{
  "abstract": "Thyroid nodule malignancy assessment relies on ultrasound imaging, yet current risk stratification systems suffer from substantial interobserver variability, contributing to a high rate of unnecessary biopsies. We propose a radiomics-based framework combining quantitative feature extraction via PyRadiomics with a Generalized Additive Model (GAM) classifier, selected among six candidates for its favorable trade-off between discriminative performance and interpretability. To complement point predictions with uncertainty estimates, Cross-Conformal Prediction with label-conditional guarantees is applied, flagging ambiguous cases for clinical review rather than forcing a binary decision. Experiments are conducted on TN5000, a publicly available ultrasound dataset of thyroid nodules. After Spearman correlation filtering, 36 features are retained and the GAM achieves an AUC of $0.89$, a sensitivity of $0.83$, and a specificity of $0.82$ on the validation set. At a significance level $ε=0.09$, selected to ensure empirical coverage guarantees on both classes, 63\\% of nodules on the test set receive a certain (i.e., single-label) prediction, with conditional precision of $0.94$ for certain malignant and conditional Negative Predictive Value (NPV) of $0.73$ for certain benign cases, while the remaining 37\\% are referred to the clinician.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13387",
  "author_total": 3,
  "authors": [
    {
      "name": "Laura Testa"
    },
    {
      "name": "Pierpaolo Brutti"
    },
    {
      "name": "Umberto Ferraro Petrillo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7shvc1amfnya5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13387",
  "fulltext_url": null,
  "id": "7shvc1amfnya5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13387",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7221540159",
  "pdf_url": "https://arxiv.org/pdf/2610.13387",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7shvc1amfnya5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:46Z",
      "native_id": "2610.13387",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13387"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Radiomics-Based Thyroid Nodule Classification with Label-Conditional Conformal Prediction",
  "title_text": "Radiomics-Based Thyroid Nodule Classification with Label-Conditional Conformal Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:46Z",
  "url": "https://research.draconitic.com/paper/7shvc1amfnya5",
  "venue": "arXiv",
  "versions": []
}