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First, we utilized encoders from a Convolutional Neural Network and a Vision Transformer (ViT), pre-trained with four learning strategies: from scratch, ImageNet-1K (1,000 classes of natural images), FractalDB-1K (1,000 classes of fractal images), and FractalDB-10K (10,000 classes of fractal images), with the latter three utilizing publicly available models. Second, the models were fine-tuned using CT images and physician-created contour data. Model accuracy was then evaluated using the volumetric Dice Similarity Coefficient (vDSC), surface Dice Similarity Coefficient (sDSC), and 95th percentile Hausdorff Distance (HD95) between the predicted and ground truth GTV contours, averaged across the fourfold cross-validation. Additionally, the segmentation accuracy was compared between simple and complex groups, categorized by the surface-to-volume ratio, to assess the impact of GTV shape complexity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Pre-trained with FractalDB-10K yielded the best segmentation accuracy across all metrics. For the ViT model, the vDSC, sDSC, and HD95 results were 0.800\u2009\u00b1\u20090.079, 0.732\u2009\u00b1\u20090.152, and 2.04\u2009\u00b1\u20091.59\u00a0mm for FractalDB-10K; 0.779\u2009\u00b1\u20090.093, 0.688\u2009\u00b1\u20090.156, and 2.72\u2009\u00b1\u20093.12\u00a0mm for FractalDB-1K; 0.764\u2009\u00b1\u20090.102, 0.660\u2009\u00b1\u20090.156, and 3.03\u2009\u00b1\u20093.47\u00a0mm for ImageNet-1K, respectively. In conditions FractalDB-1K and ImageNet-1K, there was no significant difference in the simple group, whereas the complex group showed a significantly higher vDSC (0.743\u2009\u00b1\u20090.095 vs 0.714\u2009\u00b1\u20090.104,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.006).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Pre-training with fractal structures achieved comparable or superior accuracy to ImageNet pre-training for early-stage lung cancer GTV auto-segmentation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11604-025-01865-8","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T03:45:40Z","timestamp":1757907940000},"page":"183-195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fractal-driven self-supervised learning enhances early-stage lung cancer GTV segmentation: a novel transfer learning framework"],"prefix":"10.1007","volume":"44","author":[{"given":"Ryota","family":"Tozuka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5018-3800","authenticated-orcid":false,"given":"Noriyuki","family":"Kadoya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arata","family":"Yasunaga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahide","family":"Saito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takafumi","family":"Komiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hikaru","family":"Nemoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidetoshi","family":"Ando","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Onishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keiichi","family":"Jingu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"issue":"4","key":"1865_CR1","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1097\/JTO.0000000000000119","volume":"9","author":"AC Lo","year":"2014","unstructured":"Lo AC, Liu M, Chan E, Lund C, Truong PT, Loewen S, et al. 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