{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T22:34:37Z","timestamp":1791758077462,"version":"4.3.4"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T00:00:00Z","timestamp":1766275200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T00:00:00Z","timestamp":1766275200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Radiomics involves extracting and analyzing quantitative imaging features to support medical decision\u2010making, particularly in radiology and oncology. When applied to radiotherapy dose distributions, this approach, referred to as \u2018dosiomics\u2019, aims to identify the spatial dose patterns associated with treatment outcomes. However, software discrepancies in feature extraction may hinder reproducibility and limit the clinical adoption of radiomic\/dosiomic models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study presents the first comprehensive evaluation of software agreement and feature reproducibility across tools in the field of dosiomics, assessing seven feature\u2010extraction tools. The evaluation focused on the impact of built\u2010in image pre\u2010processing steps (e.g., interpolation and discretization), feature\u2010extraction configurations (i.e., aggregation methods), and the morphological characteristics of the regions of interest (ROIs), such as the presence of holes or disconnected components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      Five open\u2010source programs (MIRP, S\u2010IBEX, RaCaT, SERA, and PyRadiomics) and two proprietary tools (SPAARC and RadiomiCRO) were evaluated. The Image Biomarker Standardization Initiative (IBSI)\n                      <jats:italic>digital phantom<\/jats:italic>\n                      was used to preliminarily assess software IBSI\u2010compliance and to identify and exclude features with inconsistent implementation from subsequent analyses. Dosiomic features were then extracted from a digital dataset comprising eight Intensity Modulated Radiation Therapy (IMRT) dose distributions emulating a head and neck radiotherapy plan (available in both isotropic and anisotropic formats) and 10 ROIs, following a systematic feature extraction framework. The effects of pre\u2010processing parameters, feature\u2010extraction configurations, and ROI morphological characteristics were analyzed systematically.\n                    <\/jats:p>\n                    <jats:p>\n                      The evaluation metrics included the percentage of matching features across software to the third significant digit, the\n                      <jats:italic>Agreement<\/jats:italic>\n                      metric, and the coefficient of variation (CV) to quantify both software performance and dosiomic feature variability across them.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The preliminary IBSI\u2010compliance evaluation showed that MIRP, S\u2010IBEX, RaCaT, and SERA achieved over 94% matching features with IBSI benchmark values. In contrast, SPAARC, RadiomiCRO, and PyRadiomics demonstrated lower compliance due to non\u2010computable features.<\/jats:p>\n                    <jats:p>\n                      On dose distributions, all tools exhibited high match percentages (&gt;77%) for the isotropic dataset, which did not require software\u2010specific interpolation. However, discrepancies increased significantly with program\u2010specific interpolation for the anisotropic dose dataset, with match rates dropping to 14%.\n                      <jats:italic>Agreement<\/jats:italic>\n                      across software was consistently high for the isotropic dataset but notably lower for the anisotropic dataset. This trend was less evident when looking at the CV, which showed only a mild increase for the anisotropic format.\n                    <\/jats:p>\n                    <jats:p>\n                      Fixed bin size (FBS) discretization displayed lower\n                      <jats:italic>Agreement<\/jats:italic>\n                      and higher CV values, particularly in the cumulative intensity\u2010volume histogram (IVH) feature family. High CV values were predominantly observed for some feature family\u2010ROI combinations, including GLRLM, GLSZM, and NGLDM computed using 2.5D\/3D aggregation methods.\n                    <\/jats:p>\n                    <jats:p>Additionally, we observed that some binary masks were incorrectly generated (e.g., without holes) when using the DICOM format, therefore, we relied on NRRD input files whenever possible, resulting in feature reproducibility remaining unaffected by this aspect.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The findings of this study indicate that, when properly configured, the tools show good overall agreement, with variability limited to specific features and pre\u2010processing choices. While variations in program\u2010specific resampling and FBS discretization implementation are present, their overall impact on dosiomic feature reproducibility remains minimal.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70203","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T04:04:17Z","timestamp":1766376257000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing the agreement of radiomic tools for dosiomics: A multi\u2010software comparative study"],"prefix":"10.1002","volume":"53","author":[{"given":"Andrea","family":"Bettinelli","sequence":"first","affiliation":[{"name":"Medical Physics Department Veneto Institute of Oncology IOV \u2010 IRCCS Padua Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Marturano","sequence":"additional","affiliation":[{"name":"A. A. 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