Alireza Safarian , Seyed Ali Mirshahvalad , Abolfazl Farbod , Theresa Jung , Hadi Nasrollahi , Gregor Schweighofer-Zwink , Gundula Rendl , Christian Pirich , Reza Vali , Mohsen Beheshti
{"title":"人工智能在肿瘤中的应用[18F]FDG PET成像:进展和未来趋势-第二部分。","authors":"Alireza Safarian , Seyed Ali Mirshahvalad , Abolfazl Farbod , Theresa Jung , Hadi Nasrollahi , Gregor Schweighofer-Zwink , Gundula Rendl , Christian Pirich , Reza Vali , Mohsen Beheshti","doi":"10.1053/j.semnuclmed.2025.06.012","DOIUrl":null,"url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) into [<sup>18</sup>F]FDG PET/CT imaging continues to expand, offering new opportunities for more precise, consistent, and personalized oncologic evaluations. Building on the foundation established in Part I, this second part explores AI-driven innovations across a broader range of malignancies, including hematological, genitourinary, melanoma, and central nervous system tumors as well applications of AI in pediatric oncology.</div><div>Radiomics and machine learning algorithms are being explored for their ability to enhance diagnostic accuracy, reduce interobserver variability, and inform complex clinical decision-making, such as identifying patients with refractory lymphoma, assessing pseudoprogression in melanoma, or predicting brain metastases in extracranial malignancies. Additionally, AI-assisted lesion segmentation, quantitative feature extraction, and heterogeneity analysis are contributing to improved prediction of treatment response and long-term survival outcomes. Despite encouraging results, variability in imaging protocols, segmentation methods, and validation strategies across studies continues to challenge reproducibility and remains a barrier to clinical translation. This review evaluates recent advancements of AI, its current clinical applications, and emphasizes the need for robust standardization and prospective validation to ensure the reproducibility and generalizability of AI tools in PET imaging and clinical practice.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 5","pages":"Pages 856-868"},"PeriodicalIF":5.9000,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence for Tumor [18F]FDG PET Imaging: Advancements and Future Trends - Part II\",\"authors\":\"Alireza Safarian , Seyed Ali Mirshahvalad , Abolfazl Farbod , Theresa Jung , Hadi Nasrollahi , Gregor Schweighofer-Zwink , Gundula Rendl , Christian Pirich , Reza Vali , Mohsen Beheshti\",\"doi\":\"10.1053/j.semnuclmed.2025.06.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The integration of artificial intelligence (AI) into [<sup>18</sup>F]FDG PET/CT imaging continues to expand, offering new opportunities for more precise, consistent, and personalized oncologic evaluations. Building on the foundation established in Part I, this second part explores AI-driven innovations across a broader range of malignancies, including hematological, genitourinary, melanoma, and central nervous system tumors as well applications of AI in pediatric oncology.</div><div>Radiomics and machine learning algorithms are being explored for their ability to enhance diagnostic accuracy, reduce interobserver variability, and inform complex clinical decision-making, such as identifying patients with refractory lymphoma, assessing pseudoprogression in melanoma, or predicting brain metastases in extracranial malignancies. Additionally, AI-assisted lesion segmentation, quantitative feature extraction, and heterogeneity analysis are contributing to improved prediction of treatment response and long-term survival outcomes. Despite encouraging results, variability in imaging protocols, segmentation methods, and validation strategies across studies continues to challenge reproducibility and remains a barrier to clinical translation. This review evaluates recent advancements of AI, its current clinical applications, and emphasizes the need for robust standardization and prospective validation to ensure the reproducibility and generalizability of AI tools in PET imaging and clinical practice.</div></div>\",\"PeriodicalId\":21643,\"journal\":{\"name\":\"Seminars in nuclear medicine\",\"volume\":\"55 5\",\"pages\":\"Pages 856-868\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seminars in nuclear medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0001299825000868\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seminars in nuclear medicine","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0001299825000868","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Artificial Intelligence for Tumor [18F]FDG PET Imaging: Advancements and Future Trends - Part II
The integration of artificial intelligence (AI) into [18F]FDG PET/CT imaging continues to expand, offering new opportunities for more precise, consistent, and personalized oncologic evaluations. Building on the foundation established in Part I, this second part explores AI-driven innovations across a broader range of malignancies, including hematological, genitourinary, melanoma, and central nervous system tumors as well applications of AI in pediatric oncology.
Radiomics and machine learning algorithms are being explored for their ability to enhance diagnostic accuracy, reduce interobserver variability, and inform complex clinical decision-making, such as identifying patients with refractory lymphoma, assessing pseudoprogression in melanoma, or predicting brain metastases in extracranial malignancies. Additionally, AI-assisted lesion segmentation, quantitative feature extraction, and heterogeneity analysis are contributing to improved prediction of treatment response and long-term survival outcomes. Despite encouraging results, variability in imaging protocols, segmentation methods, and validation strategies across studies continues to challenge reproducibility and remains a barrier to clinical translation. This review evaluates recent advancements of AI, its current clinical applications, and emphasizes the need for robust standardization and prospective validation to ensure the reproducibility and generalizability of AI tools in PET imaging and clinical practice.
期刊介绍:
Seminars in Nuclear Medicine is the leading review journal in nuclear medicine. Each issue brings you expert reviews and commentary on a single topic as selected by the Editors. The journal contains extensive coverage of the field of nuclear medicine, including PET, SPECT, and other molecular imaging studies, and related imaging studies. Full-color illustrations are used throughout to highlight important findings. Seminars is included in PubMed/Medline, Thomson/ISI, and other major scientific indexes.