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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorRathore, Saima-
dc.contributor.authorIftikhar, Muhammad A.-
dc.contributor.authorChaddad, Ahmad-
dc.contributor.authorSingh, Ashish-
dc.contributor.authorGillani, Zeeshan-
dc.contributor.authorAbdulkadir, Ahmed-
dc.date.accessioned2023-10-27T09:08:06Z-
dc.date.available2023-10-27T09:08:06Z-
dc.date.issued2023-09-16-
dc.identifier.issn0169-2607de_CH
dc.identifier.issn1872-7565de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28968-
dc.description.abstractBackground: Magnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy. Purpose: To evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients. Materials and methods: Multi-contrast MRI, comprising T1-weighted, T1-weighted post-contrast, T2-weighted, T2 fluid-attenuated-inversion-recovery, and pathology images from glioma patients (n = 160) were retrospectively collected (1983–2008) from TCGA alongside age and sex. Phenotypic profiling of tumors was performed by quantifying the radiographic and histopathologic descriptors extracted from the delineated region-of-interest in MRI and PATH images. A Cox proportional hazard model was trained with the MRI and PATH features, IDH mutation status, and basic demographic variables (age and sex) to predict OS. The performance was evaluated in a split-train-test configuration using the concordance-index, computed between the predicted risk score and observed OS. Results: The average age of patients was 51.2years (women: n = 77, age-range=18–84years; men: n = 83, age-range=21–80years). The median OS of the participants was 494.5 (range,3–4752), 481 (range,7–4752), and 524.5 days (range,3–2869), respectively, in complete dataset, training, and test datasets. The addition of MRI or PATH features improved prediction of OS when compared to models based on age, sex, and mutation status alone or their combination (p < 0.001). The full multi-omics model integrated MRI, PATH, clinical, and genetic profiles and predicted the OS best (c-index= 0.87). Conclusion: The combination of imaging, genetic, and clinical profiles leads to a more accurate prognosis than the clinical and/or mutation status.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofComputer Methods and Programs in Biomedicinede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectClinical measurede_CH
dc.subjectDigital histopathology imagede_CH
dc.subjectGenomic markerde_CH
dc.subjectGliomade_CH
dc.subjectMachine learningde_CH
dc.subjectMulti-omicsde_CH
dc.subjectRadiographic imagede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc610.28: Biomedizin, Biomedizinische Technikde_CH
dc.titleImaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profilesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
dc.identifier.doi10.1016/j.cmpb.2023.107812de_CH
dc.identifier.pmid37757566de_CH
zhaw.funding.euNode_CH
zhaw.issue107812de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume242de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf206795de_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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Rathore, S., Iftikhar, M. A., Chaddad, A., Singh, A., Gillani, Z., & Abdulkadir, A. (2023). Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles. Computer Methods and Programs in Biomedicine, 242(107812). https://doi.org/10.1016/j.cmpb.2023.107812
Rathore, S. et al. (2023) ‘Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles’, Computer Methods and Programs in Biomedicine, 242(107812). Available at: https://doi.org/10.1016/j.cmpb.2023.107812.
S. Rathore, M. A. Iftikhar, A. Chaddad, A. Singh, Z. Gillani, and A. Abdulkadir, “Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles,” Computer Methods and Programs in Biomedicine, vol. 242, no. 107812, Sep. 2023, doi: 10.1016/j.cmpb.2023.107812.
RATHORE, Saima, Muhammad A. IFTIKHAR, Ahmad CHADDAD, Ashish SINGH, Zeeshan GILLANI und Ahmed ABDULKADIR, 2023. Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles. Computer Methods and Programs in Biomedicine. 16 September 2023. Bd. 242, Nr. 107812. DOI 10.1016/j.cmpb.2023.107812
Rathore, Saima, Muhammad A. Iftikhar, Ahmad Chaddad, Ashish Singh, Zeeshan Gillani, and Ahmed Abdulkadir. 2023. “Imaging Phenotypes Predict Overall Survival in Glioma More Accurate than Basic Demographic and Cell Mutation Profiles.” Computer Methods and Programs in Biomedicine 242 (107812). https://doi.org/10.1016/j.cmpb.2023.107812.
Rathore, Saima, et al. “Imaging Phenotypes Predict Overall Survival in Glioma More Accurate than Basic Demographic and Cell Mutation Profiles.” Computer Methods and Programs in Biomedicine, vol. 242, no. 107812, Sept. 2023, https://doi.org/10.1016/j.cmpb.2023.107812.


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