Lyra Vassiliki, Chatziioannou Sofia, Kallergi Maria
Nuclear Medicine Department, General University Hospital of Larissa, 411 10 Larissa, Greece.
2nd Department of Radiology, Nuclear Medicine Section, Attikon University Hospital of Athens, 124 62 Chaidari, Greece.
Metabolites. 2022 Feb 28;12(3):217. doi: 10.3390/metabo12030217.
Pediatric cancer, although rare, requires the most optimized treatment approach to obtain high survival rates and minimize serious long-term side effects in early adulthood. F-FDG PET/CT is most helpful and widely used in staging, recurrence detection, and response assessment in pediatric oncology. The well-known F-FDG PET metabolic indices of metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG) have already revealed an independent significant prognostic value for survival in oncologic patients, although the corresponding cut-off values remain study-dependent and not validated for use in clinical practice. Advanced tumor "radiomic" analysis sheds new light into these indices. Numerous patterns of texture F-FDG uptake features can be extracted from segmented PET tumor images due to new powerful computational systems supporting complex "deep learning" algorithms. This high number of "quantitative" tumor imaging data, although not decrypted in their majority and once standardized for the different imaging systems and segmentation methods, could be used for the development of new "clinical" models for specific cancer types and, more interestingly, for specific age groups. In addition, data from novel techniques of tumor genome analysis could reveal new genes as biomarkers for prognosis and/or targeted therapies in childhood malignancies. Therefore, this ever-growing information of "radiogenomics", in which the underlying tumor "genetic profile" could be expressed in the tumor-imaging signature of "radiomics", possibly represents the next model for precision medicine in pediatric cancer management. This paper reviews F-FDG PET image segmentation methods as applied to pediatric sarcomas and lymphomas and summarizes reported findings on the values of metabolic and radiomic features in the assessment of these pediatric tumors.
儿科癌症虽然罕见,但需要最优化的治疗方法,以获得高生存率,并将成年早期严重的长期副作用降至最低。F-FDG PET/CT在儿科肿瘤学的分期、复发检测和疗效评估中最有帮助且应用广泛。代谢肿瘤体积(MTV)和肿瘤病灶糖酵解(TLG)这两个著名的F-FDG PET代谢指标已显示出对肿瘤患者生存具有独立的显著预后价值,尽管相应的临界值仍因研究而异,尚未在临床实践中得到验证。先进的肿瘤“放射组学”分析为这些指标带来了新的启示。由于支持复杂“深度学习”算法的强大新计算系统,可以从分割后的PET肿瘤图像中提取大量F-FDG摄取纹理特征模式。尽管这些大量的“定量”肿瘤成像数据大多尚未解密,且一旦针对不同成像系统和分割方法进行标准化,可用于开发针对特定癌症类型,更有趣的是针对特定年龄组的新“临床”模型。此外,肿瘤基因组分析新技术的数据可能揭示新的基因,作为儿童恶性肿瘤预后和/或靶向治疗的生物标志物。因此,这种不断增长的“放射基因组学”信息,其中潜在的肿瘤“基因图谱”可以在“放射组学”的肿瘤成像特征中表达,可能代表了儿科癌症管理中精准医学的下一个模式。本文综述了应用于儿科肉瘤和淋巴瘤的F-FDG PET图像分割方法,并总结了关于代谢和放射组学特征在评估这些儿科肿瘤中的价值的报道结果。