PMID- 36806538
OWN - NLM
STAT- MEDLINE
DCOM- 20230518
LR  - 20230518
IS  - 1095-9114 (Electronic)
IS  - 0895-3996 (Linking)
VI  - 31
IP  - 3
DP  - 2023
TI  - Applying a nomogram based on preoperative CT to predict early recurrence of 
      laryngeal squamous cell carcinoma after surgery.
PG  - 435-452
LID - 10.3233/XST-221320 [doi]
AB  - 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.
FAU - Yao, Yao
AU  - Yao Y
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding 
      Hospital, Qingdao University, Yantai, Shandong, China.
AD  - Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, 
      Yantai, Shandong, China.
FAU - Jia, Chuanliang
AU  - Jia C
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding 
      Hospital, Qingdao University, Yantai, Shandong, China.
AD  - Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, 
      Yantai, Shandong, China.
AD  - Big data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, 
      Qingdao University, Yantai, Shandong, China.
FAU - Zhang, Haicheng
AU  - Zhang H
AD  - Big data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, 
      Qingdao University, Yantai, Shandong, China.
AD  - Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 
      Shandong, China.
FAU - Mou, Yakui
AU  - Mou Y
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding 
      Hospital, Qingdao University, Yantai, Shandong, China.
AD  - Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, 
      Yantai, Shandong, China.
FAU - Wang, Cai
AU  - Wang C
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding 
      Hospital, Qingdao University, Yantai, Shandong, China.
AD  - Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, 
      Yantai, Shandong, China.
FAU - Han, Xiao
AU  - Han X
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding 
      Hospital, Qingdao University, Yantai, Shandong, China.
AD  - Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, 
      Yantai, Shandong, China.
FAU - Yu, Pengyi
AU  - Yu P
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding 
      Hospital, Qingdao University, Yantai, Shandong, China.
AD  - Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, 
      Yantai, Shandong, China.
FAU - Mao, Ning
AU  - Mao N
AD  - Big data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, 
      Qingdao University, Yantai, Shandong, China.
AD  - Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 
      Shandong, China.
FAU - Song, Xicheng
AU  - Song X
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding 
      Hospital, Qingdao University, Yantai, Shandong, China.
AD  - Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, 
      Yantai, Shandong, China.
LA  - eng
PT  - Journal Article
PT  - Randomized Controlled Trial
PT  - Research Support, Non-U.S. Gov't
PL  - Netherlands
TA  - J Xray Sci Technol
JT  - Journal of X-ray science and technology
JID - 9000080
SB  - IM
MH  - Humans
MH  - *Head and Neck Neoplasms
MH  - *Nomograms
MH  - Retrospective Studies
MH  - ROC Curve
MH  - Squamous Cell Carcinoma of Head and Neck/diagnostic imaging/surgery
MH  - Tomography, X-Ray Computed/methods
OTO - NOTNLM
OT  - Laryngeal squamous cell carcinoma
OT  - early recurrence
OT  - nomogram
OT  - radiomics
EDAT- 2023/02/23 06:00
MHDA- 2023/05/16 06:42
CRDT- 2023/02/22 10:46
PHST- 2023/05/16 06:42 [medline]
PHST- 2023/02/23 06:00 [pubmed]
PHST- 2023/02/22 10:46 [entrez]
AID - XST221320 [pii]
AID - 10.3233/XST-221320 [doi]
PST - ppublish
SO  - J Xray Sci Technol. 2023;31(3):435-452. doi: 10.3233/XST-221320.