{
  "abstract": "Voxel-level prediction of treatment response from longitudinal Fluorodexoyglucose (FDG) positron emission tomography / computed tomography (PET/CT) can enable spatially adaptive dose planning in advanced non-small cell lung cancer (NSCLC), but point predictions alone lack the uncertainty quantification needed for safe clinical decision-making. Standard conformal prediction (CP) provides distribution-free coverage but yields spatially uniform intervals and assumes exchangeability--an assumption violated by intra-tumoral spatial correlation. We propose a multiscale CP framework built on a re-optimized VoxelForecast generalized least squares model that explicitly captures voxel-level covariance. Variogram sensitivity analysis identified the Stable model as significantly superior to previouly used Matern family. The framework combines hierarchical nested leave-one-patient-out calibration, which restores exchangeability for valid voxel-level coverage, with residual-variance CP (VarCP), which learns a heteroscedastic, location-dependent scale to produce adaptive intervals while preserving finite-sample marginal coverage guarantees. On two prospective NSCLC trial cohorts--FLARE-RT (N=25 locally advanced, chemoradio-therapy; 11,100 voxels) and BRIGHT (N=19 metastatic, chemoimmunotherapy; 26,980 voxels)--multiscale feature integration reduced voxel-level prediction error by 35% and 31%, respectively. VarCP maintained nominal coverage while producing 7-18% (FLARE-RT) and 4-8% (BRIGHT) narrower voxel-level intervals than a hierarchically calibrated standard CP baseline (paired Wilcoxon signed-rank, significant at most miscoverage levels), with comparable lesion-level gains (7-18% and 6-10%) via median-based aggregation of voxel residuals. VarCP delivers tighter yet mathematically guaranteed spatial uncertainty maps, supporting both voxel-level dose-adjustment and whole-lesion treatment planning in advanced NSCLC.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiation Oncology, University of Washington, Seattle, WA"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "S. Wang"
    },
    {
      "name": "L. Deng"
    },
    {
      "name": "T. Ye"
    },
    {
      "name": "P. E. Kinahan"
    },
    {
      "name": "J. Zeng"
    },
    {
      "name": "J. H. Gennari"
    },
    {
      "affs": [
        0
      ],
      "name": "S. R. Bowen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3r3c8xj8nczz6.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.30.26364431",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26364431v1.full",
  "id": "3r3c8xj8nczz6",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26364431v1",
  "language": null,
  "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": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7216505414",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26364431v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3r3c8xj8nczz6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:56Z",
      "native_id": "10.64898/2026.09.30.26364431",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26364431v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Uncertainty-Aware Multiscale Treatment Response Forecasting in Advanced Non-Small Cell Lung Cancer Using Residual-Variance Conformal Prediction",
  "title_text": "Uncertainty-Aware Multiscale Treatment Response Forecasting in Advanced Non-Small Cell Lung Cancer Using Residual-Variance Conformal Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:56Z",
  "url": "https://research.draconitic.com/paper/3r3c8xj8nczz6",
  "venue": "medRxiv",
  "versions": []
}