Pullen Lieke C E, Noortman Wyanne A, Triemstra Lianne, de Jongh Cas, Rademaker Fenna J, Spijkerman Romy, Kalisvaart Gijsbert M, Gertsen Emma C, de Geus-Oei Lioe-Fee, Tolboom Nelleke, de Steur Wobbe O, Dantuma Maura, Slart Riemer H J A, van Hillegersberg Richard, Siersema Peter D, Ruurda Jelle P, van Velden Floris H P, Vegt Erik
Biomedical Photonic Imaging Group, University of Twente, 7522 NB Enschede, The Netherlands.
Department of Radiology, Leiden University Medical Center, 2333 ZD Leiden, The Netherlands.
Cancers (Basel). 2023 May 23;15(11):2874. doi: 10.3390/cancers15112874.
To improve identification of peritoneal and distant metastases in locally advanced gastric cancer using [F]FDG-PET radiomics.
[F]FDG-PET scans of 206 patients acquired in 16 different Dutch hospitals in the prospective multicentre PLASTIC-study were analysed. Tumours were delineated and 105 radiomic features were extracted. Three classification models were developed to identify peritoneal and distant metastases (incidence: 21%): a model with clinical variables, a model with radiomic features, and a clinicoradiomic model, combining clinical variables and radiomic features. A least absolute shrinkage and selection operator (LASSO) regression classifier was trained and evaluated in a 100-times repeated random split, stratified for the presence of peritoneal and distant metastases. To exclude features with high mutual correlations, redundancy filtering of the Pearson correlation matrix was performed (r = 0.9). Model performances were expressed by the area under the receiver operating characteristic curve (AUC). In addition, subgroup analyses based on Lauren classification were performed.
None of the models could identify metastases with low AUCs of 0.59, 0.51, and 0.56, for the clinical, radiomic, and clinicoradiomic model, respectively. Subgroup analysis of intestinal and mixed-type tumours resulted in low AUCs of 0.67 and 0.60 for the clinical and radiomic models, and a moderate AUC of 0.71 in the clinicoradiomic model. Subgroup analysis of diffuse-type tumours did not improve the classification performance.
Overall, [F]FDG-PET-based radiomics did not contribute to the preoperative identification of peritoneal and distant metastases in patients with locally advanced gastric carcinoma. In intestinal and mixed-type tumours, the classification performance of the clinical model slightly improved with the addition of radiomic features, but this slight improvement does not outweigh the laborious radiomic analysis.
利用[F]FDG-PET放射组学提高局部进展期胃癌腹膜和远处转移的识别。
分析了前瞻性多中心PLASTIC研究中在荷兰16家不同医院采集的206例患者的[F]FDG-PET扫描图像。勾勒出肿瘤轮廓并提取105个放射组学特征。开发了三种分类模型来识别腹膜和远处转移(发生率:21%):一个包含临床变量的模型、一个包含放射组学特征的模型以及一个结合临床变量和放射组学特征的临床放射组学模型。采用最小绝对收缩和选择算子(LASSO)回归分类器,并在100次重复随机分割中进行训练和评估,按腹膜和远处转移的存在情况进行分层。为排除具有高相互相关性的特征,对Pearson相关矩阵进行冗余过滤(r = 0.9)。模型性能用受试者操作特征曲线下面积(AUC)表示。此外,还基于劳伦分类进行了亚组分析。
临床模型、放射组学模型和临床放射组学模型的AUC分别为0.59、0.51和0.56,均无法很好地识别转移。对肠型和混合型肿瘤的亚组分析显示,临床模型和放射组学模型的AUC较低,分别为0.67和0.60,而临床放射组学模型的AUC为中等水平,为0.71。弥漫型肿瘤的亚组分析并未改善分类性能。
总体而言,基于[F]FDG-PET的放射组学对局部进展期胃癌患者术前腹膜和远处转移的识别并无帮助。在肠型和混合型肿瘤中,加入放射组学特征后临床模型的分类性能略有改善,但这种轻微改善并不能抵消繁琐的放射组学分析工作。