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脑胶质母细胞瘤 C-蛋氨酸 PET/CT 基于影像组学的肿瘤与正常脑 SUV 比值测定

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

机构信息

Scientific Board Secretary, Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia.

Medical Physicist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; PhD Student; National Research Nuclear University MEPhI, 31 Kashirskoe Shosse, Moscow, 115409, Russia.

出版信息

Sovrem Tekhnologii Med. 2023;15(1):5-11. doi: 10.17691/stm2023.15.1.01. Epub 2023 Jan 28.

Abstract

UNLABELLED

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. is to evaluate the potential of radiomics in the analysis of PET/CT glioblastoma images identifying the relationship between the radiomic features and the С-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 C-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.

摘要

目的

评估放射组学在识别与专家在常规中确定的 C-蛋氨酸肿瘤与正常脑摄取比(TNR)之间的关系的 PET/CT 脑胶质瘤图像分析中的潜力。

材料与方法

纳入 40 名(平均年龄 55±12 岁;77.5%为男性)经组织学证实为“脑胶质瘤”的患者的 PET/CT 数据(2018-2020 年)。TNR 计算为肿瘤与完整组织中 C-蛋氨酸标准摄取值的比值。对每个 PET 的放射组学特征进行计算,在肿瘤和周围组织的指定容积感兴趣区内进行。采用线性回归模型确定 TNR 与放射组学特征之间的关系。根据相关分析和 LASSO 正则化,将预测因子纳入模型。进行了 300 次机器学习实验,随机划分训练(70%)和测试(30%)子集。对 300 次测试获得的模型质量指标和预测因子重要性进行了总结。

结果

在 412 个与 TNR 显著相关的 PET/CT 放射组学参数中(p<0.05),正则化过程在每个模型中留下的参数不超过 30 个(中位数预测因子数量为 9 [7;13])。实验表明,TNR 与单独的放射组学特征之间存在非随机的线性相关性(Spearman 相关系数为 0.58 [0.43;0.74]),这些特征主要是分形维数,可描述图像的几何特征。

结论

放射组学可客观地确定反映脑胶质瘤生物学活性的 PET/CT 图像纹理特征。尽管在应用中存在局限性,但初步结果为神经肿瘤学中这些方法提供了良好的前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5764/10306961/5d3c387d0170/STM-15-1-01-f1.jpg

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