PMID- 35814386
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  - Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With 
      Colorectal Cancer by Radiomics Models Based on Multiphasic CT.
PG  - 848798
LID - 10.3389/fonc.2022.848798 [doi]
LID - 848798
AB  - OBJECTIVE: To develop and validate radiomics models based on multiphasic CT in 
      predicting Kirsten rat sarcoma virus (KRAS) gene mutation status in patients with 
      colorectal cancer (CRC). MATERIALS AND METHODS: A total of 231 patients with 
      pathologically confirmed CRC were retrospectively enrolled and randomly divided 
      into training(n=184) and test groups(n=47) in a ratio of 4:1. A total of 1316 
      quantitative radiomics features were extracted from non-contrast phase (NCP), 
      arterial-phase (AP) and venous-phase (VP) CT for each patient. Four steps were 
      applied for feature selection including Spearman correlation analysis, variance 
      threshold, least absolute contraction and selection operator, and multivariate 
      stepwise regression analysis. Clinical and pathological characteristics were also 
      assessed. Subsequently, three classification methods, logistic regression (LR), 
      support vector machine (SVM) and random tree (RT) algorithm, were applied to 
      develop seven groups of prediction models (NCP, AP, VP, AP+VP, AP+VP+NCP, AP&VP, 
      AP&VP&NCP) for KRAS mutation prediction. The performance of these models was 
      evaluated by receiver operating characteristics curve (ROC) analysis. RESULTS: 
      Among the three groups of single-phase models, the AP model, developed by LR 
      algorithm, showed the best prediction performance with an AUC value of 0.811 (95% 
      CI:0.685-0.938) in the test cohort. Compared with the single-phase models, the 
      dual-phase (AP+VP) model with the LR algorithm showed better prediction 
      performance (AUC=0.826, 95% CI:0.700-0.952). The performance of multiphasic 
      (AP+VP+NCP) model with the LR algorithm (AUC=0.811, 95%CI: 0.679-0.944) is 
      comparable to the model with the SVM algorithm (AUC=0.811, 95%CI: 0.695-0.918) in 
      the test cohort, but the sensitivity, specificity, and accuracy of the 
      multiphasic (AP+VP+NCP) model with the LR algorithm were 0.810, 0.808, 0.809 
      respectively, which were highest among these seven groups of prediction models in 
      the test cohort. CONCLUSION: The CT radiomics models have the potential to 
      predict KRAS mutation in patients with CRC; different phases may affect the 
      predictive efficacy of radiomics model, of which arterial-phase CT is more 
      informative. The combination of multiphasic CT images can further improve the 
      performance of radiomics model.
CI  - Copyright (c) 2022 Hu, Xia, Wang, Peng, Liu, Xie, Liao, Wan and Li.
FAU - Hu, Jianfeng
AU  - Hu J
AD  - Department of Radiology, The First Affiliated Hospital of Guangzhou Medical 
      University, Guangzhou, China.
FAU - Xia, Xiaoying
AU  - Xia X
AD  - Department of Radiology, The First Affiliated Hospital of Guangzhou Medical 
      University, Guangzhou, China.
FAU - Wang, Peng
AU  - Wang P
AD  - Department of Radiology, The First Affiliated Hospital of Guangzhou Medical 
      University, Guangzhou, China.
FAU - Peng, Yu
AU  - Peng Y
AD  - Department of Radiology, The First Affiliated Hospital of Guangzhou Medical 
      University, Guangzhou, China.
FAU - Liu, Jieqiong
AU  - Liu J
AD  - Department of Radiology, The First Affiliated Hospital of Guangzhou Medical 
      University, Guangzhou, China.
FAU - Xie, Xiaobin
AU  - Xie X
AD  - Department of Radiology, The First Affiliated Hospital of Guangzhou Medical 
      University, Guangzhou, China.
FAU - Liao, Yuting
AU  - Liao Y
AD  - Department of Pharmaceutical Diagnostics, GE Healthcare, Shanghai, China.
FAU - Wan, Qi
AU  - Wan Q
AD  - Department of Radiology, The First Affiliated Hospital of Guangzhou Medical 
      University, Guangzhou, China.
FAU - Li, Xinchun
AU  - Li X
AD  - Department of Radiology, The First Affiliated Hospital of Guangzhou Medical 
      University, Guangzhou, China.
LA  - eng
PT  - Journal Article
DEP - 20220624
PL  - Switzerland
TA  - Front Oncol
JT  - Frontiers in oncology
JID - 101568867
PMC - PMC9263192
OTO - NOTNLM
OT  - Kirsten rat sarcoma virus
OT  - colorectal cancer
OT  - computed tomography
OT  - mutation
OT  - radiomics
COIS- Author YL was employed by GE Healthcare, Shanghai, China. The remaining 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- 2022/07/12 06:00
MHDA- 2022/07/12 06:01
PMCR- 2022/01/01
CRDT- 2022/07/11 04:16
PHST- 2022/01/05 00:00 [received]
PHST- 2022/05/25 00:00 [accepted]
PHST- 2022/07/11 04:16 [entrez]
PHST- 2022/07/12 06:00 [pubmed]
PHST- 2022/07/12 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fonc.2022.848798 [doi]
PST - epublish
SO  - Front Oncol. 2022 Jun 24;12:848798. doi: 10.3389/fonc.2022.848798. eCollection 
      2022.