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Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy

Cancer Imaging(2025)10.1186/s40644-024-00818-0Source: DataRank Database

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy is a research paper published in Cancer Imaging (2025). On theSindex it has a DataRank of 0. It has been cited 7 times.

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Open Access7 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)Impact (FWCI 4.94)OA: goldTopics (78)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (10)

Clifford Lindsay ORCID,Chenxi Zhang ORCID,Ji‐Soo Kim ORCID,Xin Chen ORCID,Jing Li ORCID

Live enrichment

PubMed:39838503 PMC:PMC11752626 OpenAlex:https://openalex.org/W4406665870

National Institutes of Health awards

Grant: R01LM012434, R01LM013891, and R01LM013772.

NLM NIH HHS

Grant: R01 LM012434

NLM NIH HHS

Grant: R01 LM013772

NLM NIH HHS

Grant: R01 LM013891

National Institutes of Health

Grant: 1R01LM012434-01

Computer aided diagnosis of cancer metastases in the brain

National Institutes of Health

Grant: 1R01LM013891-01A1

A New Informatics Approach for Detection of Cerebrovascular Abnormalities

National Institutes of Health

Grant: 1R01LM013772-01A1

Targeted Neural Text Summarization of Electronic Medical Records to Improve Imaging Diagnostics

FWCI

4.94

Citation Percentile

1.0%

Citation Trend

2025

goldLicense: cc-by

journalhttps://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-024-00818-0

GOLDhttps://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-024-00818-0

publisherhttps://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-024-00818-0

publisherhttps://link.springer.com/content/pdf/10.1186/s40644-024-00818-0.pdf

publisherhttps://link.springer.com/article/10.1186/s40644-024-00818-0/fulltext.html

Fields of Study

Glioma Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingBrain Metastases and TreatmentMedicine02 engineering and technology0202 electrical engineering, electronic engineering, information engineeringHumansDeep LearningBrain NeoplasmsRetrospective StudiesGliomaMaleFemaleImmunotherapyMiddle AgedNeoplasm Recurrence, LocalNeural Networks, ComputerAdultPrognosisAgedMagnetic Resonance ImagingNeoplasm GradingRadiomics

MeSH Terms

Deep LearningRadiomicsAdultAgedBrain NeoplasmsFemaleGliomaHumansImmunotherapyMagnetic Resonance ImagingMaleMiddle AgedNeoplasm Recurrence, LocalPrognosisRetrospective StudiesNeural Networks, ComputerNeoplasm Grading

Keywords

Convolutional neural networkArtificial intelligenceReceiver operating characteristicMedicineRadiomicsSegmentationGliomaIntraclass correlationSupport vector machineCross-validationPattern recognition (psychology)Feature selectionDeep learningData setComputer scienceInternal medicineOverall SurvivalHigh-grade GliomaConvolutional Neural NetworksMaleAdultR895-920Medical physics. Medical radiology. Nuclear medicineHumansRC254-282Retrospective StudiesAgedBrain NeoplasmsResearchNeoplasms. Tumors. Oncology. Including cancer and carcinogensMiddle AgedPrognosisMagnetic Resonance ImagingFemaleImmunotherapyNeural Networks, ComputerNeoplasm Recurrence, LocalNeoplasm Grading

Sustainable Development Goals

SDG 0: Good health and well-being