{
  "abstract": "Background\n\nAccurately distinguishing benign from malignant small renal masses (SRMs) is crucial to avoid unnecessary surgeries, yet reliable preoperative diagnostic tools remain limited.Methods\n\nA total of 189 patients with SRMs from two hospitals (2018-2022) were enrolled. Clinical data and MRI features were analyzed by two radiologists. Radiomics features were automatically extracted from manually segmented ROIs using dedicated software. Significant features were selected via univariate analysis, multivariate regression, and Lasso to develop three models: radiomics, clinical, and combined nomogram. Diagnostic performance was evaluated using the Area Under the receiver operating characteristic Curve (AUC).Results\n\nThe radiomics-clinical nomogram was constructed using T2WI signal intensity (p < 0.001), pseudocapsule (p = 0.001), and radiomics score (Rad-score, p = 0.001) via multivariate logistic regression. In the training set, the nomogram achieved an AUC of 0.962 (95% CI: 0.931-0.993), outperforming both the radiomics model (AUC = 0.918, 95% CI: 0.862-0.974) and the clinical model (AUC = 0.856, 95% CI: 0.780-0.933). Favorable performance was observed in the internal validation set (nomogram: AUC = 0.981, 95% CI: 0.944-1.000; radiomics model: AUC = 0.912, 95% CI: 0.750-1.000; clinical model: AUC = 0.844, 95% CI: 0.578-1.000) and external validation set (nomogram: AUC = 0.971, 95% CI: 0.886-1.000; radiomics model: AUC = 0.928, 95% CI: 0.841-0.989; clinical model: AUC = 0.916,95% CI: 0.760-0.976).Conclusion\n\nAll three models enable effective and non-invasive preoperative differentiation of benign from malignant SRMs, with the radiomics-clinical nomogram exhibiting the highest diagnostic accuracy, supporting its potential as a valuable clinical tool.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Urology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.",
    "Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China.",
    "Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.",
    "Department of Urology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. Electronic address: kongjq5@mail.sysu.edu.cn.",
    "Department of Urology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. Electronic address: dongwen@mail.sysu.edu.cn."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chao Fang"
    },
    {
      "affs": [
        1
      ],
      "name": "Li Tian"
    },
    {
      "affs": [
        2
      ],
      "name": "Ming Gao"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaohui Duan"
    },
    {
      "affs": [
        0
      ],
      "name": "Qi Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Zixin Xu"
    },
    {
      "affs": [
        0
      ],
      "name": "Junjiong Zheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Yushu Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Ming Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Xu Chen"
    },
    {
      "affs": [
        2
      ],
      "name": "Wei Jiang"
    },
    {
      "affs": [
        3
      ],
      "name": "Jianqiu Kong"
    },
    {
      "affs": [
        4
      ],
      "name": "Wen Dong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d4ds690aynqhe.bib",
  "cited_by": 0,
  "doi": "10.1016/j.clinsp.2026.101212",
  "fulltext_url": "https://europepmc.org/articles/PMC13691506",
  "id": "d4ds690aynqhe",
  "issns": [
    "1807-5932",
    "1980-5322"
  ],
  "landing_url": "https://europepmc.org/article/MED/42887293",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7222138302",
  "pdf_url": "https://europepmc.org/articles/PMC13691506?pdf=render",
  "pmcid": "PMC13691506",
  "pmid": "42887293",
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d4ds690aynqhe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:46Z",
      "native_id": "MED:42887293",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42887293"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "A multimodal MRI radiomics sonata for differentiating small renal masses: a two-center retrospective study.",
  "title_text": "A multimodal MRI radiomics sonata for differentiating small renal masses: a two-center retrospective study.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:46Z",
  "url": "https://research.draconitic.com/paper/d4ds690aynqhe",
  "venue": "Clinics (Sao Paulo, Brazil)",
  "versions": []
}