Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patient Selection
2.2. Image Acquisition and Imaging Texture Analysis
2.3. Radiomics Workflow and Feature Extraction
2.4. Tumor Microenvironment-Based RNA Immune Response Gene Sequencing
2.5. Statistical Analysis for Clinical Data
2.6. CATCH Model
3. Results
3.1. Patient Characteristics
3.2. Identification of Genes Influencing Recurrence and Performance of the CATCH Model
3.3. Adjusted Radiomic Features Obtained from CATCH Model for Cancer Recurrence
3.4. Adjusted Radiomic Features Impact Clinical Outcome
3.5. Correlation between Adjusted Radiomic Features and Immune Gene Expression
3.6. PECAM1 as a Therapeutic Target Identified by Adjusted Radiomic Features in Recurrent Colorectal Cancer
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AUC | area under the curve |
CATCH | covariate-adjusted tensor classification in the high-dimension |
CRC | colorectal cancer |
CT | computed tomography |
CRC | colorectal cancer |
DEGs | differentially expressed genes |
DICOM | Digital Imaging and Communications in Medicine |
DV | dependence variance |
FOLFOX | leucovorin (folinic acid), fluorouracil, and oxaliplatin |
IV | inverse variance |
LDA | linear discriminant analysis |
LGLZE | low gray level zone emphasis |
mFOLFOX7 | modified FOLFOX |
MMR | mismatch repair |
NCCN | National Comprehensive Cancer Network |
NCKUH | National Cheng Kung University Hospital |
PACS | picture archiving and communication system |
RF | random forest |
RFS | recurrence-free survival |
TME | tumor microenvironment |
VOI | volume of interest |
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Methods | Accuracy | Sensitivity | Specificity | F1 score | AUC |
---|---|---|---|---|---|
Random forest | 0.68 | 0.16 | 0.83 | 0.24 | 0.46 |
LDA | 0.64 | 0.32 | 0.78 | 0.35 | 0.55 |
CATCH | 0.60 | 0.66 | 0.48 | 0.69 | 0.56 |
Features | Coefficient |
---|---|
wavelet LHH_glcm_Idmn | 6.57 |
wavelet LHH_glcm_Idn | 4.45 |
wavelet LLH_glcm_Idn | 0.69 |
wavelet LHL_glcm_InverseVariance (IV) | 0.07 |
wavelet HHH_gldm_DependenceVariance (DV) | 0.06 |
wavelet LHH_glszm_GrayLevelNonUniformityNormalized (GLNN) | −0.11 |
wavelet LHH_gldm_LowGrayLevelEmphasis (LGLE) | −0.20 |
wavelet LHH_glrlm_LowGrayLevelRunEmphasis (LGLRE) | −0.73 |
wavelet LHH_ngtdm_Contrast | −5.22 |
wavelet LHH_glszm_LowGrayLevelZoneEmphasis (LGLZE) | −5.71 |
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Huang, Y.-C.; Tsai, Y.-S.; Li, C.-I.; Chan, R.-H.; Yeh, Y.-M.; Chen, P.-C.; Shen, M.-R.; Lin, P.-C. Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer. Cancers 2022, 14, 1895. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14081895
Huang Y-C, Tsai Y-S, Li C-I, Chan R-H, Yeh Y-M, Chen P-C, Shen M-R, Lin P-C. Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer. Cancers. 2022; 14(8):1895. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14081895
Chicago/Turabian StyleHuang, Yi-Ching, Yi-Shan Tsai, Chung-I Li, Ren-Hao Chan, Yu-Min Yeh, Po-Chuan Chen, Meng-Ru Shen, and Peng-Chan Lin. 2022. "Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer" Cancers 14, no. 8: 1895. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14081895