Applying a nomogram based on preoperative CT to predict early recurrence of laryngeal squamous cell carcinoma after surgery.

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
Yao Yao, Chuanliang Jia, Haicheng Zhang, Yakui Mou, Cai Wang, Xiao Han, Pengyi Yu, Ning Mao, Xicheng Song
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引用次数: 1

Abstract

Purpose: To identify the value of a computed tomography (CT)-based radiomics model to predict probability of early recurrence (ER) in patients diagnosed with laryngeal squamous cell carcinoma (LSCC) after surgery.

Materials and method: Pre-operative CT scans of 140 LSCC patients treated by surgery are reviewed and selected. These patients are randomly split into the training set (n = 97) and test set (n = 43). The regions of interest of each patient were delineated manually by two senior radiologists. Radiomics features are extracted from CT images acquired in non-enhanced, arterial, and venous phases. Variance threshold, one-way ANOVA, and least absolute shrinkage and selection operator algorithm are used for feature selection. Then, radiomics models are built with five algorithms namely, k-nearest neighbor (KNN), logistic regression (LR), linear support vector machine (LSVM), radial basis function SVM (RSVM), and polynomial SVM (PSVM). Clinical factors are selected using univariate and multivariate logistic regressions. Last, a radiomics nomogram incorporating the radiomics signature and clinical factors is built to predict ER and its efficiency is evaluated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) is also used to evaluate clinical usefulness.

Results: Four features are remarkably associated with ER in patients with LSCC. Applying to test set, the area under the ROC curves (AUCs) of KNN, LR, LSVM, RSVM, and PSVM are 0.936, 0.855, 0.845, 0.829, and 0.794, respectively. The radiomics nomogram shows better discrimination (with AUC: 0.939, 95% CI: 0.867-0.989) than the best radiomics model and the clinical model. Predicted and actual ERs in the calibration curves are in good agreement. DCA shows that the radiomics nomogram is clinically useful.

Conclusion: The radiomics nomogram, as a noninvasive prediction tool, exhibits favorable performance for ER prediction of LSCC patients after surgery.

应用基于术前CT的形态图预测喉鳞癌术后早期复发。
目的:探讨基于计算机断层扫描(CT)的放射组学模型在预测喉鳞癌(LSCC)术后早期复发(ER)概率中的价值。材料与方法:回顾并选择140例经手术治疗的LSCC患者的术前CT扫描。这些患者被随机分为训练集(n = 97)和测试集(n = 43)。每位患者感兴趣的区域由两位资深放射科医师手动划定。放射组学特征是从非增强期、动脉期和静脉期获得的CT图像中提取的。采用方差阈值、单因素方差分析、最小绝对收缩和选择算子算法进行特征选择。然后,利用k近邻(KNN)、逻辑回归(LR)、线性支持向量机(LSVM)、径向基函数支持向量机(RSVM)和多项式支持向量机(PSVM)五种算法构建放射组学模型。临床因素选择采用单因素和多因素logistic回归。最后,建立结合放射组学特征和临床因素的放射组学nomogram预测ER,并通过受试者工作特征(ROC)曲线和校准曲线评价其有效性。决策曲线分析(DCA)也用于评估临床有用性。结果:四个特征与LSCC患者的ER显著相关。应用于测试集,KNN、LR、LSVM、RSVM、PSVM的ROC曲线下面积(auc)分别为0.936、0.855、0.845、0.829、0.794。放射组学nomogram (AUC: 0.939, 95% CI: 0.867 ~ 0.989)优于最佳放射组学模型和临床模型。校正曲线上的预测电阻抗与实际电阻抗吻合较好。DCA显示放射组学图在临床上是有用的。结论:放射组学影像学作为一种无创预测工具,对LSCC术后ER预测有较好的效果。
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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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