Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong 250012, China.
Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China.
Biomed Res Int. 2020 Aug 8;2020:4340521. doi: 10.1155/2020/4340521. eCollection 2020.
In the clinical management of hypopharyngeal squamous cell carcinoma (HSCC), preoperative identification of early recurrence (≤2 years) after curative resection is essential. Thus, we aimed to develop a CT-based radiomic signature to predict early recurrence in HSCC patients preoperatively.
In total, 167 HSCC patients who underwent partial surgery were enrolled in this retrospective study and divided into two groups, i.e., the training cohort ( = 133) and the validation cohort ( = 34). Each individual was followed up for at least for 2 years. Radiomic features were extracted from CT images, and the radiomic signature was built with the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model. The associations of preoperative clinical factors with early recurrence were evaluated. A radiomic signature-combined model was built, and the area under the curve (AUC) was used to explore their performance in discriminating early recurrence.
Among the 1415 features, 335 of them were selected using the variance threshold method. Then, the SelectKBest method was further used for the selection of 31 candidate features. Finally, 11 out of 31 optimal features were identified with the LASSO algorithm. In the LR classifier, the AUCs of the training and validation sets in discriminating early recurrence were 0.83 (95% CI: 0.76-0.90) (sensitivity 0.8 and specificity 0.83) and 0.83 (95% CI: 0.67-0.99) (sensitivity 0.69 and specificity 0.71), respectively.
Using the radiomic signature, we developed a radiomic signature to preoperatively predict early recurrence in patients with HSCC, which may serve as a potential noninvasive tool to guide personalized treatment.
在喉下咽鳞状细胞癌（HSCC）的临床治疗中，术前识别根治性切除术后的早期复发（≤2 年）至关重要。因此，我们旨在开发一种基于 CT 的放射组学特征，以预测 HSCC 患者术前的早期复发。
本回顾性研究共纳入 167 例接受部分手术的 HSCC 患者，分为训练队列（n=133）和验证队列（n=34）。每位患者的随访时间至少为 2 年。从 CT 图像中提取放射组学特征，然后使用最小绝对收缩和选择算子（LASSO）逻辑回归（LR）模型构建放射组学特征。评估术前临床因素与早期复发的相关性。构建放射组学特征-联合模型，并使用曲线下面积（AUC）评估其在鉴别早期复发中的性能。
在 1415 个特征中，使用方差阈值法选择了 335 个特征。然后，进一步使用 SelectKBest 方法选择 31 个候选特征。最后，LASSO 算法确定了 31 个最优特征中的 11 个。在 LR 分类器中，训练集和验证集在鉴别早期复发方面的 AUC 分别为 0.83（95%CI：0.76-0.90）（敏感性为 0.8，特异性为 0.83）和 0.83（95%CI：0.67-0.99）（敏感性为 0.69，特异性为 0.71）。
使用放射组学特征，我们开发了一种放射组学特征，用于术前预测 HSCC 患者的早期复发，这可能成为指导个性化治疗的一种潜在的非侵入性工具。
