{
  "abstract": "Automated lumbar facet joint degeneration grading requires both accurate anatomical segmentation and clinically meaningful region-level classification. We evaluated whether voxel-level segmentation performance was aligned with downstream degeneration grading. This retrospective study included 145 lumbar computed tomography (CT) examinations and 725 level-specific bilateral facet joint regions of interest (ROIs), including 165 severe and 560 non-severe regions. Weishaupt grades 0-1 were categorized as non-severe and grades 2-3 as severe. Scheme A used binary nnU-Net segmentation followed by 11 quantitative region-level features, training-fold-specific standardization, and logistic regression; an oracle comparison using ground-truth masks was compared with analysis using predicted masks. Scheme B used direct three-class segmentation. Patient-level five-fold cross-validation was performed, with 116 training and 29 validation patients per fold. The results were reported as mean ± standard deviation (SD). Scheme A achieved a binary segmentation Dice coefficient of 0.770±0.008. In the oracle comparison, the region-level area under the receiver operating characteristic (ROC) curve (AUC), recall, F1-score, accuracy, and case-level accuracy were 0.671±0.100, 0.538±0.172, 0.399±0.102, 0.640±0.051, and 0.579±0.096, respectively. Using predicted masks, the corresponding values were 0.650±0.068, 0.603±0.250, 0.375±0.072, 0.563±0.123, and 0.545±0.071. Scheme B achieved a native nnU-Net mean validation Dice of 0.493±0.044 and region-level AUC, recall, F1-score, accuracy, and case-level accuracy of 0.730±0.024, 0.445±0.051, 0.510±0.045, 0.808±0.015, and 0.738±0.058, respectively. Voxel-level segmentation and clinically oriented grading performance were not directly aligned. Scheme A provided stable anatomical segmentation but more variable downstream grading, whereas scheme B achieved better overall region- and case-level classification performance but lower severe-degeneration recall. Both spatial and clinical decision metrics should therefore be considered when evaluating automated facet joint grading systems.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Orthopedics, Affiliated Nantong Clinical College of Nantong University, Nantong First People's Hospital, School of Medicine, Nantong University, Nantong, China.",
    "Department of Orthopedics, Nantong First People's Hospital, Southeast University, Nantong, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Baisen Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Yukang Cheng"
    },
    {
      "affs": [
        1
      ],
      "name": "Chunyan Ji"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhiming Cui"
    },
    {
      "affs": [
        1
      ],
      "name": "Guanhua Xu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuyu Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dcz4pxakw7te7.bib",
  "cited_by": 0,
  "doi": "10.21037/qims-2026-1485",
  "fulltext_url": "https://europepmc.org/articles/PMC13743281",
  "id": "dcz4pxakw7te7",
  "issns": [
    "2223-4292",
    "2223-4306"
  ],
  "landing_url": "https://europepmc.org/article/MED/42997901",
  "language": "eng",
  "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": [
    [
      "openalex",
      "2204|Medical Imaging and Analysis"
    ]
  ],
  "openalex": "W7223252573",
  "pdf_url": "https://europepmc.org/articles/PMC13743281?pdf=render",
  "pmcid": "PMC13743281",
  "pmid": "42997901",
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dcz4pxakw7te7.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:41Z",
      "native_id": "MED:42997901",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42997901"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:2741|Advanced MRI Techniques and Applications",
      "subfield": "imaging"
    }
  ],
  "title": "Automated quantitative computed tomography grading of lumbar facet joint degeneration: influence of segmentation errors on instance-level clinical assessment.",
  "title_text": "Automated quantitative computed tomography grading of lumbar facet joint degeneration: influence of segmentation errors on instance-level clinical assessment.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:41Z",
  "url": "https://research.draconitic.com/paper/dcz4pxakw7te7",
  "venue": "Quantitative imaging in medicine and surgery",
  "versions": []
}