{
  "abstract": "BackgroundPatients with ER-positive/HER2-negative early breast cancer of low or intermediate grade are generally considered to have a favourable prognosis, yet a number will recur. This risk is poorly captured by standard clinicopathological factors. AI-based medical devices for whole slide image (WSI) analyses used for risk-stratification often predict on slide-level thereby overlooking spatial localisation of the biomarkers. We hypothesise that the spatial distribution of a previously reported AI-based marker provides independent prognostic value beyond its global score.\n\nPatients and MethodsThis Swedish multicentre cohort study includes 6106 patients with primary invasive breast cancer: 3078 from the Stockholm region (training) and 3028 from Sk[a]ne (independent test). Tile-level high-grade morphology scores were generated with ViT_DG, a vision-transformer adaptation of DeepGrade, and the presence of clusters of high-risk tiles localised at the invasive tumour front was defined as a binary biomarker. Primary analyses were pre-specified in the ER-positive/HER2-negative/lymph-node-negative and Grade 1-2 subgroup. Association with breast-cancer recurrence-free interval (BCRFI) and distant metastasis were assessed using multivariable Cox proportional hazards models (HR).\n\nResultsIn the primary subgroup (n=1536 training; n=1403 test), tumour front clusters were present in 37% and 39% of patients. Cluster-positive status was associated with a 2.30-fold increased risk in the external test. Cluster-negative patients achieved a 10-year BCRFI of 95.3%, equivalent to Grade 1 patients and better than Grade 2 (92.4%), whereas cluster-positive patients (90.2%) did worse than the Grade 3 reference. The association was retained in patients classified as low-risk by ViT_DG (test set multivariable HR 2.86), identifying high-risk patients not captured by the global score.\n\nConclusionsThe absence of high-risk morphology clusters at the tumour front is an independent prognostic indicator, identifying a low-risk group independently of the global AI biomarker classification. More broadly, preserving the spatial distribution of AI-derived predictions enables prognostic stratification beyond slide-level aggregation.\n\nClinicalTrials.gov identifier: NCT02306096",
  "abstract_shown": true,
  "affiliations": [
    "Karolinska Instittutet"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "C. Boissin"
    },
    {
      "name": "Y. Xiang"
    },
    {
      "name": "B. Liu"
    },
    {
      "name": "J. Vallon-Christersson"
    },
    {
      "name": "E. Walker"
    },
    {
      "name": "J. Hartman"
    },
    {
      "name": "M. Rantalainen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0apjwsztcv74w.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.07.26364961",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.07.26364961v1.full",
  "id": "0apjwsztcv74w",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.07.26364961v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "oncology"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7221070404",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.07.26364961v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0apjwsztcv74w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:32Z",
      "native_id": "10.64898/2026.10.07.26364961",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.07.26364961v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "A spatial AI-based biomarker with independent prognostic value in early low- and intermediate- risk breast cancer",
  "title_text": "A spatial AI-based biomarker with independent prognostic value in early low- and intermediate- risk breast cancer",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:32Z",
  "url": "https://research.draconitic.com/paper/0apjwsztcv74w",
  "venue": "medRxiv",
  "versions": []
}