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

Article type: Research Article

Authors: Yao, Yao^{a; b; 1} | Jia, Chuanliang^{a; b; c} | Zhang, Haicheng^{c; d; 1} | Mou, Yakui^{a; b} | Wang, Cai^{a; b} | Han, Xiao^{a; b} | Yu, Pengyi^{a; b} | Mao, Ning^{c; d; *} | Song, Xicheng^{a; b; *}

Affiliations: [a] Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China | [b] Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, China | [c] Big data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China | [d] Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China

Correspondence: [*] Corresponding author: Xicheng Song, Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, China. E-mail: [email protected] (XS) and Ning Mao, Big data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, China. E-mail: [email protected] (NM).

Note: [1] Yao Yao, Chuanliang Jia and Haicheng Zhang are co-first authors on the paper.

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.

Keywords: Laryngeal squamous cell carcinoma, nomogram, early recurrence, radiomics

DOI: 10.3233/XST-221320

Journal: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 435-452, 2023