Liu Shihe, Liu Shunli, Zhang Chuanyu, Yu Hualong, Liu Xuejun, Hu Yabin, Xu Wenjian, Tang Xiaoyan, Fu Qing
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China.
Front Oncol. 2020 Sep 4;10:1268. doi: 10.3389/fonc.2020.01268. eCollection 2020.
Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study, we investigated the association between radiomics features and the tumor histological subtypes, and we aimed to establish a nomogram for the classification of small cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This was a retrospective single center study. In total, 468 cases including 202 patients with SCLC and 266 patients with NSCLC were enrolled in our study, and were randomly divided into a training set ( = 327) and a validation set ( = 141) in a 7:3 ratio. The clinical data of the patients, including age, sex, smoking history, tumor maximum diameter, clinical stage, and serum tumor markers, were collected. All patients underwent enhanced computed tomography (CT) scans, and all lesions were pathologically confirmed. A radiomics signature was generated from the training set using the least absolute shrinkage and selection operator algorithm. Independent risk factors were identified by multivariate logistic regression analysis, and a radiomics nomogram based on the radiomics signature and clinical features was constructed. The capability of the nomogram was evaluated in the training set and validated in the validation set. Fourteen of 396 radiomics parameters were screened as important factors for establishing the radiomics model. The radiomics signature performed well in differentiating SCLC and NSCLC, with an area under the curve (AUC) of 0.86 (95% CI: 0.82-0.90) in the training set and 0.82 (95% CI: 0.75-0.89) in the validation set. The radiomics nomogram had better predictive performance [AUC = 0.94 (95% CI: 0.90-0.98) in the validation set] than the clinical model [AUC = 0.86 (95% CI: 0.80-0.93)] and the radiomics signature [AUC = 0.82 (95% CI: 0.75-0.89)], and the accuracy was 86.2% (95% CI: 0.79-0.92) in the validation set. The enhanced CT radiomics signature performed well in the classification of SCLC and NSCLC. The nomogram based on the radiomics signature and clinical factors has better diagnostic performance for the classification of SCLC and NSCLC than the simple application of the radiomics signature.
放射组学可通过将特征算法应用于医学影像数据来无创地量化肿瘤表型特征。在本研究中，我们调查了放射组学特征与肿瘤组织学亚型之间的关联，并旨在建立一个用于小细胞肺癌（SCLC）和非小细胞肺癌（NSCLC）分类的列线图。这是一项回顾性单中心研究。我们的研究共纳入了468例患者，其中包括202例SCLC患者和266例NSCLC患者，并按照7:3的比例随机分为训练集（=327）和验证集（=141）。收集了患者的临床数据，包括年龄、性别、吸烟史、肿瘤最大直径、临床分期和血清肿瘤标志物。所有患者均接受了增强计算机断层扫描（CT），且所有病变均经病理证实。使用最小绝对收缩和选择算子算法从训练集中生成放射组学特征。通过多因素逻辑回归分析确定独立危险因素，并构建基于放射组学特征和临床特征的放射组学列线图。在训练集中评估列线图的性能，并在验证集中进行验证。396个放射组学参数中的14个被筛选为建立放射组学模型的重要因素。放射组学特征在区分SCLC和NSCLC方面表现良好，在训练集中曲线下面积（AUC）为0.86（95%CI：0.82 - 0.90），在验证集中为0.82（95%CI：0.75 - 0.89）。放射组学列线图比临床模型[AUC = 0.86（95%CI：0.80 - 0.93）]和放射组学特征[AUC = 0.82（95%CI：0.75 - 0.89）]具有更好的预测性能[在验证集中AUC = 0.94（95%CI：0.90 - 0.98）]，在验证集中准确率为86.2%（95%CI：0.79 - 0.92）。增强CT放射组学特征在SCLC和NSCLC的分类中表现良好。基于放射组学特征和临床因素的列线图在SCLC和NSCLC分类方面比单纯应用放射组学特征具有更好的诊断性能。
