{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T14:22:11Z","timestamp":1791814931767,"version":"4.3.4"},"posted":{"date-parts":[[2025,1,12]]},"group-title":"Radiology and Imaging","reference-count":47,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T00:00:00Z","timestamp":1736640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Currently, a majority of institution-specific automatic MRI-based contouring algorithms are trained, tested, and validated on one contrast weighting (i.e., T2-weighted), however their actual performance within this contrast weighting (i.e., across different repetition times, TR, and echo times, TE) is under-investigated and poorly understood. As a result, external institutions with different scan protocols for the same contrast weighting may experience sub-optimal performance.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>The purpose of this study was to develop a method to evaluate the robustness of automatic contouring algorithms to varying MRI contrast weightings.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>\n                    One healthy volunteer and one patient was scanned using SyntheticMR on the MR-Simulation device. The parotid and submandibular glands in these subjects were contoured using an automatic contouring algorithm trained on T2-weighted MRIs. For ground truth manual contours, two radiation oncology residents and one pre-resident physician were recruited and their STAPLE consensus was determined. A total of 216 different MRI TR and TE combinations were simulated across T1-, T2-, and PD-weighted contrast ranges using SyntheticMR\u2019s post-processing software, SyMRI. Comparisons between automatic contouring algorithm contours and the ground truth were determined using the Dice similarity coefficient (DSC) and 95\n                    <jats:sup>th<\/jats:sup>\n                    percentile Hausdorff distance (HD95).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Notable differences in the automatic contouring model\u2019s performance were seen across the contrast-weighted range, even within the T2-weighted range. Further, some models even performed as well or better across subsets of the T1-weighted range. The PD-weighted range saw the worst performance. The range of discrepancy in DSC and HD95 exceeded 0.2 and 3.66 mm, respectively, in some structures. In the T2-weighted contrast region where the model was trained, 100%, 40%, 24%, and 57% for the DSC in the left parotid, right parotid, left submandibular, and right submandibular gland, respectively, exceeded interobserver variability.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>This study demonstrates the variable performance of MRI-based automatic contouring algorithms across varying TR and TE combinations. This methodology could be applied in future studies as a method for evaluating model sensitivity, out of distribution detection ability, and performance drift.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.01.10.25319895","type":"posted-content","created":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T14:50:15Z","timestamp":1736693415000},"source":"Crossref","is-referenced-by-count":1,"title":["A Method for Sensitivity Analysis of Automatic Contouring Algorithms Across Different MRI Contrast Weightings Using SyntheticMR"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9788-7987","authenticated-orcid":false,"given":"Lucas","family":"McCullum","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2668-9717","authenticated-orcid":false,"given":"Zayne","family":"Belal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4371-8453","authenticated-orcid":false,"given":"Warren","family":"Floyd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7472-3701","authenticated-orcid":false,"given":"Alaa Mohamed 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