{
  "abstract": "Purpose: This study investigates possible differences of female and male patients’ data in regard to the prediction of mortality and troponin level of acute pulmonary embolism (APE) patients. Radiomics features are utilized to gain insights of sex-specific image properties, which should support APE diagnosis and treatment.\n\nMethods: A database of 200 manually segmented APE patients’ computed tomographic data (100 female and 100 male) was used for the prediction of 30-day mortality and biomarker troponin level. The radiomics features were extracted from the segmented thrombi and reduced via correlation analysis and 12 feature selection followed each by 12 classification methods. For comparison, the weighted F1-Score, Matthew’s Correlation Coefficient, and balanced accuracy were used. For assessment of generalizability, the study was extended to 427 automatically selected datasets.\n\nResults: Sex-dependent differences have been found in the resulting metrics and highest achieving feature selection and classification methods. Not stratifying the dataset by sex resulted in lower performance compared to the sex-stratified male and female only calculations. The subgroup of female patients achieved up to 31.04 % higher metric values compared to the other cohorts. Different thrombus properties contributed to the prediction based on the patients’ sex as well.\n\nConclusion: In this study, we analyzed possible correlations between sex of APE patients and mortality and troponin levels as well as radiomics features. Our analysis identified an influence of sex on the prediction task and image feature selection. Female patient data especially had higher predictive potential, which could help for further sex-stratified analysis of APE.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany",
    "Department of Medical Informatics and Statistics, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany",
    "Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Minden, Germany"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lea Ehrhardt"
    },
    {
      "affs": [
        1
      ],
      "name": "Georg Hille"
    },
    {
      "affs": [
        1
      ],
      "name": "Ehsan Amini"
    },
    {
      "affs": [
        2
      ],
      "name": "Alexey Surov"
    },
    {
      "affs": [
        2
      ],
      "name": "Jan Borggrefe"
    },
    {
      "affs": [
        0
      ],
      "name": "Patrique Fiedler"
    },
    {
      "affs": [
        1
      ],
      "name": "Sylvia Saalfeld"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bwkp4b54g19e0.bib",
  "cited_by": 0,
  "doi": "10.1007/s11548-026-03704-1",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13723516/",
  "id": "bwkp4b54g19e0",
  "issns": [
    "1861-6410",
    "1861-6429"
  ],
  "landing_url": "https://doi.org/10.1007/s11548-026-03704-1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Article"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7163511826",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s11548-026-03704-1.pdf",
  "pmcid": "PMC13723516",
  "pmid": "42240724",
  "published": "2026-06-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bwkp4b54g19e0.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:22:08Z",
      "native_id": "PMC13723516",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s11548-026-03704-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Influence of patients’ sex on radiomics-based predictions of lung artery thrombus",
  "title_text": "Influence of patients’ sex on radiomics-based predictions of lung artery thrombus",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:20:19Z",
  "url": "https://research.draconitic.com/paper/bwkp4b54g19e0",
  "venue": "International Journal of Computer Assisted Radiology and Surgery",
  "versions": []
}