PMID- 36561373
OWN - NLM
STAT- MEDLINE
DCOM- 20221226
LR  - 20230112
IS  - 2040-2309 (Electronic)
IS  - 2040-2295 (Print)
IS  - 2040-2295 (Linking)
VI  - 2022
DP  - 2022
TI  - Development and Validation of the Random Forest Model via Combining CT-PET Image 
      Features and Demographic Data for Distant Metastases among Lung Cancer Patients.
PG  - 7793533
LID - 10.1155/2022/7793533 [doi]
LID - 7793533
AB  - The work aimed at developing and validating a random forest model of CT-PET image 
      features combined with demographic data to diagnose distant metastases among lung 
      cancer patients. This study involved lung cancer patients from The Cancer Genome 
      Atlas lung adenocarcinoma (TCGA-LUAD) dataset, the lung PET-CT dataset, the lung 
      squamous cell carcinoma (LSCC) dataset, and the National Cancer Institute's 
      Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma (CPTAC-LUAD) 
      dataset and collected the information on 178 CT, 178 PET, and the patients' age, 
      history of smoking, and gender. We conducted image processing and feature 
      extraction. Finally, 4 computed tomography (CT) image features and 2 positron 
      emission tomography (PET) image features were extracted. Four prediction models 
      based on CT image features, PET image features, and demographic data were 
      developed, and the area under the receiver operating characteristic (ROC) curve 
      was used to evaluate the performance of prediction models. A total of 178 
      eligible samples were randomly divided into a training set (n = 134) and a 
      testing set (n = 44) at a ratio of 3 : 1, with 2021 as a random number. ROC 
      analyses illustrated that the predictive performance for distant metastases of 
      combining CT-PET image features and demographic data for training and testing 
      were 0.923 (95% confidence interval (CI): 0.873-0.973) and 0.873 (95% CI: 
      0.757-0.990). In addition, the predictive performance of the combined model in 
      the testing set was significantly better than that of the CT-demographic data 
      model (0.716, 95% CI: 0.531-0.902), PET-demographic data model (0.802, 95% CI: 
      0.633-0.970), and CT-PET model (0.797, 95% CI: 0.666-0.928). The random forest 
      model via combining CT-PET image features and demographic data could have great 
      performance in predicting distant metastases among lung cancer patients.
CI  - Copyright (c) 2022 Lijun Bi and Yi Guo.
FAU - Bi, Lijun
AU  - Bi L
AD  - Department of Electronic Information, Shandong University of Science and 
      Technology (SDUST), Qingdao 266590, Shandong, China.
FAU - Guo, Yi
AU  - Guo Y
AUID- ORCID: 0000-0003-4940-8616
AD  - Department of Radiology, Changzhou No. 2 People's Hospital, Changzhou 213000, 
      Jiangsu, China.
LA  - eng
PT  - Journal Article
DEP - 20221213
PL  - England
TA  - J Healthc Eng
JT  - Journal of healthcare engineering
JID - 101528166
SB  - IM
MH  - Humans
MH  - Positron Emission Tomography Computed Tomography/methods
MH  - Proteomics
MH  - Random Forest
MH  - *Lung Neoplasms/diagnostic imaging
MH  - Tomography, X-Ray Computed
MH  - Positron-Emission Tomography
MH  - *Adenocarcinoma of Lung
MH  - Demography
PMC - PMC9767733
COIS- The authors declare that there are no conflicts of interest regarding the 
      publication of this article.
EDAT- 2022/12/24 06:00
MHDA- 2022/12/27 06:00
PMCR- 2022/12/13
CRDT- 2022/12/23 02:24
PHST- 2022/06/28 00:00 [received]
PHST- 2022/11/18 00:00 [revised]
PHST- 2022/11/26 00:00 [accepted]
PHST- 2022/12/23 02:24 [entrez]
PHST- 2022/12/24 06:00 [pubmed]
PHST- 2022/12/27 06:00 [medline]
PHST- 2022/12/13 00:00 [pmc-release]
AID - 10.1155/2022/7793533 [doi]
PST - epublish
SO  - J Healthc Eng. 2022 Dec 13;2022:7793533. doi: 10.1155/2022/7793533. eCollection 
      2022.