{
  "abstract": "Objective\n\nTo investigate the potential value of radiomic features based on pretreatment laryngoscopic images in predicting the efficacy of neoadjuvant immunochemotherapy for locally advanced hypopharyngeal squamous cell carcinoma.Materials and methods\n\nPatients with locally advanced hypopharyngeal squamous cell carcinoma who received neoadjuvant immunochemotherapy at our hospital were retrospectively enrolled, and their pretreatment white-light and narrow-band imaging (NBI) laryngoscopic images were collected. Patients were divided into a complete response (CR) group and a non-complete response (non-CR) group according to treatment efficacy. After uniformly delineating regions of interest on the original lesion areas of all images, the images were converted into HSV, RGB, and CIELAB color spaces, yielding a total of nine channels. A total of 104 texture features were then extracted on each channel. Intergroup difference analysis was performed using an independent-samples t-test, and key features were further screened by LASSO regression.Results\n\nA total of 499 laryngoscopic images were analyzed in this study. Statistical analysis identified 67 differential features and ultimately 8 key features in white-light images; 52 differential features and ultimately 11 key features were identified in NBI images.Conclusion\n\nThis exploratory study preliminarily demonstrates that radiomic features based on pretreatment laryngoscopic images have potential in differentiating the efficacy of neoadjuvant immunochemotherapy. The screened key features provide useful clues for the subsequent construction of prediction models, although their clinical applicability still requires further validation in large-sample prospective studies.Level of evidence\n\n3.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Otorhinolaryngology Head and Neck Surgery Beijing Tongren Hospital, Capital Medical University Beijing China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wen Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhuoheng Liu",
      "orcid": "0009-0003-9634-5926"
    },
    {
      "affs": [
        0
      ],
      "name": "Lifei Feng"
    },
    {
      "affs": [
        0
      ],
      "name": "Xinming Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Zishi Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Duoxuan Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhigang Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Zufei Li",
      "orcid": "0000-0003-0161-3311"
    },
    {
      "affs": [
        0
      ],
      "name": "Yang Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/90s0915ag7zw7.bib",
  "cited_by": 0,
  "doi": "10.1002/lio2.70596",
  "fulltext_url": "https://europepmc.org/articles/PMC13711484",
  "id": "90s0915ag7zw7",
  "issns": [
    "2378-8038"
  ],
  "landing_url": "https://europepmc.org/article/MED/42948597",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7224015370",
  "pdf_url": "https://europepmc.org/articles/PMC13711484?pdf=render",
  "pmcid": "PMC13711484",
  "pmid": "42948597",
  "published": "2026-10-27",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/90s0915ag7zw7.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:33Z",
      "native_id": "MED:42948597",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42948597"
    },
    {
      "first_seen": "2026-10-31T12:22:15Z",
      "native_id": "PMC13711484",
      "source": "pmc",
      "url": "https://doi.org/10.1002/lio2.70596"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Analysis of Laryngoscopic Image Differences in Different Responses to Neoadjuvant Immunotherapy Combined With Chemotherapy in Locally Advanced Hypopharyngeal Carcinoma.",
  "title_text": "Analysis of Laryngoscopic Image Differences in Different Responses to Neoadjuvant Immunotherapy Combined With Chemotherapy in Locally Advanced Hypopharyngeal Carcinoma.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:22:15Z",
  "url": "https://research.draconitic.com/paper/90s0915ag7zw7",
  "venue": "Laryngoscope investigative otolaryngology",
  "versions": []
}