{
  "abstract": "IntroductionAccurate prognostic stratification remains challenging in locally advanced nasopharyngeal carcinoma (LA-NPC), as conventional clinicopathological factors provide limited precision for individualized risk assessment. Although hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) contain abundant morphological information, their prognostic potential in LA-NPC remains underexplored using computational pathology approaches.Material and methodsPatients with stage III–IVA LA-NPC from three Chinese tertiary centers were allocated into a training cohort, an internal validation cohort, and an independent external test cohort. A CLAM-inspired attention-based multiple-instance learning (MIL) model was developed using pretreatment H&E-stained WSIs and externally validated. The model was compared with four alternative MIL architectures incorporating different feature extractors and pooling strategies. Prognostic performance for 3-year progression-free survival (PFS) was evaluated using time-dependent receiver operating characteristic analysis, the concordance index (C-index), Kaplan–Meier analysis, decision curve analysis, and Cox regression.ResultsA total of 253 patients contributing 265 WSIs were included: 149 patients/160 WSIs in the training cohort, 52 patients/53 WSIs in the internal validation cohort, and 52 patients/52 WSIs in the independent external test cohort. In the internal validation cohort, the CLAM-inspired Attention MIL model yielded an AUC of 0.883 (95% CI, 0.762–0.969) and a C-index of 0.802 (95% CI, 0.705–0.884) for predicting 3-year PFS. In the external test cohort, the model achieved an AUC of 0.721 (95% CI, 0.573–0.849) and a C-index of 0.710 (95% CI, 0.591–0.819). The model significantly stratified patients into high- and low-risk groups in both cohorts and showed numerically higher AUC and C-index estimates than the four comparative MIL architectures. The risk score was linked to an elevated risk of progression (HR, 1.30; 95% CI, 1.06-1.60; P = 0.0126).ConclusionsA weakly supervised CLAM-inspired Attention MIL framework can extract prognostically relevant information from routine H&E-stained WSIs and provide externally validated risk stratification for patients with LA-NPC. This approach may complement conventional clinicopathological assessment and facilitate individualized prognostic evaluation in precision oncology.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Chao Jiang"
    },
    {
      "name": "Wenxuan Lu"
    },
    {
      "name": "Mingjun Ding"
    },
    {
      "name": "Xuanchen Zhou"
    },
    {
      "name": "Butuo Li"
    },
    {
      "name": "Xiong Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/07ay0y5v54wzc.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1966935",
  "fulltext_url": null,
  "id": "07ay0y5v54wzc",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1966935",
  "language": null,
  "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": [
    [
      "feed-journal",
      "medicine/oncology"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-09",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/07ay0y5v54wzc.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:34Z",
      "native_id": "10.3389/fonc.2026.1966935",
      "source": "feed-frontiers",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1966935"
    },
    {
      "first_seen": "2026-11-09T12:23:35Z",
      "native_id": "10.3389/fonc.2026.1966935",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1966935/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "feed-journal:medicine/oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Weakly supervised computational pathology for prognostic stratification in locally advanced nasopharyngeal carcinoma: a multicenter external validation study",
  "title_text": "Weakly supervised computational pathology for prognostic stratification in locally advanced nasopharyngeal carcinoma: a multicenter external validation study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T18:20:20Z",
  "url": "https://research.draconitic.com/paper/07ay0y5v54wzc",
  "venue": "Frontiers in Oncology",
  "versions": []
}