PMID- 35574352
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 2234-943X (Print)
IS  - 2234-943X (Electronic)
IS  - 2234-943X (Linking)
VI  - 12
DP  - 2022
TI  - Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach 
      in Laryngeal Squamous Cell Carcinoma.
PG  - 823428
LID - 10.3389/fonc.2022.823428 [doi]
LID - 823428
AB  - OBJECTIVE: We aim to establish and validate computed tomography (CT)-based 
      radiomics model for predicting TP53 status in patients with laryngeal squamous 
      cell carcinoma (LSCC). METHODS: We divided all patients into a training set 1 
      (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to 
      predict TP53. Radiomics features were selected by analysis of variance (ANOVA) 
      and the least absolute shrinkage and selection operator (Lasso) regression 
      analysis. Five radiomics models were established by using K-Nearest Neighbor, 
      logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and 
      polynomial-SVM in training set 1. We also divided all patients into a training 
      set 2 and a testing set 2 according to different CT equipment to establish and 
      evaluate the stability of the radiomics models. RESULTS: After ANOVA and 
      subsequent Lasso regression analysis, 22 radiomics features were selected to 
      build the radiomics model in training set 1. The radiomics model based on 
      linear-SVM has the best predictive performance of the five models, and the area 
      under the receiver operating characteristic curve in training set 1 and testing 
      set 1 were 0.831(95% confidence interval [CI] 0.692-0.970) and 0.797(95% CI 
      0.632-0.957) respectively. The specificity, sensitivity, and accuracy were 
      0.971(95% CI 0.834-0.999), 0.714(95% CI 0.535-0.848), and 0.843(95% CI 
      0.657-0.928) in training set 1 and 0.750(95% CI 0.500-0.938), 0.786(95% CI 
      0.571-1.000), and 0.667(95% CI 0.467-0.720) in testing set 1, respectively. In 
      addition, the radiomics model also achieved stable prediction results even in 
      different CT equipment. Decision curve analysis showed that the radiomics model 
      for predicting TP53 status could benefit LSCC patients. CONCLUSION: We developed 
      and validated a relatively optimal radiomics model for TP53 status prediction by 
      trying five different machine learning methods in patients with LSCC. It shown 
      great potential of radiomics features for predicting TP53 status preoperatively 
      and guiding clinical treatment.
CI  - Copyright (c) 2022 Tian, Li, Jia, Mou, Zhang, Wu, Li, Yu, Mao and Song.
FAU - Tian, Ruxian
AU  - Tian R
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai 
      Yuhuangding Hospital of Qingdao University, Yantai, China.
FAU - Li, Yumei
AU  - Li Y
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai 
      Yuhuangding Hospital of Qingdao University, Yantai, China.
FAU - Jia, Chuanliang
AU  - Jia C
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai 
      Yuhuangding Hospital of Qingdao University, Yantai, China.
FAU - Mou, Yakui
AU  - Mou Y
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai 
      Yuhuangding Hospital of Qingdao University, Yantai, China.
FAU - Zhang, Haicheng
AU  - Zhang H
AD  - Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 
      China.
FAU - Wu, Xinxin
AU  - Wu X
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai 
      Yuhuangding Hospital of Qingdao University, Yantai, China.
FAU - Li, Jingjing
AU  - Li J
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai 
      Yuhuangding Hospital of Qingdao University, Yantai, China.
FAU - Yu, Guohua
AU  - Yu G
AD  - Department of Pathology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 
      China.
FAU - Mao, Ning
AU  - Mao N
AD  - Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 
      China.
FAU - Song, Xicheng
AU  - Song X
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, The Affiliated Yantai 
      Yuhuangding Hospital of Qingdao University, Yantai, China.
AD  - Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, 
      Yantai, China.
LA  - eng
PT  - Journal Article
DEP - 20220428
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC9095903
OTO - NOTNLM
OT  - TP53
OT  - computed tomography
OT  - laryngeal squamous cell carcinoma
OT  - machine learning
OT  - radiomics
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/05/17 06:00
MHDA- 2022/05/17 06:01
PMCR- 2022/01/01
CRDT- 2022/05/16 04:42
PHST- 2021/11/27 00:00 [received]
PHST- 2022/04/04 00:00 [accepted]
PHST- 2022/05/16 04:42 [entrez]
PHST- 2022/05/17 06:00 [pubmed]
PHST- 2022/05/17 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fonc.2022.823428 [doi]
PST - epublish
SO  - Front Oncol. 2022 Apr 28;12:823428. doi: 10.3389/fonc.2022.823428. eCollection 
      2022.