{
  "abstract": "Background: Extensive-stage small cell lung cancer (ES-SCLC) is a rare yet highly aggressive malignancy with severely limited treatment options. The efficacy of immunotherapy for ES-SCLC remain limited, with only 14% of patients deriving additional benefit from immune checkpoint inhibitors (ICIs), underscoring significant heterogeneity in patient response. This study aimed to develop a model for the accurate prediction of prognosis and therapeutic response to optimize treatment strategies.\n\nMethods: 99 and 25 ES-SCLC patients were enrolled as training and independent internal validation cohorts. Baseline CT images, clinical data, and laboratory markers were collected. Prognostic factors were screened via Cox regression. A mediastinal window CT-based deep learning model (MWDL) was developed and combined with Progastrin-releasing peptide (log(ProGRP + 1)) and bone metastasis to establish the novel MWGB-Cox model.\n\nResults: ProGRP and bone metastasis were independent prognostic factors with area under the curves (AUCs) of 0.705 and 0.666 respectively for predicting 1-year OS. Among the pure deep learning models, the MWDL model achieved better performance with a mean AUC of 0.813 than the deep learning model based on lung window CT images (LWDL, AUC 0.756). Whereas the multimodal MWGB-Cox model not only achieved the highest AUC (0.902) for 1-year OS prediction, but also effectively overcame the limitation of insufficient sensitivity. The predictive performance of the MWGB-Cox model was further validated in the independent internal validation cohort.\n\nConclusions: This novel, simple, and cost-effective multimodal MWGB-Cox model integrating CT-based deep learning, log(ProGRP + 1), and bone metastasis could robustly predict 1-year OS of ES-SCLC patients receiving first-line immunotherapy, providing a reliable tool for clinical decision-making.\n\nSignificance: By integrating CT-based deep learning, log(ProGRP + 1) levels, and bone metastasis, the novel multimodal model achieved the highest predictive AUC (0.902) with improved sensitivity, serving as a simple, cost-effective tool for clinical decision-making.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University , ,",
    "Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine , ,",
    "Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center , ,",
    "Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University , ,",
    "Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jing Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Liwen Gu"
    },
    {
      "affs": [
        2
      ],
      "name": "Peixin Dong"
    },
    {
      "affs": [
        0
      ],
      "name": "Yu Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "Baomo Liu"
    },
    {
      "affs": [
        3
      ],
      "name": "Zhihua Wen"
    },
    {
      "affs": [
        0
      ],
      "name": "Yanbin Zhou"
    },
    {
      "affs": [
        4
      ],
      "name": "Haohua Yao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f38kr0xppbend.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1901931",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661403/?tool=EBI",
  "id": "f38kr0xppbend",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13661403",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2730|Lung Cancer Research Studies"
    ]
  ],
  "openalex": "W7214900212",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661403/pdf/?tool=EBI",
  "pmcid": "PMC13661403",
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/f38kr0xppbend.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:20Z",
      "native_id": "PMC:PMC13661403",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13661403"
    },
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1901931",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1901931/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Lung Cancer Research Studies",
      "subfield": "oncology"
    }
  ],
  "title": "A novel multimodal model integrating CT-based deep learning and clinical information for predicting 1-year OS of first-line immunotherapy in ES-SCLC patients",
  "title_text": "A novel multimodal model integrating CT-based deep learning and clinical information for predicting 1-year OS of first-line immunotherapy in ES-SCLC patients",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:13Z",
  "url": "https://research.draconitic.com/paper/f38kr0xppbend",
  "venue": "Frontiers in oncology",
  "versions": []
}