Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer.
Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer.
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DOI：
10.1002/mp.14684
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发表时间：
2021-03
期刊：
影响因子：
3.8
通讯作者：
Boellaard R
中科院分区：
文献类型：
--
作者：
Pfaehler E;Mesotten L;Zhovannik I;Pieplenbosch S;Thomeer M;Vanhove K;Adriaensens P;Boellaard R
放射组学是指提取描述医学图像中显示的肿瘤表型的大量图像生物标志物。从正电子发射断层扫描 (PET) 图像中提取的放射组学显示出对多种癌症类型的诊断和预后价值。然而，大量放射组学特征不可重复或与传统 PET 指标高度相关。此外，临床中使用的放射组学特征应能产生有关肿瘤质地的相关信息。在这项研究中，我们提出了一个框架来识别技术和临床有意义的特征，并使用 PET 非小细胞肺癌 (NSCLC) 数据集举例说明我们的结果。拟议的选择程序由几个步骤组成。首先，我们只包含在多中心环境中可重现的特征。接下来，我们应用体素随机化步骤来识别反映实际纹理信息的特征，即 90% 的患者扫描产生与随机纹理显着不同的值。最后，剩余的特征与标准 PET 指标相关，以进一步消除常见 PET 指标的冗余。针对不同的体积范围进行选择程序，即排除体积较小的病灶，以评估肿瘤大小对结果的影响。为了举例说明我们的过程，所选特征用于预测 150 名 NSCLC 患者数据集中的 1 年生存率。使用体积作为较小病变的预测因素，并使用选定的特征之一作为较大病变的预测因素来建立预测模型。将两种模型的预测精度与体积预测精度进行了比较。所选特征的数量取决于分析中包含的病变大小。当包含整个数据集时，在反映实际纹理的 19 个特征中，只有两个与传统 PET 指标没有很强的相关性。当排除小于 11.49 和 33.10 mL（数据集的 25 和 50 个百分位数）的病变时，在消除与标准 PET 指标高度相关的特征后，27 个特征中的 4 个和 29 个特征中的 13 个仍保留。当排除小于 103.9 mL（75%）的病灶时，53 个特征中的 33 个仍保留。对于较大的病变，其中一些特征在分类准确性方面优于体积特征（提高 4-10%）。与仅使用体积相比，使用体积作为较小病变的预测因子和较大病变的选定特征之一的组合也提高了准确性（从 72% 增加到 76%）。当对较小的病变进行放射组学分析时，应首先仔细研究纹理特征是否反映了实际的异质性信息。接下来，为了评估放射组学特征的附加价值，验证与所有传统 PET 指标不存在相关性至关重要。对大于 11.4 mL 的病变进行放射组学分析可能会为传统指标提供更多信息，同时反映实际的肿瘤纹理。使用体积和所选特征之一的组合进行预测有望提高放射组学模型的准确性和可靠性。
Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non‐small cell lung cancer (NSCLC) dataset. The proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1‐yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume. The number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4–10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%). When performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.
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影响因子：
9.3
作者：
Hatt, Mathieu;Majdoub, Mohamed;Visvikis, Dimitris
通讯作者：
Visvikis, Dimitris
影响因子：
2.5
作者：
Collewet, G;Strzelecki, M;Mariette, F
通讯作者：
Mariette, F
DOI：
10.1007/s00259-016-3325-5
发表时间：
2016-07-01
影响因子：
9.1
作者：
Desseroit, Marie-Charlotte;Visvikis, Dimitris;Hatt, Mathieu
通讯作者：
Hatt, Mathieu
影响因子：
10.6
作者：
Kim, Do-Hoon;Jung, Ji-hoon;Ahn, Byeong-Cheol
通讯作者：
Ahn, Byeong-Cheol
影响因子：
3.2
作者：
Kolinger, Guilherme D.;Garcia, David Vallez;Boellaard, Ronald
通讯作者：
Boellaard, Ronald