OS08.6.A Glioblastoma treatment response machine learning monitoring biomarkers: a systematic review and meta-analysis Article Swipe
Thomas C. Booth
,
Alysha Chelliah
,
Andrei Roman
,
Ayisha Al Busaidi
,
Haris Shuaib
,
Aysha Luis
,
Ayesha Mirchandani
,
Burcu Alparslan
,
Nina Mansoor
,
K. Ashkan
,
Sébastien Ourselin
,
Marc Modat
,
Mariusz Grzeda
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/neuonc/noab180.036
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/neuonc/noab180.036
BACKGROUND The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML). MATERIAL AND METHODS PRISMA methodology was followed. Articles published 09/2018-01/2021 (since previous reviews) were searched for using MEDLINE, EMBASE, and the Cochrane Register by two reviewers independently. Included study participants were adult patients with high grade glioma who had undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide) and subsequently underwent follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics - the target condition). Risk of bias and applicability was assessed with QUADAS 2. A third reviewer arbitrated any discrepancy. Contingency tables were created for hold-out test sets and recall, specificity, precision, F1-score, balanced accuracy calculated. A meta-analysis was performed using a bivariate model for recall, false positive rate and area-under the receiver operator characteristic curve (AUC). RESULTS Eighteen studies were included with 1335 patients in training sets and 384 in test sets. To determine whether there was progression or a mimic, the reference standard combination of follow-up imaging and histopathology at re-operation was applied in 67% (13/18) of studies. The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data. Ten studies (10/18, 56%) had internal or external hold-out test set data that could be included in a meta-analysis of monitoring biomarker studies. The pooled sensitivity was 0.77 (0.65–0.86). The pooled false positive rate (1-specificity) was 0.35 (0.25–0.47). The summary point estimate for the AUC was 0.77. CONCLUSION There is likely good diagnostic performance of machine learning models that use MRI features to distinguish between progression and mimics. The diagnostic performance of ML using implicit features did not appear to be superior to ML using explicit features. There are a range of ML-based solutions poised to become treatment response monitoring biomarkers for glioblastoma. To achieve this, the development and validation of ML models require large, well-annotated datasets where the potential for confounding in the study design has been carefully considered. Therefore, multidisciplinary efforts and multicentre collaborations are necessary.
Related Topics
Concepts
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1093/neuonc/noab180.036
- https://academic.oup.com/neuro-oncology/article-pdf/23/Supplement_2/ii11/40329600/noab180.036.pdf
- OA Status
- bronze
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3201455146
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3201455146Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/neuonc/noab180.036Digital Object Identifier
- Title
-
OS08.6.A Glioblastoma treatment response machine learning monitoring biomarkers: a systematic review and meta-analysisWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-01Full publication date if available
- Authors
-
Thomas C. Booth, Alysha Chelliah, Andrei Roman, Ayisha Al Busaidi, Haris Shuaib, Aysha Luis, Ayesha Mirchandani, Burcu Alparslan, Nina Mansoor, K. Ashkan, Sébastien Ourselin, Marc Modat, Mariusz GrzedaList of authors in order
- Landing page
-
https://doi.org/10.1093/neuonc/noab180.036Publisher landing page
- PDF URL
-
https://academic.oup.com/neuro-oncology/article-pdf/23/Supplement_2/ii11/40329600/noab180.036.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/neuro-oncology/article-pdf/23/Supplement_2/ii11/40329600/noab180.036.pdfDirect OA link when available
- Concepts
-
Medicine, Meta-analysis, Contingency table, MEDLINE, Temozolomide, Receiver operating characteristic, Confounding, Systematic review, Bivariate analysis, Publication bias, Radiation therapy, Oncology, Medical physics, Internal medicine, Machine learning, Computer science, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3201455146 |
|---|---|
| doi | https://doi.org/10.1093/neuonc/noab180.036 |
| ids.doi | https://doi.org/10.1093/neuonc/noab180.036 |
| ids.mag | 3201455146 |
| ids.openalex | https://openalex.org/W3201455146 |
| fwci | 0.0 |
| type | review |
| title | OS08.6.A Glioblastoma treatment response machine learning monitoring biomarkers: a systematic review and meta-analysis |
| biblio.issue | Supplement_2 |
| biblio.volume | 23 |
| biblio.last_page | ii12 |
| biblio.first_page | ii11 |
| topics[0].id | https://openalex.org/T12422 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9959999918937683 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Radiomics and Machine Learning in Medical Imaging |
| topics[1].id | https://openalex.org/T12702 |
| topics[1].field.id | https://openalex.org/fields/28 |
| topics[1].field.display_name | Neuroscience |
| topics[1].score | 0.9602000117301941 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2808 |
| topics[1].subfield.display_name | Neurology |
| topics[1].display_name | Brain Tumor Detection and Classification |
| topics[2].id | https://openalex.org/T10129 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9577000141143799 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2716 |
| topics[2].subfield.display_name | Genetics |
| topics[2].display_name | Glioma Diagnosis and Treatment |
| is_xpac | False |
| apc_list.value | 4612 |
| apc_list.currency | USD |
| apc_list.value_usd | 4612 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C71924100 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6730466485023499 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[0].display_name | Medicine |
| concepts[1].id | https://openalex.org/C95190672 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5959466099739075 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q815382 |
| concepts[1].display_name | Meta-analysis |
| concepts[2].id | https://openalex.org/C91998498 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5780825614929199 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1541178 |
| concepts[2].display_name | Contingency table |
| concepts[3].id | https://openalex.org/C2779473830 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5642051696777344 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1540899 |
| concepts[3].display_name | MEDLINE |
| concepts[4].id | https://openalex.org/C2777389519 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5468804240226746 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q425088 |
| concepts[4].display_name | Temozolomide |
| concepts[5].id | https://openalex.org/C58471807 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5049722790718079 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q327120 |
| concepts[5].display_name | Receiver operating characteristic |
| concepts[6].id | https://openalex.org/C77350462 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5029141306877136 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1125472 |
| concepts[6].display_name | Confounding |
| concepts[7].id | https://openalex.org/C189708586 |
| concepts[7].level | 3 |
| concepts[7].score | 0.4641590714454651 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1504425 |
| concepts[7].display_name | Systematic review |
| concepts[8].id | https://openalex.org/C64341305 |
| concepts[8].level | 2 |
| concepts[8].score | 0.463102787733078 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q4919225 |
| concepts[8].display_name | Bivariate analysis |
| concepts[9].id | https://openalex.org/C2780439572 |
| concepts[9].level | 3 |
| concepts[9].score | 0.43240416049957275 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q919364 |
| concepts[9].display_name | Publication bias |
| concepts[10].id | https://openalex.org/C509974204 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4190016984939575 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q180507 |
| concepts[10].display_name | Radiation therapy |
| concepts[11].id | https://openalex.org/C143998085 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3413393497467041 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q162555 |
| concepts[11].display_name | Oncology |
| concepts[12].id | https://openalex.org/C19527891 |
| concepts[12].level | 1 |
| concepts[12].score | 0.33488357067108154 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1120908 |
| concepts[12].display_name | Medical physics |
| concepts[13].id | https://openalex.org/C126322002 |
| concepts[13].level | 1 |
| concepts[13].score | 0.33221518993377686 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[13].display_name | Internal medicine |
| concepts[14].id | https://openalex.org/C119857082 |
| concepts[14].level | 1 |
| concepts[14].score | 0.2659327983856201 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[14].display_name | Machine learning |
| concepts[15].id | https://openalex.org/C41008148 |
| concepts[15].level | 0 |
| concepts[15].score | 0.12375524640083313 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[15].display_name | Computer science |
| concepts[16].id | https://openalex.org/C17744445 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[16].display_name | Political science |
| concepts[17].id | https://openalex.org/C199539241 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[17].display_name | Law |
| keywords[0].id | https://openalex.org/keywords/medicine |
| keywords[0].score | 0.6730466485023499 |
| keywords[0].display_name | Medicine |
| keywords[1].id | https://openalex.org/keywords/meta-analysis |
| keywords[1].score | 0.5959466099739075 |
| keywords[1].display_name | Meta-analysis |
| keywords[2].id | https://openalex.org/keywords/contingency-table |
| keywords[2].score | 0.5780825614929199 |
| keywords[2].display_name | Contingency table |
| keywords[3].id | https://openalex.org/keywords/medline |
| keywords[3].score | 0.5642051696777344 |
| keywords[3].display_name | MEDLINE |
| keywords[4].id | https://openalex.org/keywords/temozolomide |
| keywords[4].score | 0.5468804240226746 |
| keywords[4].display_name | Temozolomide |
| keywords[5].id | https://openalex.org/keywords/receiver-operating-characteristic |
| keywords[5].score | 0.5049722790718079 |
| keywords[5].display_name | Receiver operating characteristic |
| keywords[6].id | https://openalex.org/keywords/confounding |
| keywords[6].score | 0.5029141306877136 |
| keywords[6].display_name | Confounding |
| keywords[7].id | https://openalex.org/keywords/systematic-review |
| keywords[7].score | 0.4641590714454651 |
| keywords[7].display_name | Systematic review |
| keywords[8].id | https://openalex.org/keywords/bivariate-analysis |
| keywords[8].score | 0.463102787733078 |
| keywords[8].display_name | Bivariate analysis |
| keywords[9].id | https://openalex.org/keywords/publication-bias |
| keywords[9].score | 0.43240416049957275 |
| keywords[9].display_name | Publication bias |
| keywords[10].id | https://openalex.org/keywords/radiation-therapy |
| keywords[10].score | 0.4190016984939575 |
| keywords[10].display_name | Radiation therapy |
| keywords[11].id | https://openalex.org/keywords/oncology |
| keywords[11].score | 0.3413393497467041 |
| keywords[11].display_name | Oncology |
| keywords[12].id | https://openalex.org/keywords/medical-physics |
| keywords[12].score | 0.33488357067108154 |
| keywords[12].display_name | Medical physics |
| keywords[13].id | https://openalex.org/keywords/internal-medicine |
| keywords[13].score | 0.33221518993377686 |
| keywords[13].display_name | Internal medicine |
| keywords[14].id | https://openalex.org/keywords/machine-learning |
| keywords[14].score | 0.2659327983856201 |
| keywords[14].display_name | Machine learning |
| keywords[15].id | https://openalex.org/keywords/computer-science |
| keywords[15].score | 0.12375524640083313 |
| keywords[15].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.1093/neuonc/noab180.036 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S106908163 |
| locations[0].source.issn | 1522-8517, 1523-5866 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1522-8517 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Neuro-Oncology |
| locations[0].source.host_organization | https://openalex.org/P4310311648 |
| locations[0].source.host_organization_name | Oxford University Press |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| locations[0].source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| locations[0].license | |
| locations[0].pdf_url | https://academic.oup.com/neuro-oncology/article-pdf/23/Supplement_2/ii11/40329600/noab180.036.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Neuro-Oncology |
| locations[0].landing_page_url | https://doi.org/10.1093/neuonc/noab180.036 |
| locations[1].id | pmh:oai:pubmedcentral.nih.gov:8427453 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S2764455111 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed Central |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Neuro Oncol |
| locations[1].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8427453 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5003607819 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0984-3998 |
| authorships[0].author.display_name | Thomas C. Booth |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I183935753 |
| authorships[0].affiliations[0].raw_affiliation_string | King’s College London, London, United Kingdom |
| authorships[0].institutions[0].id | https://openalex.org/I183935753 |
| authorships[0].institutions[0].ror | https://ror.org/0220mzb33 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I183935753 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | King's College London |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | T C Booth |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | King’s College London, London, United Kingdom |
| authorships[1].author.id | https://openalex.org/A5048217578 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0867-1565 |
| authorships[1].author.display_name | Alysha Chelliah |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I183935753 |
| authorships[1].affiliations[0].raw_affiliation_string | King’s College London, London, United Kingdom |
| authorships[1].institutions[0].id | https://openalex.org/I183935753 |
| authorships[1].institutions[0].ror | https://ror.org/0220mzb33 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I183935753 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | King's College London |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | A Chelliah |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | King’s College London, London, United Kingdom |
| authorships[2].author.id | https://openalex.org/A5034620211 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5340-5325 |
| authorships[2].author.display_name | Andrei Roman |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I200166805 |
| authorships[2].affiliations[0].raw_affiliation_string | Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom |
| authorships[2].institutions[0].id | https://openalex.org/I200166805 |
| authorships[2].institutions[0].ror | https://ror.org/00j161312 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I200166805 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | Guy's and St Thomas' NHS Foundation Trust |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | A Roman |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom |
| authorships[3].author.id | https://openalex.org/A5025449701 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3539-3743 |
| authorships[3].author.display_name | Ayisha Al Busaidi |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210111135 |
| authorships[3].affiliations[0].raw_affiliation_string | King’s College Hospital NHS Foundation Trust, London, United Kingdom |
| authorships[3].institutions[0].id | https://openalex.org/I4210111135 |
| authorships[3].institutions[0].ror | https://ror.org/01n0k5m85 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210111135 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | King's College Hospital NHS Foundation Trust |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | A Al Busaidi |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | King’s College Hospital NHS Foundation Trust, London, United Kingdom |
| authorships[4].author.id | https://openalex.org/A5090413585 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6975-5960 |
| authorships[4].author.display_name | Haris Shuaib |
| authorships[4].countries | GB |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I200166805 |
| authorships[4].affiliations[0].raw_affiliation_string | Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom |
| authorships[4].institutions[0].id | https://openalex.org/I200166805 |
| authorships[4].institutions[0].ror | https://ror.org/00j161312 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I200166805 |
| authorships[4].institutions[0].country_code | GB |
| authorships[4].institutions[0].display_name | Guy's and St Thomas' NHS Foundation Trust |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | H Shuaib |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom |
| authorships[5].author.id | https://openalex.org/A5040046638 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-5493-099X |
| authorships[5].author.display_name | Aysha Luis |
| authorships[5].countries | GB |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I183935753 |
| authorships[5].affiliations[0].raw_affiliation_string | King’s College London, London, United Kingdom |
| authorships[5].institutions[0].id | https://openalex.org/I183935753 |
| authorships[5].institutions[0].ror | https://ror.org/0220mzb33 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I183935753 |
| authorships[5].institutions[0].country_code | GB |
| authorships[5].institutions[0].display_name | King's College London |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | A Luis |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | King’s College London, London, United Kingdom |
| authorships[6].author.id | https://openalex.org/A5004354959 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-6664-9345 |
| authorships[6].author.display_name | Ayesha Mirchandani |
| authorships[6].countries | GB |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I2802466933 |
| authorships[6].affiliations[0].raw_affiliation_string | Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom |
| authorships[6].institutions[0].id | https://openalex.org/I2802466933 |
| authorships[6].institutions[0].ror | https://ror.org/04v54gj93 |
| authorships[6].institutions[0].type | healthcare |
| authorships[6].institutions[0].lineage | https://openalex.org/I2802466933 |
| authorships[6].institutions[0].country_code | GB |
| authorships[6].institutions[0].display_name | Cambridge University Hospitals NHS Foundation Trust |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | A Mirchandani |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom |
| authorships[7].author.id | https://openalex.org/A5064952656 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-5518-341X |
| authorships[7].author.display_name | Burcu Alparslan |
| authorships[7].countries | GB |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210111135 |
| authorships[7].affiliations[0].raw_affiliation_string | King’s College Hospital NHS Foundation Trust, London, United Kingdom |
| authorships[7].institutions[0].id | https://openalex.org/I4210111135 |
| authorships[7].institutions[0].ror | https://ror.org/01n0k5m85 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210111135 |
| authorships[7].institutions[0].country_code | GB |
| authorships[7].institutions[0].display_name | King's College Hospital NHS Foundation Trust |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | B Alparslan |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | King’s College Hospital NHS Foundation Trust, London, United Kingdom |
| authorships[8].author.id | https://openalex.org/A5011502507 |
| authorships[8].author.orcid | |
| authorships[8].author.display_name | Nina Mansoor |
| authorships[8].countries | GB |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I4210111135 |
| authorships[8].affiliations[0].raw_affiliation_string | King’s College Hospital NHS Foundation Trust, London, United Kingdom |
| authorships[8].institutions[0].id | https://openalex.org/I4210111135 |
| authorships[8].institutions[0].ror | https://ror.org/01n0k5m85 |
| authorships[8].institutions[0].type | healthcare |
| authorships[8].institutions[0].lineage | https://openalex.org/I4210111135 |
| authorships[8].institutions[0].country_code | GB |
| authorships[8].institutions[0].display_name | King's College Hospital NHS Foundation Trust |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | N Mansoor |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | King’s College Hospital NHS Foundation Trust, London, United Kingdom |
| authorships[9].author.id | https://openalex.org/A5113650869 |
| authorships[9].author.orcid | |
| authorships[9].author.display_name | K. Ashkan |
| authorships[9].countries | GB |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I4210111135 |
| authorships[9].affiliations[0].raw_affiliation_string | King’s College Hospital NHS Foundation Trust, London, United Kingdom |
| authorships[9].institutions[0].id | https://openalex.org/I4210111135 |
| authorships[9].institutions[0].ror | https://ror.org/01n0k5m85 |
| authorships[9].institutions[0].type | healthcare |
| authorships[9].institutions[0].lineage | https://openalex.org/I4210111135 |
| authorships[9].institutions[0].country_code | GB |
| authorships[9].institutions[0].display_name | King's College Hospital NHS Foundation Trust |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | K Ashkan |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | King’s College Hospital NHS Foundation Trust, London, United Kingdom |
| authorships[10].author.id | https://openalex.org/A5082106258 |
| authorships[10].author.orcid | https://orcid.org/0000-0002-5694-5340 |
| authorships[10].author.display_name | Sébastien Ourselin |
| authorships[10].countries | GB |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I183935753 |
| authorships[10].affiliations[0].raw_affiliation_string | King’s College London, London, United Kingdom |
| authorships[10].institutions[0].id | https://openalex.org/I183935753 |
| authorships[10].institutions[0].ror | https://ror.org/0220mzb33 |
| authorships[10].institutions[0].type | education |
| authorships[10].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I183935753 |
| authorships[10].institutions[0].country_code | GB |
| authorships[10].institutions[0].display_name | King's College London |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | S Ourselin |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | King’s College London, London, United Kingdom |
| authorships[11].author.id | https://openalex.org/A5018652821 |
| authorships[11].author.orcid | https://orcid.org/0000-0002-5277-8530 |
| authorships[11].author.display_name | Marc Modat |
| authorships[11].countries | GB |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I183935753 |
| authorships[11].affiliations[0].raw_affiliation_string | King’s College London, London, United Kingdom |
| authorships[11].institutions[0].id | https://openalex.org/I183935753 |
| authorships[11].institutions[0].ror | https://ror.org/0220mzb33 |
| authorships[11].institutions[0].type | education |
| authorships[11].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I183935753 |
| authorships[11].institutions[0].country_code | GB |
| authorships[11].institutions[0].display_name | King's College London |
| authorships[11].author_position | middle |
| authorships[11].raw_author_name | M Modat |
| authorships[11].is_corresponding | False |
| authorships[11].raw_affiliation_strings | King’s College London, London, United Kingdom |
| authorships[12].author.id | https://openalex.org/A5023417526 |
| authorships[12].author.orcid | |
| authorships[12].author.display_name | Mariusz Grzeda |
| authorships[12].countries | GB |
| authorships[12].affiliations[0].institution_ids | https://openalex.org/I183935753 |
| authorships[12].affiliations[0].raw_affiliation_string | King’s College London, London, United Kingdom |
| authorships[12].institutions[0].id | https://openalex.org/I183935753 |
| authorships[12].institutions[0].ror | https://ror.org/0220mzb33 |
| authorships[12].institutions[0].type | education |
| authorships[12].institutions[0].lineage | https://openalex.org/I124357947, https://openalex.org/I183935753 |
| authorships[12].institutions[0].country_code | GB |
| authorships[12].institutions[0].display_name | King's College London |
| authorships[12].author_position | last |
| authorships[12].raw_author_name | M Grzeda |
| authorships[12].is_corresponding | False |
| authorships[12].raw_affiliation_strings | King’s College London, London, United Kingdom |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://academic.oup.com/neuro-oncology/article-pdf/23/Supplement_2/ii11/40329600/noab180.036.pdf |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | OS08.6.A Glioblastoma treatment response machine learning monitoring biomarkers: a systematic review and meta-analysis |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12422 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9959999918937683 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Radiomics and Machine Learning in Medical Imaging |
| related_works | https://openalex.org/W2235074116, https://openalex.org/W2022503084, https://openalex.org/W2019927904, https://openalex.org/W2041906258, https://openalex.org/W2900295007, https://openalex.org/W1975919648, https://openalex.org/W573094082, https://openalex.org/W2913538305, https://openalex.org/W2019296828, https://openalex.org/W2061602261 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1093/neuonc/noab180.036 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S106908163 |
| best_oa_location.source.issn | 1522-8517, 1523-5866 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1522-8517 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Neuro-Oncology |
| best_oa_location.source.host_organization | https://openalex.org/P4310311648 |
| best_oa_location.source.host_organization_name | Oxford University Press |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| best_oa_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://academic.oup.com/neuro-oncology/article-pdf/23/Supplement_2/ii11/40329600/noab180.036.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Neuro-Oncology |
| best_oa_location.landing_page_url | https://doi.org/10.1093/neuonc/noab180.036 |
| primary_location.id | doi:10.1093/neuonc/noab180.036 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S106908163 |
| primary_location.source.issn | 1522-8517, 1523-5866 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1522-8517 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Neuro-Oncology |
| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| primary_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| primary_location.license | |
| primary_location.pdf_url | https://academic.oup.com/neuro-oncology/article-pdf/23/Supplement_2/ii11/40329600/noab180.036.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Neuro-Oncology |
| primary_location.landing_page_url | https://doi.org/10.1093/neuonc/noab180.036 |
| publication_date | 2021-09-01 |
| publication_year | 2021 |
| referenced_works_count | 0 |
| abstract_inverted_index.- | 97 |
| abstract_inverted_index.A | 111, 133 |
| abstract_inverted_index.a | 138, 177, 264, 336 |
| abstract_inverted_index.2. | 110 |
| abstract_inverted_index.ML | 319, 330, 358 |
| abstract_inverted_index.To | 170, 350 |
| abstract_inverted_index.at | 188 |
| abstract_inverted_index.be | 242, 261, 327 |
| abstract_inverted_index.by | 54 |
| abstract_inverted_index.in | 24, 162, 167, 192, 203, 214, 219, 263, 369 |
| abstract_inverted_index.is | 296 |
| abstract_inverted_index.of | 4, 18, 102, 183, 195, 200, 208, 212, 235, 266, 301, 318, 338, 357 |
| abstract_inverted_index.on | 14 |
| abstract_inverted_index.or | 176, 253 |
| abstract_inverted_index.to | 9, 86, 221, 229, 309, 326, 329, 342 |
| abstract_inverted_index.384 | 166 |
| abstract_inverted_index.67% | 193 |
| abstract_inverted_index.AND | 32 |
| abstract_inverted_index.AUC | 291 |
| abstract_inverted_index.MRI | 307 |
| abstract_inverted_index.Ten | 247 |
| abstract_inverted_index.The | 2, 197, 270, 276, 285, 315 |
| abstract_inverted_index.aim | 3 |
| abstract_inverted_index.and | 50, 78, 81, 104, 125, 146, 165, 186, 210, 225, 231, 313, 355, 380 |
| abstract_inverted_index.any | 115 |
| abstract_inverted_index.are | 335, 383 |
| abstract_inverted_index.can | 241 |
| abstract_inverted_index.did | 323 |
| abstract_inverted_index.due | 228 |
| abstract_inverted_index.for | 46, 121, 141, 289, 348, 367 |
| abstract_inverted_index.had | 69, 251 |
| abstract_inverted_index.has | 373 |
| abstract_inverted_index.low | 233 |
| abstract_inverted_index.not | 324 |
| abstract_inverted_index.set | 257 |
| abstract_inverted_index.the | 5, 51, 98, 148, 179, 205, 215, 222, 232, 245, 290, 353, 365, 370 |
| abstract_inverted_index.two | 55 |
| abstract_inverted_index.use | 306 |
| abstract_inverted_index.was | 8, 36, 106, 135, 174, 190, 273, 282, 292 |
| abstract_inverted_index.who | 68 |
| abstract_inverted_index.0.35 | 283 |
| abstract_inverted_index.0.77 | 274 |
| abstract_inverted_index.1335 | 160 |
| abstract_inverted_index.56%) | 250 |
| abstract_inverted_index.Risk | 101 |
| abstract_inverted_index.been | 374 |
| abstract_inverted_index.bias | 103, 209 |
| abstract_inverted_index.data | 258 |
| abstract_inverted_index.from | 94, 244 |
| abstract_inverted_index.good | 298 |
| abstract_inverted_index.high | 65, 206 |
| abstract_inverted_index.rate | 145, 280 |
| abstract_inverted_index.risk | 207 |
| abstract_inverted_index.sets | 124, 164 |
| abstract_inverted_index.test | 16, 123, 168, 256 |
| abstract_inverted_index.that | 238, 259, 305 |
| abstract_inverted_index.were | 44, 61, 119, 157 |
| abstract_inverted_index.with | 64, 76, 108, 159 |
| abstract_inverted_index.(ML). | 30 |
| abstract_inverted_index.0.77. | 293 |
| abstract_inverted_index.There | 295, 334 |
| abstract_inverted_index.adult | 62 |
| abstract_inverted_index.could | 260 |
| abstract_inverted_index.curve | 152 |
| abstract_inverted_index.data. | 246 |
| abstract_inverted_index.drawn | 243 |
| abstract_inverted_index.false | 143, 278 |
| abstract_inverted_index.grade | 66 |
| abstract_inverted_index.level | 234 |
| abstract_inverted_index.model | 140 |
| abstract_inverted_index.point | 287 |
| abstract_inverted_index.range | 337 |
| abstract_inverted_index.sets. | 169 |
| abstract_inverted_index.small | 198 |
| abstract_inverted_index.study | 59, 216, 371 |
| abstract_inverted_index.there | 173 |
| abstract_inverted_index.third | 112 |
| abstract_inverted_index.this, | 352 |
| abstract_inverted_index.using | 47, 137, 320, 331 |
| abstract_inverted_index.where | 364 |
| abstract_inverted_index.(AUC). | 153 |
| abstract_inverted_index.(since | 41 |
| abstract_inverted_index.PRISMA | 34 |
| abstract_inverted_index.QUADAS | 109 |
| abstract_inverted_index.appear | 325 |
| abstract_inverted_index.assess | 10 |
| abstract_inverted_index.become | 343 |
| abstract_inverted_index.design | 372 |
| abstract_inverted_index.glioma | 67 |
| abstract_inverted_index.large, | 361 |
| abstract_inverted_index.likely | 297 |
| abstract_inverted_index.mimic, | 178 |
| abstract_inverted_index.mimics | 96 |
| abstract_inverted_index.models | 304, 359 |
| abstract_inverted_index.poised | 341 |
| abstract_inverted_index.pooled | 271, 277 |
| abstract_inverted_index.review | 7 |
| abstract_inverted_index.status | 90 |
| abstract_inverted_index.tables | 118 |
| abstract_inverted_index.target | 99 |
| abstract_inverted_index.(10/18, | 249 |
| abstract_inverted_index.(13/18) | 194 |
| abstract_inverted_index.EMBASE, | 49 |
| abstract_inverted_index.METHODS | 33 |
| abstract_inverted_index.RESULTS | 154 |
| abstract_inverted_index.achieve | 351 |
| abstract_inverted_index.adults, | 25 |
| abstract_inverted_index.applied | 191 |
| abstract_inverted_index.between | 311 |
| abstract_inverted_index.created | 120 |
| abstract_inverted_index.designs | 217 |
| abstract_inverted_index.efforts | 379 |
| abstract_inverted_index.imaging | 85, 185 |
| abstract_inverted_index.limited | 239 |
| abstract_inverted_index.machine | 28, 302 |
| abstract_inverted_index.mimics. | 314 |
| abstract_inverted_index.numbers | 199 |
| abstract_inverted_index.patient | 201, 226 |
| abstract_inverted_index.recall, | 126, 142 |
| abstract_inverted_index.require | 360 |
| abstract_inverted_index.studies | 13, 156, 248 |
| abstract_inverted_index.suggest | 237 |
| abstract_inverted_index.summary | 286 |
| abstract_inverted_index.through | 27 |
| abstract_inverted_index.whether | 172 |
| abstract_inverted_index.(maximal | 73 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Articles | 38 |
| abstract_inverted_index.Cochrane | 52 |
| abstract_inverted_index.Eighteen | 155 |
| abstract_inverted_index.Included | 58 |
| abstract_inverted_index.MATERIAL | 31 |
| abstract_inverted_index.MEDLINE, | 48 |
| abstract_inverted_index.ML-based | 339 |
| abstract_inverted_index.Register | 53 |
| abstract_inverted_index.accuracy | 17, 131 |
| abstract_inverted_index.adjuvant | 79 |
| abstract_inverted_index.assessed | 107 |
| abstract_inverted_index.balanced | 130 |
| abstract_inverted_index.concerns | 211 |
| abstract_inverted_index.datasets | 363 |
| abstract_inverted_index.estimate | 288 |
| abstract_inverted_index.explicit | 332 |
| abstract_inverted_index.external | 254 |
| abstract_inverted_index.features | 308, 322 |
| abstract_inverted_index.hold-out | 122, 255 |
| abstract_inverted_index.implicit | 321 |
| abstract_inverted_index.included | 158, 202, 262 |
| abstract_inverted_index.internal | 252 |
| abstract_inverted_index.learning | 29, 303 |
| abstract_inverted_index.operator | 150 |
| abstract_inverted_index.patients | 63, 161 |
| abstract_inverted_index.positive | 144, 279 |
| abstract_inverted_index.previous | 42 |
| abstract_inverted_index.receiver | 149 |
| abstract_inverted_index.recently | 11 |
| abstract_inverted_index.relation | 220 |
| abstract_inverted_index.response | 21, 89, 345 |
| abstract_inverted_index.reviewer | 113 |
| abstract_inverted_index.reviews) | 43 |
| abstract_inverted_index.searched | 45 |
| abstract_inverted_index.standard | 71, 181, 224 |
| abstract_inverted_index.studies, | 204 |
| abstract_inverted_index.studies. | 196, 269 |
| abstract_inverted_index.superior | 328 |
| abstract_inverted_index.training | 163 |
| abstract_inverted_index.F1-score, | 129 |
| abstract_inverted_index.biomarker | 268 |
| abstract_inverted_index.bivariate | 139 |
| abstract_inverted_index.carefully | 375 |
| abstract_inverted_index.determine | 87, 171 |
| abstract_inverted_index.developed | 26 |
| abstract_inverted_index.evidence, | 236 |
| abstract_inverted_index.features. | 333 |
| abstract_inverted_index.follow-up | 84, 184 |
| abstract_inverted_index.followed. | 37 |
| abstract_inverted_index.performed | 136 |
| abstract_inverted_index.potential | 366 |
| abstract_inverted_index.published | 12, 39 |
| abstract_inverted_index.reference | 180, 223 |
| abstract_inverted_index.reviewers | 56 |
| abstract_inverted_index.selection | 227 |
| abstract_inverted_index.solutions | 340 |
| abstract_inverted_index.treatment | 20, 72, 88, 344 |
| abstract_inverted_index.undergone | 70 |
| abstract_inverted_index.underwent | 83 |
| abstract_inverted_index.BACKGROUND | 1 |
| abstract_inverted_index.CONCLUSION | 294 |
| abstract_inverted_index.Therefore, | 377 |
| abstract_inverted_index.arbitrated | 114 |
| abstract_inverted_index.area-under | 147 |
| abstract_inverted_index.biomarkers | 23, 347 |
| abstract_inverted_index.diagnostic | 15, 299, 316 |
| abstract_inverted_index.monitoring | 22, 267, 346 |
| abstract_inverted_index.necessary. | 384 |
| abstract_inverted_index.precision, | 128 |
| abstract_inverted_index.resection, | 74 |
| abstract_inverted_index.systematic | 6 |
| abstract_inverted_index.validation | 356 |
| abstract_inverted_index.Contingency | 117 |
| abstract_inverted_index.calculated. | 132 |
| abstract_inverted_index.combination | 182 |
| abstract_inverted_index.conclusions | 240 |
| abstract_inverted_index.concomitant | 77 |
| abstract_inverted_index.condition). | 100 |
| abstract_inverted_index.confounding | 368 |
| abstract_inverted_index.considered. | 376 |
| abstract_inverted_index.development | 354 |
| abstract_inverted_index.distinguish | 310 |
| abstract_inverted_index.methodology | 35 |
| abstract_inverted_index.multicentre | 381 |
| abstract_inverted_index.performance | 300, 317 |
| abstract_inverted_index.progression | 175, 312 |
| abstract_inverted_index.sensitivity | 272 |
| abstract_inverted_index.discrepancy. | 116 |
| abstract_inverted_index.glioblastoma | 19 |
| abstract_inverted_index.participants | 60 |
| abstract_inverted_index.radiotherapy | 75 |
| abstract_inverted_index.re-operation | 189 |
| abstract_inverted_index.specificity, | 127 |
| abstract_inverted_index.subsequently | 82 |
| abstract_inverted_index.(particularly | 218 |
| abstract_inverted_index.applicability | 105, 213 |
| abstract_inverted_index.confounding), | 230 |
| abstract_inverted_index.glioblastoma. | 349 |
| abstract_inverted_index.meta-analysis | 134, 265 |
| abstract_inverted_index.temozolomide) | 80 |
| abstract_inverted_index.(0.25–0.47). | 284 |
| abstract_inverted_index.(0.65–0.86). | 275 |
| abstract_inverted_index.(specifically, | 91 |
| abstract_inverted_index.characteristic | 151 |
| abstract_inverted_index.collaborations | 382 |
| abstract_inverted_index.distinguishing | 92 |
| abstract_inverted_index.histopathology | 187 |
| abstract_inverted_index.independently. | 57 |
| abstract_inverted_index.well-annotated | 362 |
| abstract_inverted_index.(1-specificity) | 281 |
| abstract_inverted_index.09/2018-01/2021 | 40 |
| abstract_inverted_index.multidisciplinary | 378 |
| abstract_inverted_index.progression/recurrence | 93, 95 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.17798907 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |