Determining Chromosomal Arms 1p/19q Co-deletion Status in Low Graded Glioma by Cross Correlation-periodogram Pattern Analysis Article Swipe
Debanjali Bhattacharya
,
Neelam Sinha
,
Jitender Saini
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-537094/v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-537094/v1
Prediction of mutational status of different graded glioma is extremely crucial for its diagnosis and treatment planning. Currently FISH and the surgical biopsy techniques are the ‘gold standard’ in the field of diagnostics; the analyses of which helps to decide appropriate treatment regime. In this study we have proposed a novel approach that analyzed structural MRI image signature pattern for predicting 1p/19q co-deletion status non-invasively. A total of 159 patients with grade-II and grade-III glioma were included in the analysis. These patients earlier underwent biopsy; the report of which confirmed 57 cases with no 1p/19q co-deletion and 102 cases with 1p/19q co-deletion. Tumor tissue heterogeneity was investigated by variance of cross correlation (VoCC). The marked difference in pattern of VoCC between two classes was quantified using Lomb-Scargle (LS) periodogram. Energy and the cutoff frequency of LS power spectral density were derived and utilized as the features for classification. RUSBoost classifier was used that yield highest classification accuracy of 84% for G-II and 86.09% for G-III respectively in classifying 1p/19q co-deleted and 1p/19q non-deleted glioma. In clinical practice the proposed technique can be utilized as a pre-confirmatory test of glioma mutation, before wet-lab validation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-537094/v1
- https://www.researchsquare.com/article/rs-537094/v1.pdf?c=1631882509000
- OA Status
- green
- Cited By
- 1
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4230288201
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4230288201Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-537094/v1Digital Object Identifier
- Title
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Determining Chromosomal Arms 1p/19q Co-deletion Status in Low Graded Glioma by Cross Correlation-periodogram Pattern AnalysisWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-05-25Full publication date if available
- Authors
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Debanjali Bhattacharya, Neelam Sinha, Jitender SainiList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-537094/v1Publisher landing page
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https://www.researchsquare.com/article/rs-537094/v1.pdf?c=1631882509000Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-537094/v1.pdf?c=1631882509000Direct OA link when available
- Concepts
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Glioma, Correlation, Biopsy, Medicine, Cutoff, Internal medicine, Oncology, Mathematics, Cancer research, Physics, Quantum mechanics, GeometryTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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16Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.analyzed | 54 |
| abstract_inverted_index.approach | 52 |
| abstract_inverted_index.clinical | 176 |
| abstract_inverted_index.features | 146 |
| abstract_inverted_index.grade-II | 72 |
| abstract_inverted_index.included | 77 |
| abstract_inverted_index.patients | 70, 82 |
| abstract_inverted_index.practice | 177 |
| abstract_inverted_index.proposed | 49, 179 |
| abstract_inverted_index.spectral | 138 |
| abstract_inverted_index.surgical | 22 |
| abstract_inverted_index.utilized | 143, 183 |
| abstract_inverted_index.variance | 109 |
| abstract_inverted_index.Currently | 18 |
| abstract_inverted_index.analysis. | 80 |
| abstract_inverted_index.confirmed | 90 |
| abstract_inverted_index.diagnosis | 14 |
| abstract_inverted_index.different | 6 |
| abstract_inverted_index.extremely | 10 |
| abstract_inverted_index.frequency | 134 |
| abstract_inverted_index.grade-III | 74 |
| abstract_inverted_index.mutation, | 190 |
| abstract_inverted_index.planning. | 17 |
| abstract_inverted_index.signature | 58 |
| abstract_inverted_index.technique | 180 |
| abstract_inverted_index.treatment | 16, 42 |
| abstract_inverted_index.underwent | 84 |
| abstract_inverted_index.Prediction | 1 |
| abstract_inverted_index.classifier | 150 |
| abstract_inverted_index.co-deleted | 170 |
| abstract_inverted_index.difference | 116 |
| abstract_inverted_index.mutational | 3 |
| abstract_inverted_index.predicting | 61 |
| abstract_inverted_index.quantified | 125 |
| abstract_inverted_index.structural | 55 |
| abstract_inverted_index.techniques | 24 |
| abstract_inverted_index.appropriate | 41 |
| abstract_inverted_index.classifying | 168 |
| abstract_inverted_index.co-deletion | 63, 96 |
| abstract_inverted_index.correlation | 112 |
| abstract_inverted_index.non-deleted | 173 |
| abstract_inverted_index.standard’ | 28 |
| abstract_inverted_index.validation. | 193 |
| abstract_inverted_index.Lomb-Scargle | 127 |
| abstract_inverted_index.co-deletion. | 102 |
| abstract_inverted_index.diagnostics; | 33 |
| abstract_inverted_index.investigated | 107 |
| abstract_inverted_index.periodogram. | 129 |
| abstract_inverted_index.respectively | 166 |
| abstract_inverted_index.heterogeneity | 105 |
| abstract_inverted_index.classification | 156 |
| abstract_inverted_index.classification. | 148 |
| abstract_inverted_index.non-invasively. | 65 |
| abstract_inverted_index.pre-confirmatory | 186 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5014371833 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I181514455 |
| citation_normalized_percentile.value | 0.54574046 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |