{
  "abstract": "Objectives: To improve the diagnostic accuracy of benign versus malignant parotid tumors using non-contrast CT. We propose a clinician-in-the-loop hybrid framework that mitigates low soft-tissue contrast by fusing semi-automated 3D visual features with expert semantic descriptors.\n\nMethods: This multi-center retrospective study analyzed non-contrast CTs from 367 patients. We developed AttenNet, a semi-automated model. First, an nnU-Net module automates 3D tumor segmentation, eliminating manual bounding boxes. A 3D-ResNet backbone then extracts volumetric features, which are dynamically fused with radiologist-provided semantic labels via a Multi-Head Cross-Attention Fusion (MHAF) mechanism. This allows the network to query visual morphology based on expert priors. Diagnostic performance and Gradient-weighted Class Activation Mapping (Grad-CAM) interpretability were evaluated.\n\nResults: The segmentation achieved a median Dice of 0.827. AttenNet (3D-ResNet) yielded an AUC of 0.908, significantly outperforming the image-only 3D-ResNet (AUC: 0.741) and expert-feature models (AUC: 0.821). External validation ( n = 20) reached an AUC of 0.560–0.910.However, given the extremely small sample size, these results must be interpreted strictly as an exploratory proof-of-concept rather than definitive confirmation of generalizability. Grad-CAM confirmed that AttenNet highlights clinically essential geometric indicators-specifically tumor margins and internal heterogeneity-for differentiating malignancy, whereas single-modality baselines focused on background artifacts.\n\nConclusion: This clinician-in-the-loop paradigm successfully integrates spatial imaging with expert reasoning. While multimodal fusion drove the primary performance gains, the MHAF mechanism provided enhanced spatial interpretability.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China",
    "Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China",
    "Department of Oncology, Meizhou People’s Hospital",
    "Department of Oncology, Bazhong City Hospital of Traditional Chinese Medicine"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Qin Yang"
    },
    {
      "affs": [
        1
      ],
      "name": "Wenjun Liao"
    },
    {
      "affs": [
        2
      ],
      "name": "Jianda Sun"
    },
    {
      "affs": [
        3
      ],
      "name": "Dacheng Mu"
    },
    {
      "affs": [
        0
      ],
      "name": "Minyang Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiaohua Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yue Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Shichuan Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9sbfh3z15zfnd.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1926379",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716596/?tool=EBI",
  "id": "9sbfh3z15zfnd",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1926379/full",
  "language": "eng",
  "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 Research"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
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  "openalex": "W7222347215",
  "pdf_url": "https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1926379/pdf",
  "pmcid": "PMC13716596",
  "pmid": "42959120",
  "published": "2026-10-15",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9sbfh3z15zfnd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1926379",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1926379/full"
    },
    {
      "first_seen": "2026-10-31T00:20:37Z",
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      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13716596"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
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  ],
  "title": "Radiologist-guided multimodal fusion network for differentiating parotid tumors on 3D non-contrast CT",
  "title_text": "Radiologist-guided multimodal fusion network for differentiating parotid tumors on 3D non-contrast CT",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:21:05Z",
  "url": "https://research.draconitic.com/paper/9sbfh3z15zfnd",
  "venue": "Frontiers in Oncology",
  "versions": []
}