Zhao Zhen, Xiao Dongdong, Nie Chuansheng, Zhang Hao, Jiang Xiaobing, Jecha Ali Rajab, Yan Pengfei, Zhao Hongyang
Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Geriatric Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2021 Jul 9;11:709321. doi: 10.3389/fonc.2021.709321. eCollection 2021.
Given the similarities in clinical manifestations of cystic-solid pituitary adenomas (CS-PAs) and craniopharyngiomas (CPs), this study aims to establish and validate a nomogram based on preoperative imaging features and blood indices to differentiate between CS-PAs and CPs.
A departmental database was searched to identify patients who had undergone tumor resection between January 2012 and December 2020, and those diagnosed with CS-PAs or CPs by histopathology were included. Preoperative magnetic resonance imaging (MRI) features as well as blood indices were retrieved and analyzed. Radiological features were extracted from the tumor on contrast-enhanced T1 (CE-T1) weighted and T2 weighted sequences. The two independent samples -test and principal component analysis (PCA) were used for feature selection, data dimension reduction, and radiomics signature building. Next, the radiomics signature was put in five classification models for exploring the best classifier with superior identification performance. Multivariate logistic regression analysis was then used to establish a radiomic-clinical model containing radiomics and hematological features, and the model was presented as a nomogram. The performance of the radiomics-clinical model was assessed by calibration curve, clinical effectiveness as well as internal validation.
A total of 272 patients were included in this study: 201 with CS-PAs and 71 with CPs. These patients were randomized into training set (n=182) and test set (n=90). The radiomics signature, which consisted of 18 features after dimensionality reduction, showed superior discrimination performance in 5 different classification models. The area under the curve (AUC) values of the training set and the test set obtained by the radiomics signature are 0.92 and 0.88 in the logistic regression model, 0.90 and 0.85 in the Ridge classifier, 0.88 and 0.82 in the stochastic gradient descent (SGD) classifier, 0.78 and 0.85 in the linear support vector classification (Linear SVC), 0.93 and 0.86 in the multilayers perceptron (MLP) classifier, respectively. The predictive factors of the nomogram included radiomic signature, age, WBC count, and FIB. The nomogram showed good discrimination performance (with an AUC of 0.93 in the training set and 0.90 in the test set) and good calibration. Moreover, decision curve analysis (DCA) demonstrated satisfactory clinical effectiveness of the proposed radiomic-clinical nomogram.
A personalized nomogram containing radiomics signature and blood indices was proposed in this study. This nomogram is simple yet effective in differentiating between CS-PAs and CPs and thus can be used in routine clinical practice.
鉴于囊性实性垂体腺瘤（CS-PAs）和颅咽管瘤（CPs）临床表现相似，本研究旨在建立并验证一种基于术前影像特征和血液指标的列线图，以区分CS-PAs和CPs。
检索科室数据库，以识别2012年1月至2020年12月期间接受肿瘤切除术的患者，纳入经组织病理学诊断为CS-PAs或CPs的患者。检索并分析术前磁共振成像（MRI）特征以及血液指标。从肿瘤的对比增强T1（CE-T1）加权和T2加权序列中提取放射学特征。使用两独立样本t检验和主成分分析（PCA）进行特征选择、数据降维和构建放射组学特征。接下来，将放射组学特征纳入五个分类模型，以探索具有卓越识别性能的最佳分类器。然后使用多因素逻辑回归分析建立包含放射组学和血液学特征的放射组学-临床模型，并将该模型呈现为列线图。通过校准曲线、临床有效性以及内部验证评估放射组学-临床模型的性能。
本研究共纳入272例患者：201例为CS-PAs，71例为CPs。这些患者被随机分为训练集（n = 182）和测试集（n = 90）。放射组学特征在降维后由18个特征组成，在5种不同分类模型中显示出卓越的鉴别性能。在逻辑回归模型中，放射组学特征在训练集和测试集获得的曲线下面积（AUC）值分别为0.92和0.88；在岭回归分类器中分别为0.90和0.85；在随机梯度下降（SGD）分类器中分别为0.88和0.82；在线性支持向量分类（Linear SVC）中分别为0.78和0.85；在多层感知器（MLP）分类器中分别为0.93和0.86。列线图的预测因素包括放射组学特征、年龄白细胞计数和纤维蛋白原。列线图显示出良好的鉴别性能（训练集AUC为0.93，测试集为0.90）和良好的校准。此外，决策曲线分析（DCA）表明所提出的放射组学-临床列线图具有令人满意的临床有效性。
本研究提出了一种包含放射组学特征和血液指标的个性化列线图。该列线图在区分CS-PAs和CPs方面简单有效，因此可用于常规临床实践。
