基于 CT 的放射组学特征鉴别胰腺神经内分泌肿瘤的组织学特征。
CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors.
机构信息
Department of Radiology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.
Radiology, Guys and St Thomas' NHS Foundation Trust, London, UK.
出版信息
Radiol Med. 2021 Jun;126(6):745-760. doi: 10.1007/s11547-021-01333-z. Epub 2021 Feb 1.
PURPOSE
To assess the ability of radiomic features (RF) extracted from contrast-enhanced CT images (ceCT) and non-contrast-enhanced (non-ceCT) in discriminating histopathologic characteristics of pancreatic neuroendocrine tumors (panNET).
METHODS
panNET contours were delineated on pre-surgical ceCT and non-ceCT. First- second- and higher-order RF (adjusted to eliminate redundancy) were extracted and correlated with histological panNET grade (G1 vs G2/G3), metastasis, lymph node invasion, microscopic vascular infiltration. Mann-Whitney with Bonferroni corrected p values assessed differences. Discriminative power of significant RF was calculated for each of the end-points. The performance of conventional-imaged-based-parameters was also compared to RF.
RESULTS
Thirty-nine patients were included (mean age 55-years-old; 24 male). Mean diameters of the lesions were 24 × 27 mm. Sixty-nine RF were considered. Sphericity could discriminate high grade tumors (AUC = 0.79, p = 0.002). Tumor volume (AUC = 0.79, p = 0.003) and several non-ceCT and ceCT RF were able to identify microscopic vascular infiltration: voxel-alignment, neighborhood intensity-difference and intensity-size-zone families (AUC ≥ 0.75, p < 0.001); voxel-alignment, intensity-size-zone and co-occurrence families (AUC ≥ 0.78, p ≤ 0.002), respectively). Non-ceCT neighborhood-intensity-difference (AUC = 0.75, p = 0.009) and ceCT intensity-size-zone (AUC = 0.73, p = 0.014) identified lymph nodal invasion; several non-ceCT and ceCT voxel-alignment family features were discriminative for metastasis (p < 0.01, AUC = 0.80-0.85). Conventional CT 'necrosis' could discriminate for microscopic vascular invasion (AUC = 0.76, p = 0.004) and 'arterial vascular invasion' for microscopic metastasis (AUC = 0.86, p = 0.001). No conventional-imaged-based-parameter was significantly associated with grade and lymph node invasion.
CONCLUSIONS
Radiomic features can discriminate histopathology of panNET, suggesting a role of radiomics as a non-invasive tool for tumor characterization.
TRIAL REGISTRATION NUMBER
NCT03967951, 30/05/2019.
目的
评估从对比增强 CT 图像（ceCT）和非对比增强（non-ceCT）中提取的放射组学特征（RF）在鉴别胰腺神经内分泌肿瘤（panNET）的组织病理学特征方面的能力。
方法
在术前 ceCT 和 non-ceCT 上描绘 panNET 轮廓。提取一阶、二阶和更高阶 RF（调整以消除冗余），并与组织学 panNET 分级（G1 与 G2/G3）、转移、淋巴结浸润、微观血管浸润相关。采用 Mann-Whitney 检验和 Bonferroni 校正 p 值评估差异。计算每个终点的显著 RF 的判别能力。还比较了基于常规成像的参数与 RF 的性能。
结果
共纳入 39 例患者（平均年龄 55 岁；24 例男性）。病变的平均直径为 24×27mm。共考虑了 69 个 RF。球形度可区分高级别肿瘤（AUC=0.79，p=0.002）。肿瘤体积（AUC=0.79，p=0.003）和一些 non-ceCT 和 ceCT RF 能够识别微观血管浸润：体素配准、邻域强度差和强度大小区域家族（AUC≥0.75，p<0.001）；体素配准、强度大小区域和共生家族（AUC≥0.78，p≤0.002），分别）。Non-ceCT 邻域强度差（AUC=0.75，p=0.009）和 ceCT 强度大小区域（AUC=0.73，p=0.014）可识别淋巴结浸润；一些 non-ceCT 和 ceCT 体素配准家族特征对转移具有判别能力（p<0.01，AUC=0.80-0.85）。常规 CT“坏死”可区分微观血管浸润（AUC=0.76，p=0.004），“动脉血管浸润”可区分微观转移（AUC=0.86，p=0.001）。没有常规成像参数与分级和淋巴结浸润有显著相关性。
结论
放射组学特征可鉴别 panNET 的组织病理学，提示放射组学作为一种肿瘤特征的非侵入性工具具有一定作用。
试验注册号
NCT03967951，2019 年 5 月 30 日。
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