu‐Xin; Huang, Zhi‐Guang; Yang, Hong; Chen, Gang

doi:10.1007/s00432-020-03153-6

Cited by 16 publications

(16 citation statements)

References 51 publications

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“…Images contain information about tumor phenotypes that are regulated not only by cell-intrinsic biological processes but also by the tumor microenvironment (59,(83)(84)(85). Recently, machine learning-based radiomics models demonstrated the capacity to evaluate enrichment levels of different immune cells in GB patients.…”

Section: Heterogeneity and Molecular Subtypementioning

confidence: 99%

“…Lin et al. ( 84 ) used a consistent clustering method to classify radiological features into two subgroups. The two subtypes have significantly different histological stages and molecular factors (such as IDH and MGMT).…”

Section: Progress Of Imaging-genomics In Gbmentioning

confidence: 99%

“…A large number of retrospective studies have revealed the link between the radiomics characteristics and survival time of GB patients ( 70 , 87 ). According to radiomics risk stratification, some studies ( 28 , 54 , 84 , 88 ) also tried to explore the corresponding tumor biological information. Based on survival-related imaging features, some studies ( 28 , 54 ) construct individualized RRS, by which the patients will be divided into subgroups with different risk stratifications.…”

Section: Progress Of Imaging-genomics In Gbmentioning

confidence: 99%

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Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Liu

Chen

et al. 2021

Front. Oncol.

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Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.

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Section: Heterogeneity and Molecular Subtypementioning

confidence: 99%

Section: Progress Of Imaging-genomics In Gbmentioning

confidence: 99%

Section: Progress Of Imaging-genomics In Gbmentioning

confidence: 99%

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Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Liu

Chen

et al. 2021

Front. Oncol.

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“…The COVID-19 outbreak in Hainan province started in late January, followed by several clusters in early February. [ 2 ] At present, the number of confirmed COVID-19 cases is 0, and the epidemic in Hainan Leisure Island has been effectively brought under control. There were no newly confirmed cases since late February, 2020.…”

Section: Introductionmentioning

confidence: 99%

Insights on defeating coronavirus disease (COVID-19) outbreak and predicting tourist arrival on the Chinese Hainan Leisure Island during the COVID-19 pandemic

Gang

Chen

et al. 2021

Medicine

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Background: Hainan province is a very popular leisure tourist arriving city in China. Coronavirus disease 2019 emerged in China and rapidly in early 2020, and due to its rapid worldwide spread, the World Health Organization declared COVID-19 as a global emergency. During the COVID-19 pandemic in Hainan province, many businesses and economies were influenced in this unexpected event, especially in tourism.Methods: This study used 2 classical forecasting methods to predict the number of tourists on Hainan Leisure Island from September to December in the second half of 2020 and to summarize the COVID-19 fighting experience during the pandemic. In addition, the Hainan government implemented epidemic control measures to resume production and work, and promote new tourism measures to acquire superior COVID-19 protection.Results: Winter's method provides a statistical model for predicting the number of visitors to Hainan under normal conditions. The trend analysis method considers the impact of the black swan event, an irregular event, and only uses the data under the influence of the event to predict according to the trend. Conclusion:If the impact of the black swan event (COVID-19) continues, the prediction can be made using this method. In addition, the Hainan government has undertaken timely and effective measures against COVID-19 to promote leisure tourism development.Abbreviations: COVID-19 = coronavirus disease 2019, MAD = mean absolute deviation, MAPE = mean absolute percentage error, MSD = mean standard deviation.

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“…This process has enabled the identification of quantitative imaging markers and signature models that are reflective of microscopic tumor biology, which has led to enhanced, biologically relevant classification, tumor grading, survival prediction, and treatment response in adult and pediatric brain tumors. [4][5][6][7][8] Although studies of radiomics and radiogenomics have been performed in adult brain tumors, their application and use in pediatric neuro-oncology are evolving. 5,9,10 Initial studies showed promising ability to distinguish posterior fossa tumors like ependymoma and pilocytic astrocytoma.…”

mentioning

confidence: 99%

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

Toll

Hein

et al. 2021

AJNR Am J Neuroradiol

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Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.

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Radiomic profiles in diffuse glioma reveal distinct subtypes with prognostic value

Cited by 16 publications

References 51 publications

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Insights on defeating coronavirus disease (COVID-19) outbreak and predicting tourist arrival on the Chinese Hainan Leisure Island during the COVID-19 pandemic

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

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