Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes
Abstract
:1. Introduction
2. Results
2.1. Gene Modulation and Hub Gene Assay
2.2. Hub Gene and Image Feature Associations
2.3. Creation of the Prediction Model
3. Discussion
4. Materials and Methods
4.1. NSCLC NGS Data Processing
4.2. Weighted Gene Co-Expression Networks and Modules Associated with Clinical Traits
4.3. Hub Gene Analysis
4.4. 18F-FDG PET Imaging
4.5. Hub Gene and Image Feature Correlation
4.6. RF Prediction Model Construction
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Characteristic | Result | Rate |
---|---|---|
Average age | 69.28 | |
Sex | ||
Male | 44 | 83 |
Female | 9 | 17 |
Histology | ||
Adenocarcinoma | 36 | 68 |
Non-small cell lung cancer (not otherwise specified) | 2 | 4 |
Squamous cell carcinoma | 15 | 28 |
Smoking status | ||
Current | 8 | 15 |
Former | 37 | 70 |
Non-smoker | 8 | 15 |
EGFR mutation | ||
Wild | 38 | 72 |
Mutation | 6 | 11 |
Unknown | 9 | 17 |
KRAS mutation | ||
Wild | 32 | 60 |
Mutation | 12 | 23 |
Unknown | 9 | 17 |
T stage | ||
T1a | 10 | 19 |
T1b | 13 | 25 |
T2a | 17 | 32 |
T2b | 5 | 9 |
T3 | 5 | 9 |
T4 | 2 | 4 |
Tis | 1 | 2 |
N stage | ||
N0 | 41 | 77 |
N1 | 5 | 9 |
N2 | 7 | 13 |
Recurrence | ||
No recurrence | 34 | 64 |
Distant recurrence | 19 | 36 |
Random Forest | Image Texture Features | Hub Genes | Correlation of Genes and Image Texture | Four Image Texture Features |
---|---|---|---|---|
Precision | 0.692 | 0.8 | 0.802 | 0.832 |
Recall | 0.733 | 0.783 | 0.792 | 0.75 |
AUC | 0.729 | 0.808 | 0.912 | 0.779 |
Accuracy | 0.59 | 0.767 | 0.783 | 0.738 |
Feature Family | Features |
---|---|
Intensity histogram | Maximum standard uptake value (SUVmax) |
Mean standard uptake value (SUVmean) | |
Standard deviation (SUV_SD) | |
Total lesion glycolysis (TLG) | |
Metabolic tumor volume (MTV) | |
1st entropy | |
Gray-level co-occurrence matrix (GLCM) | Energy |
Contrast | |
Entropy | |
Homogeneity | |
Dissimilarity | |
Neighboring gray-level dependence matrix (NGLDM) | Contrast |
Coarseness | |
Busyness | |
Small number emphasis (SNE) | |
Gray-level run length matrix (GLRLM) | Short run emphasis (SRE) |
Long run emphasis (LRE) | |
Gray-level non-uniformity (GLNU) | |
Run length non-uniformity (RLNU) | |
Low gray-level run emphasis (SRLGE) | |
High gray-level run emphasis (SGHGE) | |
Gray-level size zone matrix (GLSZM) | Small zone emphasis (SAE) |
Large zone emphasis (LAE) | |
Gray-level non-uniformity (GLN) | |
Zone size non-uniformity (SZN) | |
Low gray-level zone emphasis (LGLZE) | |
High gray level zone emphasis (HGLZE) |
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Ju, H.M.; Kim, B.-C.; Lim, I.; Byun, B.H.; Woo, S.-K. Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes. Int. J. Mol. Sci. 2023, 24, 2794. https://doi.org/10.3390/ijms24032794
Ju HM, Kim B-C, Lim I, Byun BH, Woo S-K. Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes. International Journal of Molecular Sciences. 2023; 24(3):2794. https://doi.org/10.3390/ijms24032794
Chicago/Turabian StyleJu, Hye Min, Byung-Chul Kim, Ilhan Lim, Byung Hyun Byun, and Sang-Keun Woo. 2023. "Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes" International Journal of Molecular Sciences 24, no. 3: 2794. https://doi.org/10.3390/ijms24032794