{
  "abstract": "BackgroundAccurate differentiation between benign and malignant spinal tumors is critical for treatment planning, yet manual interpretation of T2-weighted fat-suppressed MRI remains subjective, with substantial inter-observer variability. This study aimed to develop a deep learning model, the multi-scale deep residual network (MSDR-Net), for automated benign–malignant classification.MethodsA total of 500 T2-weighted fat-suppressed MR images of pathologically confirmed spinal tumors (389 benign, 111 malignant) were retrospectively collected from two institutions. Data were partitioned into training (70%), validation (20%), and independent test (10%) sets, and five-fold cross-validation was performed. Leave-one-center-out external validation was also performed (development: Institution A, n = 300; evaluation: Institution B, n = 200). The proposed MSDR-Net incorporates multi-branch residual blocks that combine parallel multi-scale convolutional paths with efficient channel attention modules and was benchmarked against six established architectures.ResultsOn the independent test set (N = 50; 12 malignant cases), MSDR-Net achieved 92.0% accuracy, 91.7% sensitivity (95% CI 61.5%–99.8%), 92.1% specificity, and an AUC of 0.921, the highest point estimates among evaluated architectures. The model was well calibrated (Brier score 0.072), with net benefit over treat-all strategies across thresholds of 0.10–0.60. Derived from a small test cohort, these estimates carry substantial uncertainty and await confirmation in larger, prospective cohorts. External-cohort accuracy was 86.5% (AUC 0.875). Ablation experiments showed that removing either component degraded performance, with a modest super-additive interaction; given their substantially smaller capacity, the single-path variants’ deficit cannot be attributed solely to removing multi-scale processing. The model has 55.4M parameters and 12.3-ms inference per image.ConclusionMSDR-Net effectively captures multi-scale discriminative features of spinal tumors on T2-weighted fat-suppressed MRI, achieving high classification accuracy while maintaining computational efficiency. The model shows promise for clinical deployment as an auxiliary diagnostic tool for orthopedic surgeons and radiologists.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Zhiwei Ma"
    },
    {
      "name": "Xingyu Duan"
    },
    {
      "name": "Xiaorong Ma"
    },
    {
      "name": "Cuiping Yan"
    },
    {
      "name": "Xuewei Wang"
    },
    {
      "name": "Ningkui Niu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0x7yh900dv3k8.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1951483",
  "fulltext_url": null,
  "id": "0x7yh900dv3k8",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1951483",
  "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": "2026-10-30",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/0x7yh900dv3k8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.3389/fonc.2026.1951483",
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      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1951483"
    },
    {
      "first_seen": "2026-10-30T12:20:14Z",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "feed-journal:medicine/oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Multi-scale convolution with efficient channel attention improves benign–malignant classification of spinal tumors on T2-weighted fat-suppressed MRI",
  "title_text": "Multi-scale convolution with efficient channel attention improves benign–malignant classification of spinal tumors on T2-weighted fat-suppressed MRI",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:21:05Z",
  "url": "https://research.draconitic.com/paper/0x7yh900dv3k8",
  "venue": "Frontiers in Oncology",
  "versions": []
}