Mo Shuangyang, Wang Yingwei, Huang Cheng, Wu Wenhong, Qin Shanyu
Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Gastroenterology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China.
Heliyon. 2024 Jul 9;10(14):e34344. doi: 10.1016/j.heliyon.2024.e34344. eCollection 2024 Jul 30.
This research aimed to retrospectively construct and authenticate ultrasomics models using endoscopic ultrasonography (EUS) images for forecasting the pathological grading of pancreatic neuroendocrine tumors (PNETs).
After confirmation through pathological examination, a retrospective analysis of 79 patients was conducted, including 49 with grade 1 PNETs and 30 with grade 2/3 PNETs. These patients were randomized to the training or test cohort in a 6:4 proportion. The least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimensionality of ultrasomics features derived from standard EUS images. These nonzero coefficient features were retained and applied to construct prediction models via eight machine-learning algorithms. The optimum ulstrasomics model was determined, followed by creating and evaluating a nomogram.
Ultrasomics features of 107 were extracted, and only those with coefficients greater than zero were retained. The XGboost ultrasomics model performed exceptionally well, achieving AUCs of 0.987 and 0.781 in the training and test cohorts, respectively. Furthermore, an effective nomogram was developed and visually represented. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curve (CIC) displayed in the ulstrasomics model and nomogram demonstrated high accuracy. They provided significant net benefits for clinical decision-making.
A novel ulstrasomics model and nomogram were created and certified to predict the pathological grading of PNETs using EUS images. This study has the potential to provide valuable insights that improve the clinical applicability and efficacy of EUS in predicting the grading of PNETs.
本研究旨在回顾性构建并验证使用内镜超声（EUS）图像的超声组学模型，以预测胰腺神经内分泌肿瘤（PNETs）的病理分级。
经病理检查确诊后，对79例患者进行回顾性分析，其中49例为1级PNETs患者，30例为2/3级PNETs患者。这些患者以6:4的比例随机分为训练组或测试组。采用最小绝对收缩和选择算子（LASSO）算法降低从标准EUS图像中提取的超声组学特征的维度。保留这些非零系数特征，并通过八种机器学习算法应用于构建预测模型。确定最佳超声组学模型，随后创建并评估列线图。
提取了107个超声组学特征，仅保留系数大于零的特征。XGboost超声组学模型表现出色，在训练组和测试组中的曲线下面积（AUC）分别达到0.987和0.781。此外，还开发了有效的列线图并进行了可视化展示。最后，超声组学模型和列线图中的校准曲线、决策曲线分析（DCA）图和临床影响曲线（CIC）显示出高准确性。它们为临床决策提供了显著的净效益。
创建并验证了一种新型超声组学模型和列线图，用于使用EUS图像预测PNETs的病理分级。本研究有可能提供有价值的见解，提高EUS在预测PNETs分级方面的临床适用性和有效性。
