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The proposed framework consists of two stages: (1) a CT\u2010based automatic segmentation network generating an initial GTV as a spatial mask prompt, and (2) a refinement network that jointly incorporates spatial mask prompt and LLaMA3\u2010encoded clinical text prompt derived from structured clinical information. Performance was evaluated using overlap, surface distance, and volumetric metrics, and compared with multiple state\u2010of\u2010the\u2010art deep learning models. Ablation and radiomics analyses assessed component contributions and downstream consistency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In internal validation, the framework achieved a DSC of 80.32\u00a0\u00b1\u00a06.65%, an HD95 of 11.42\u00a0\u00b1\u00a08.90\u00a0mm, and an absolute volume difference of 9.99\u00a0\u00b1\u00a010.49\u00a0mL; all Holm\u2010adjusted\n                      <jats:italic>p<\/jats:italic>\n                      values were &lt;0.001 versus the comparison models. Mean DSCs were 78.60\u00a0\u00b1\u00a013.39% and 78.36\u00a0\u00b1\u00a015.54% in the external cohorts. Mean DSCs were 78.60\u00a0\u00b1\u00a013.39% and 78.36\u00a0\u00b1\u00a015.54% in the two external cohorts, respectively. In external cohort A, ContextUNETR showed a lower mean HD95 than the proposed model (10.93\u00a0\u00b1\u00a08.03\u00a0vs. 13.44\u00a0\u00b1\u00a018.90\u00a0mm), although the paired difference was not statistically significant after Holm correction (adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.906). Compared with the direct Stage\u20101 output, the complete framework consistently improved overlap, boundary\u2010distance, and volumetric\u2010agreement metrics across the internal and both external validation cohorts (all adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001). LLaMA3 encoding improved DSC versus an MLP using the same variables (adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.009), whereas ASSD did not differ significantly. Automatic and manual GTVs showed high radiomic concordance and comparable prognostic performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The framework improved overlap and volumetric agreement for CT\u2010only primary esophageal GTV delineation across multicenter validation cohorts, although boundary\u2010distance performance varied across external cohorts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70721","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T18:02:58Z","timestamp":1792605778000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A dual\u2010prompt framework combining spatial\u2010mask priors and clinical\u2010text conditioning for CT\u2010only GTV delineation in esophageal cancer: A multicenter 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