{
  "abstract": "Objective: We aimed to 1) evaluate whether AI algorithms could identify a very low-risk sub-population Lung-RADS 1/2 cases suitable for a safe switch to biennial screening, and 2) externally validate Sybil, an imaging-based deep learning lung cancer risk prediction tool. Materials and methods: We gathered all lung cancer screenings (LCSs) from a tertiary safety-net hospital (n=3120, up to September 2024), manually extracted relevant clinical data, and analyzed Sybil's performance across various subgroups, with a specific focus on Lung-RADS 1 and 2 cases. We simulated LCS interval extension using Sybil and the Brock model risk scores calibrated on a subset of the National Lung Screening Trial (NLST). Confidence intervals were obtained through bootstrapping. Results: The median follow-up was 2.3 years (interquartile range 1.5-3.1), with a total 6-year cancer incidence rate of 2.3% (72/3120). Lung-RADS 1 and 2 cases comprised 90.5% of our scans. Sybil's overall area under the curve (AUC) ranged from 0.819 to 0.912; while its 2-year AUC for Lung-RADS 1 and 2 cases was 0.746. In the screening interval extension simulation, calibrating for a 30% shift to biennial screening translated into 33.8% of negative screens in our data being safely extended using Sybil, with no delayed cancers. The Brock model demonstrated an AUC of 0.513 on our data, with a sensitivity of 18.2% using the 1.5% threshold. Conclusion: Sybil has the potential to safely extend LCS intervals for low-risk individuals, reducing radiology workload and opening spots for life-saving new screenings.",
  "abstract_shown": true,
  "affiliations": [
    "University of California, San Francisco"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "A. Nowroozi"
    },
    {
      "name": "M. Bondarenko"
    },
    {
      "name": "S. S. Brar"
    },
    {
      "name": "B. Haas"
    },
    {
      "name": "J. Brown"
    },
    {
      "name": "A. Rustagi"
    },
    {
      "name": "B. Graham"
    },
    {
      "name": "M. Hudson"
    },
    {
      "name": "G. Su"
    },
    {
      "name": "A. Yala"
    },
    {
      "affs": [
        0
      ],
      "name": "J. H. Sohn"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f4vr9201xmgd5.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.03.26367146",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.11.03.26367146v1.full",
  "id": "f4vr9201xmgd5",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.11.03.26367146v1",
  "language": null,
  "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": [
    [
      "medrxiv",
      "radiology and imaging"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.11.03.26367146v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f4vr9201xmgd5.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:38Z",
      "native_id": "10.64898/2026.11.03.26367146",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.11.03.26367146v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Investigating AI's potential in risk-stratifying lung cancer screenings for extending screening intervals and reducing workload: A retrospective analysis of a tertiary hospital cohort",
  "title_text": "Investigating AI's potential in risk-stratifying lung cancer screenings for extending screening intervals and reducing workload: A retrospective analysis of a tertiary hospital cohort",
  "type": "preprint",
  "updated_at": "2026-11-06T00:20:58Z",
  "url": "https://research.draconitic.com/paper/f4vr9201xmgd5",
  "venue": "medRxiv",
  "versions": []
}