{
  "abstract": "Background: The growing volume of mammography screenings has created severe radiologist shortages, while standard First-In, First-Out (FIFO) reading queues fail to prioritize urgent or complex cases, delaying critical diagnoses. Objective: This study introduces the Density and BI-RADS--Aware Triage and Report Generation (DB-ATRG) framework to fundamentally restructure mammography workflows by automating diagnostic text generation and enabling risk-based case prioritization. Methods: Utilizing the Digital Mammography Dataset for Breast Cancer Diagnosis Research (DMID), we fine-tuned the 4-billion parameter MedGemma 1.5 vision-language model using Quantization and Low-Rank Adaptation (QLoRA). The extracted biomarkers drive a dual-phase triage algorithm that flags extremely dense breasts (ACR Category D) for supplemental screening and dynamically ranks remaining cases using a calculated Cumulative Urgency Score. The clinical impact of this triage workflow was evaluated against a standard FIFO queue using a simulated cohort of 100 mammography cases. Results: DB-ATRG achieved significant improvements over the AMRG baseline in clinical text generation and classification, securing a ROUGE-L score of 0.8012, a METEOR score of 0.8358, and an ACR Density Accuracy of 0.6810. In clinical simulations, the optimized prioritization queue captured 100.0% of high-risk malignancies (BI-RADS 4 and 5) within the first 20% of the reading workload, compared to just 19.2% in the random FIFO queue. This framework effectively accelerated the mean rank position of severe cases from 42.0 down to 7.5. Conclusion: By accurately automating report generation and aggressively prioritizing severe cases, the DB-ATRG framework can drastically optimize clinical resource allocation and accelerate the time-to-diagnosis for the most vulnerable patients.",
  "abstract_shown": true,
  "affiliations": [
    "Georgia State University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "V. Phan"
    },
    {
      "name": "N. N. C. Tran"
    },
    {
      "name": "N. T. D. Bui"
    },
    {
      "affs": [
        0
      ],
      "name": "R. Jeter"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/924aqef0j5xas.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.22.26358655",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.07.22.26358655v2.full",
  "id": "924aqef0j5xas",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.07.22.26358655v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7170138787",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.07.22.26358655v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/924aqef0j5xas.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:38Z",
      "native_id": "10.64898/2026.07.22.26358655",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.07.22.26358655v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "DB-ATRG: The Density and BI-RADS-Aware Triage and Automatic Report Generation System for Mammography",
  "title_text": "DB-ATRG: The Density and BI-RADS-Aware Triage and Automatic Report Generation System for Mammography",
  "type": "preprint",
  "updated_at": "2026-11-06T00:20:52Z",
  "url": "https://research.draconitic.com/paper/924aqef0j5xas",
  "venue": "medRxiv",
  "versions": []
}