{
  "abstract": "ObjectiveMicrovascular invasion (MVI) predicts postoperative recurrence in hepatocellular carcinoma (HCC), yet preoperative prediction remains challenging. This study compared a Transformer, a convolutional neural network (CNN), and a multiple instance learning (MIL) framework for predicting MVI from contrast-enhanced CT.MethodsWe retrospectively enrolled 236 patients with pathologically confirmed HCC. Tumor regions of interest were semi-automatically delineated on arterial-phase CT images, and slices centered on the largest tumor cross-section were sampled to build a 2.5D dataset. Four models—Transformer, MIL, CNN, and a clinical baseline—were trained. Models were compared using the area under the receiver operating characteristic curve (AUC) with the DeLong test; bootstrap resampling assessed the stability of AUC estimates.ResultsThe Transformer achieved the highest AUC of 0.827 (95% CI 0.7585–0.8962), with an accuracy of 0.775, sensitivity of 0.577, and specificity of 0.889, followed by MIL (0.766; 95% CI 0.6875–0.8441), CNN (0.723), and the clinical model (0.715). Bootstrap validation confirmed stable AUC estimates for all models, with the Transformer ranking first (mean 0.827; SD 0.034). The Transformer significantly outperformed the CNN (adjusted P = 0.001) and the clinical model (adjusted P = 0.039); after correction for multiple comparisons, its difference from MIL was of borderline significance (adjusted P = 0.097). No significant differences were observed among the CNN, MIL, and clinical models.ConclusionAmong the four models evaluated, the contrast-enhanced CT-based Transformer predicted MVI most accurately, and its advantage persisted after bootstrap internal validation. These findings concern the ranking of discriminative performance only; the clinical utility of the model is not established, and prospective validation in larger cohorts is required before any clinical application.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Mianlei Lin"
    },
    {
      "name": "Xiaojun Zheng"
    },
    {
      "name": "Caitao Zhao"
    },
    {
      "name": "Tao Lin"
    },
    {
      "name": "Qishu Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1145nawysvbjr.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1988882",
  "fulltext_url": null,
  "id": "1145nawysvbjr",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1988882",
  "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": "2026-11-02",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/1145nawysvbjr.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T06:20:24Z",
      "native_id": "10.3389/fonc.2026.1988882",
      "source": "feed-frontiers",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1988882"
    },
    {
      "first_seen": "2026-11-02T12:24:06Z",
      "native_id": "10.3389/fonc.2026.1988882",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1988882/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "feed-journal:medicine/oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Predicting microvascular invasion in hepatocellular carcinoma using contrast-enhanced CT-based deep learning models: a comparative study of transformer, CNN, and MIL",
  "title_text": "Predicting microvascular invasion in hepatocellular carcinoma using contrast-enhanced CT-based deep learning models: a comparative study of transformer, CNN, and MIL",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T00:26:54Z",
  "url": "https://research.draconitic.com/paper/1145nawysvbjr",
  "venue": "Frontiers in Oncology",
  "versions": []
}