A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Subjects and Clinical Data
2.2. Tumor Mutational Burden
2.3. FDG PET/CT Imaging
2.4. PET Radiomic Features
2.5. Machine Learning Approach
2.6. Statistical Analysis
3. Results
3.1. Patients
3.2. PET-Based Radiomics and Tumor Mutation Burden
3.3. Prognostic Validation of PET Radiomic Score
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristics | Overall | Low TMB | High TMB | p |
---|---|---|---|---|
(n = 91) | (n = 70) | (n = 21) | ||
Age (range, years) | 58.0 (21–88) | 56.8 (33–88) | 62.1 (21–84) | 0.154 |
Sex, male | 53 (57.6%) | 38 (54.3%) | 15 (71.4%) | 0.162 |
TMB | 9.4 ± 12.1 | 5.7 ± 2.3 | 22.0 ± 2.3 | 0.002 |
Histological type | 0.570 | |||
Adenocarcinoma | 88 (96.7%) | 68 (97.1%) | 20 (95.2%) | |
Small cell carcinoma | 1 (1.1%) | 1 (1.4%) | 0 (0%) | |
Unclassified carcinoma | 2 (2.2%) | 1 (1.4%) | 1 (4.8%) | |
Histological grade | 0.430 | |||
Unknown | 14 (15.2%) | 9 (12.9%) | 5 (23.8%) | |
Well-differentiated | 12 (13.0%) | 11 (15.7%) | 1 (4.8%) | |
Moderately differentiated | 53 (57.6) | 41 (58.6%) | 12 (57.1%) | |
Poorly differentiated | 12 (13.0%) | 9 (12.9%) | 3 (14.3%) | |
Location | 0.012 | |||
Colon | 39 (42.9%) | 25 (35.7%) | 14 (66.7%) | |
Rectum | 52 (57.1%) | 45 (64.3%) | 7 (33.3%) | |
Chemotherapy regimen | 0.760 | |||
FOLFOX | 58 (63.7%) | 46 (65.7%) | 12 (57.1%) | |
FOLFIRI | 25 (27.5%) | 18 (25.7%) | 6 (28.6%) | |
Others | 6 (6.6%) | 5 (7.1%) | 2 (9.5%) | |
None | 2 (2.2%) | 1 (1.4%) | 1 (4.8%) | |
Palliative surgery | 0.157 | |||
Yes | 44 (48.4%) | 31 (44.3%) | 13 (61.9%) | |
No | 47 (51.6%) | 39 (55.7%) | 8 (38.1%) |
Features | Log (OR) | p | FDR |
---|---|---|---|
Surface-to-volume ratio | −13.33 | 0.006 | 0.036 |
Total lesion glycolysis | 2.02 × 10−6 | 0.007 | 0.036 |
Volume | 1.86 × 10−5 | 0.010 | 0.036 |
Area | 9.93 × 10−5 | 0.012 | 0.036 |
Compacity | 0.58 | 0.014 | 0.036 |
Complexity | 8.48 × 10−6 | 0.004 | 0.019 |
Entropy | 0.627 | 0.011 | 0.051 |
Correlation | 6.20 | 0.006 | 0.051 |
Coarseness | −33.64 | 0.037 | 0.092 |
Zone size non-uniformity | 0.003 | 0.002 | 0.037 |
Models | AUC | Accuracy | Sensitivity | Specificity | F1 Score | Precision |
---|---|---|---|---|---|---|
GLM | 0.610 | 0.769 | 0.476 | 0.814 | 0.455 | 0.435 |
LDA | 0.691 | 0.802 | 0.762 | 0.571 | 0.478 | 0.348 |
QDA | 0.580 | 0.747 | 0.381 | 0.857 | 0.410 | 0.444 |
KNN | 0.791 | 0.814 | 0.619 | 0.871 | 0.605 | 0.591 |
SVM | 0.579 | 0.758 | 0.333 | 0.914 | 0.412 | 0.538 |
RF | 0.781 | 0.836 | 0.667 | 0.814 | 0.583 | 0.519 |
NN | 0.671 | 0.780 | 0.476 | 0.900 | 0.526 | 0.588 |
Variables | Univariable | Multivariable | ||||
---|---|---|---|---|---|---|
HR | 95% CI | p | HR | 95% CI | p | |
Sex | ||||||
Female vs. Male | 0.877 | 0.295–2.610 | 0.813 | 1.422 | 0.403–5.020 | 0.584 |
Age | ||||||
≥56 | 1.097 | 0.378–3.185 | 0.865 | 0.407 | 0.106–1.557 | 0.189 |
Location | ||||||
Colon vs. Rectum | 1.023 | 0.352–2.974 | 0.966 | 1.050 | 0.282–3.904 | 0.942 |
Histological grade | ||||||
Well-differentiated | ||||||
Moderately differentiated | 0.732 | 0.083–6.434 | 0.779 | 0 | 0.998 | |
Poorly differentiated | 1.947 | 0.212–17.886 | 0.556 | 5.389 | 0.424–68.475 | 0.194 |
Chemotherapy regimen | ||||||
FOLFOX | ||||||
FOLFIRI | 0.324 | 0.067–1.561 | 0.160 | 0.315 | 0.056–1.778 | 0.191 |
Others | 3.037 | 0.348–26.501 | 0.315 | 4.303 | 0.367–50.483 | 0.245 |
Palliative surgery | ||||||
Yes | 0.210 | 0.057–0.772 | 0.019 | 0.123 | 0.026–0.594 | 0.009 |
TMB (continuous) | 0.948 | 0.861–1.044 | 0.280 | |||
TMB group | ||||||
Low vs. High | 1.334 | 0.398–4.471 | 0.640 | 0.471 | 0.097–2.280 | 0.349 |
PET radiomic score | ||||||
Low vs. High | 3.081 | 1.078–8.810 | 0.036 | 4.684 | 1.069–20.526 | 0.040 |
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Lee, H.; Moon, S.H.; Hong, J.Y.; Lee, J.; Hyun, S.H. A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer. Cancers 2023, 15, 3841. https://doi.org/10.3390/cancers15153841
Lee H, Moon SH, Hong JY, Lee J, Hyun SH. A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer. Cancers. 2023; 15(15):3841. https://doi.org/10.3390/cancers15153841
Chicago/Turabian StyleLee, Hyunjong, Seung Hwan Moon, Jung Yong Hong, Jeeyun Lee, and Seung Hyup Hyun. 2023. "A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer" Cancers 15, no. 15: 3841. https://doi.org/10.3390/cancers15153841