{
  "abstract": "Objective: To investigate a cascaded conditional generative adversarial network (cGAN)–based method for synthesizing 18F-FDG PET images from 18F-octreotide, optimize model parameter configurations, and evaluate the quality of the synthesized images.\n\nMethods: Imaging data from 71 tumor patients who underwent dual-tracer PET/CT examinations with 18F-FDG and 18F-octreotide between May 2022 and November 2025 were retrospectively collected. A cascaded deep learning architecture (first stage: 3D U-Net; second stage: self-attention 3D V-Net) was employed to evaluate the effects of image preprocessing, resampling size, and patch size on synthesis quality. Three preprocessing strategies (log transformation, normalization, and a combined approach) were compared. Image quality was assessed using five objective metrics (SSIM, PSNR, MSE, MAE, and NMI) and Likert 5-point ratings from three nuclear medicine physicians; liver and lesion-level SUV agreement were further evaluated.\n\nResults: In the held-out test cohort (n = 14), the combined preprocessing method (log transformation + normalization) significantly outperformed the individual methods (SSIM: 0.8454 ± 0.0328 vs. 0.7831 ± 0.0337 vs. 0.7438 ± 0.0362, P < 0.001). On the identical held-out test cohort (n = 14), the Group 2 parameter configuration (resampling size: 256 × 256 × 512; patch size: 128 × 128 × 128) outperformed Group 1, with an SSIM of 0.8854 ± 0.0455 and a PSNR of 28.15 ± 3.35 dB, representing relative improvements of 19.1% and 83.5%, respectively, over Group 1 (paired-samples t-test, both P < 0.001). The mean Likert score was 4.53 ± 0.19. Quantitative analysis of liver SUVmean showed no significant difference between synthesized and real images (P = 0.967, paired t-test). Lesion-level quantitative analysis demonstrated high agreement between synthetic and real 18F-FDG images, with Pearson correlation coefficients of 1.0000, 0.9995, and 0.9991 for SUVmax and SUVmean (n = 14) and SUVpeak (n = 13), respectively (all P < 0.001), no significant differences on paired t-tests (all P > 0.05), and all Bland–Altman differences within the 95% limits of agreement.\n\nConclusion: The cascaded deep learning model enabled cross-tracer synthesis from 18F-octreotide to 18F-FDG PET images. In this exploratory, single-center pilot cohort, the optimized configuration demonstrated promising quantitative and visual performance, supporting image-synthesis feasibility and potential clinical utility of a single-injection, dual-parameter molecular imaging approach for neuroendocrine tumors. Prospective multicenter validation is warranted before any clinical application.",
  "abstract_shown": true,
  "affiliations": [
    "PET/CT Center, The First Affiliated Hospital of Xi’an Jiaotong University , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ruxi Chang"
    },
    {
      "affs": [
        0
      ],
      "name": "Liping Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jungang Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiang Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Bo Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoyi Duan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0bnyxexca6tz0.bib",
  "cited_by": 0,
  "doi": "10.3389/fnume.2026.1920721",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719449/?tool=EBI",
  "id": "0bnyxexca6tz0",
  "issns": [
    "2673-8880"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13719449",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2741|Medical Imaging Techniques and Applications"
    ]
  ],
  "openalex": "W7222582869",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719449/pdf/?tool=EBI",
  "pmcid": "PMC13719449",
  "pmid": "42964248",
  "published": "2026-10-16",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/0bnyxexca6tz0.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13719449",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719449"
    },
    {
      "first_seen": "2026-11-02T12:23:24Z",
      "native_id": "PMC13719449",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fnume.2026.1920721"
    }
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Medical Imaging Techniques and Applications",
      "subfield": "imaging"
    }
  ],
  "title": "Cascaded deep learning for synthesis of 18F-FDG PET from 18F-octreotide: model parameter optimization and performance evaluation",
  "title_text": "Cascaded deep learning for synthesis of 18F-FDG PET from 18F-octreotide: model parameter optimization and performance evaluation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:30Z",
  "url": "https://research.draconitic.com/paper/0bnyxexca6tz0",
  "venue": "Frontiers in nuclear medicine",
  "versions": []
}