{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T06:04:17Z","timestamp":1790661857379,"version":"4.1.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gross extrathyroidal extension (ETE) affects surgical management and prognosis in papillary thyroid carcinoma (PTC), yet noninvasive assessment is difficult.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective exploratory feasibility study, 101 patients with pathologically confirmed PTC who underwent multi-phase contrast-enhanced MRI (MCE MRI) were analyzed. From the second and seventh contrast phases, four derived image types (ADD, ADD\/SUB, Max, Min) were generated. Deep features from six image modalities were extracted using fine-tuned ResNet101, and radiomics classifiers were trained to predict gross ETE. Logistic regression identified independent clinical and MRI predictors, which, together with the deep learning radiomic signatures (DLR_signature) derived from the best-performing derived image type and algorithm, were integrated into a nomogram. Model performance was assessed by ROC, calibration, decision curve analysis, and ten-fold cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Gross ETE was present in 37\/101 cases (36.6%). The Min-modality deep transfer learning model achieved AUCs of 0.852 (training) and 0.853 (validation); the corresponding radiomics classifier had AUCs of 0.870 and 0.805. Multivariate analysis identified mean apparent diffusion coefficient (ADC_mean) (P\u00a0=\u00a00.001) and protrusion_value (P &amp;lt; 0.001) as independent predictors. The integrated nomogram combining Min-modality DLR_signature, protrusion_value, and ADC_mean achieved AUCs of 0.956 (training) and 0.895 (validation), with average AUCs of 0.875 and 0.846 in cross-validation. Calibration and decision-curve analyses showed favorable performance in the current dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed nomogram integrating a Min-modality DLR_signature with quantitative MRI parameters showed promising potential for the noninvasive prediction of gross ETE in PTC. These findings support the feasibility of using MCE MRI post-processing for preoperative ETE prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1917885","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T05:51:01Z","timestamp":1790661061000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of gross extrathyroidal extension in papillary thyroid carcinoma using a deep learning radiomics model based on post-processing techniques for multi-phase contrast-enhanced MRI images"],"prefix":"10.3389","volume":"17","author":[{"given":"Yun","family":"Zeng","sequence":"first","affiliation":[{"name":"Department of Radiology, Minhang Hospital, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Minhang 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