基于 11C 蛋氨酸 PET/CT 确定胶质母细胞瘤中肿瘤与正常脑 SUV 比值的放射组学研究

IF 1.1 Q4 MEDICINE, RESEARCH & EXPERIMENTAL
Sovremennye Tehnologii v Medicine Pub Date : 2023-01-01 Epub Date: 2023-01-28 DOI:10.17691/stm2023.15.1.01
G V Danilov, D B Kalayeva, N B Vikhrova, T A Konakova, A I Zagorodnova, A A Popova, A A Postnov, S V Shugay, I N Pronin
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引用次数: 0

摘要

对胶质母细胞瘤患者进行 PET/CT 定量分析的现代方法在临床环境中没有严格的标准,也不排除人为因素的影响。放射组学方法可促进统一,提高医学影像分析的客观性和效率。本研究旨在评估放射组学在 PET/CT 胶质母细胞瘤图像分析中的潜力,确定放射组学特征与专家常规确定的 11С-蛋氨酸肿瘤与正常脑摄取比(TNR)之间的关系:分析对象包括40名经组织学确诊为 "胶质母细胞瘤 "的患者(平均年龄为55±12岁;77.5%为男性)的PET/CT数据(2018-2020年)。TNR按肿瘤和完整组织中测得的11C-蛋氨酸标准化摄取值的比值计算。每种 PET 的放射学特征计算都在指定的感兴趣容积区域内进行,捕捉肿瘤和周围组织。使用线性回归模型确定 TNR 与放射学特征之间的关系。在进行相关性分析和 LASSO 正则化后,预测因子被纳入模型。机器学习实验重复了 300 次,将训练子集(70%)和测试子集(30%)随机分开。结果:结果:412 个 PET/CT 放射学参数与 TNR 显著相关(p):放射组学能够客观确定 PET/CT 图像纹理特征,反映胶质母细胞瘤的生物活性。尽管在应用中还存在局限性,但首批结果为这些方法在神经肿瘤学中的应用提供了良好的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on <sup>11</sup>C-Methionine PET/CT in Glioblastoma.

Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on <sup>11</sup>C-Methionine PET/CT in Glioblastoma.

Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on <sup>11</sup>C-Methionine PET/CT in Glioblastoma.

Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on 11C-Methionine PET/CT in Glioblastoma.

Modern methodology of PET/CT quantitative analysis in patients with glioblastomas is not strictly standardized in clinic settings and does not exclude the influence of the human factor. Methods of radiomics may facilitate unification, and improve objectivity and efficiency of the medical image analysis. The aim of the study is to evaluate the potential of radiomics in the analysis of PET/CT glioblastoma images identifying the relationship between the radiomic features and the 11С-methionine tumor-to-normal brain uptake ratio (TNR) determined by an expert in routine.

Materials and methods: PET/CT data (2018-2020) from 40 patients (average age was 55±12 years; 77.5% were males) with a histologically confirmed diagnosis of "glioblastoma" were included in the analysis. TNR was calculated as a ratio of the standardized uptake value of 11C-methionine measured in the tumor and intact tissue. Calculation of radiomic features for each PET was performed in the specified volumetric region of interest, capturing the tumor with the surrounding tissues. The relationship between TNR and the radiomic features was determined using the linear regression model. Predictors were included in the model following correlation analysis and LASSO regularization. The experiment with machine learning was repeated 300 times, splitting the training (70%) and test (30%) subsets randomly. The model quality metrics and predictor significance obtained in 300 tests were summarized.

Results: Of 412 PET/CT radiomic parameters significantly correlated with TNR (p<0.05), the regularization procedure left no more than 30 in each model (the median number of predictors was 9 [7; 13]). The experiment has demonstrated a non-random linear correlation (the Spearman correlation coefficient was 0.58 [0.43; 0.74]) between TNR and separate radiomic features, primarily fractal dimensions, characterizing the geometrical properties of the image.

Conclusion: Radiomics enabled an objective determination of PET/CT image texture features reflecting the biological activity of glioblastomas. Despite the existing limitations in the application, the first results provide a good perspective of these methods in neurooncology.

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Sovremennye Tehnologii v Medicine
Sovremennye Tehnologii v Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
1.80
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