To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors. Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COVL). The RFs’ correlation with volume and SUVmax was analyzed by calculating Pearson’s correlation coefficients. DSC mean value was 0.75 ± 0.11 (0.45–0.92) between SAEB and operators and 0.78 ± 0.09 (0.36–0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax. RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.

中文翻译：

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分割和离散化对神经内分泌肿瘤 68Ga-DOTA-TOC PET/CT 图像放射组学特征的影响

旨在确定分割方法和强度离散化对神经内分泌肿瘤患者 68Ga-DOTA-TOC PET 图像中放射组学特征 (RF) 提取的影响。对 49 例患者进行回顾性分析。肿瘤轮廓由四名不同的操作员手动执行，并使用半自动基于边缘的分割（SAEB）算法。应用了三个 SUVmax 固定阈值（20%、30%、40%）。应用两种不同强度重新缩放因子进行灰度离散化，提取了 51 个 RF：一种是绝对值（AR60 = SUV 从 0 到 60），一种是相对值（RR = VOI SUV 的最小值-最大值）。计算骰子相似系数（DSC）以量化不同分割方法之间的分割一致性。分割和离散化对 RF 的影响通过类内相关系数 (ICC) 和方差系数 (COVL) 进行评估。通过计算 Pearson 相关系数来分析 RF 与体积和 SUVmax 的相关性。SAEB 和操作员之间的 DSC 平均值为 0.75 ± 0.11 (0.45–0.92)，四种手动分割之间的 DSC 平均值为 0.78 ± 0.09 (0.36–0.97)。该研究显示出高稳健性（ICC > 0.9）：（a）使用 AR60 的分割方法的 RF 中有 64.7%，通过应用 40% 的 SUVmax 阈值得到改善（86.5%）；(b) 使用 AR60 在不同 SUVmax 阈值的 50.9% 的 RF 中；(c) 在 37% 的 RF 中使用不同的分割方法进行离散化设置。一些 RF 与体积和 SUVmax 不相关。与 18F-FDG PET/CT 图像相比，NET 68Ga-DOTA-TOC 图像中的 RF 对手动分割的鲁棒性更高。百分之四十的 SUVmax 阈值可在操作员中产生卓越的 RF 稳定性，但可能会导致生物信息丢失。SAEB 分割似乎是手动分割的最佳替代方案，但需要进一步验证。最后，离散化设置对 RF 稳健性影响很大，应始终予以说明。