Computed Tomography-Based Quantitative Texture Analysis and Gut Microbial Community Signatures Predict Survival in Non-Small Cell Lung Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Ethical Statement
2.2. Study Population and Treatments
2.3. QTA and Principal Component Analysis
2.4. PD-L1 Immunohistochemistry and TPS Scoring
2.5. DNA Extraction from Stool Samples
2.6. Library Preparation and MG Sequencing
2.7. Internal Transcribed Spacer (ITS2) Sequencing
2.8. Quality Control
2.9. Microbial Taxonomic Profiling
2.10. XGBoost Models for Classification
3. Results
3.1. QTA Parameters Can Predict OS, Response to Therapy and PD-L1 Expression
3.2. Microbial Taxonomic Profiling Reveals Associations with OS, Response to Therapy, PD-L1 Expression, and Toxicity
3.3. Correlation of Clinicopathological Parameters with Metagenome and Principal Components of QTA Analysis
3.4. Outcomes Predicted by the XGB Machine Learning Algorithm
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Clinical Parameter | Long OS Patients | Short OS Patients | p-Value |
---|---|---|---|
gender | 0.802 | ||
male | 41% | 45% | |
female | 59% | 55% | |
age (years, mean) | 65.07 | 61.5 | 0.086 |
PD-L1 expression | 0.798 | ||
TPS ≥ 50% | 41.1% | 36.8% | |
TPS < 50% | 58.9% | 63.2% | |
Smoking (PY, mean) | 39.45 | 37.53 | 0.658 |
BMI (kg/m2, mean) | 25.95 | 24.67 | 0.395 |
COPD-comorbidity | >0.999 | ||
yes | 32.7% | 30.8% | |
no | 67.3% | 69.2% | |
CAT score (mean) | 10.96 | 19.07 | 0.0056 ** |
FEV1% (mean) | 69.93 | 64.7 | 0.54 |
Pseudoprogression | 0.718 | ||
yes | 18.1% | 21.4% | |
no | 81.9% | 78.6% | |
Toxicity | >0.999 | ||
yes | 65.7% | 80% | |
no | 34.3% | 20% | |
Toxicity grade (mean) | 1.06 | 0.778 | 0.157 |
Line of IT (mean) | 1.85 | 1.5 | 0.088 |
Line of IT binary | 0.123 | ||
first line | 29% | 47% | |
subsequent line | 71% | 53% | |
ICI Response at 3 months | <0.001 *** | ||
Response (CR, PR, SD) | 92% | 26% | |
Non-response (PD) | 8% | 74% |
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Dora, D.; Weiss, G.J.; Megyesfalvi, Z.; Gállfy, G.; Dulka, E.; Kerpel-Fronius, A.; Berta, J.; Moldvay, J.; Dome, B.; Lohinai, Z. Computed Tomography-Based Quantitative Texture Analysis and Gut Microbial Community Signatures Predict Survival in Non-Small Cell Lung Cancer. Cancers 2023, 15, 5091. https://doi.org/10.3390/cancers15205091
Dora D, Weiss GJ, Megyesfalvi Z, Gállfy G, Dulka E, Kerpel-Fronius A, Berta J, Moldvay J, Dome B, Lohinai Z. Computed Tomography-Based Quantitative Texture Analysis and Gut Microbial Community Signatures Predict Survival in Non-Small Cell Lung Cancer. Cancers. 2023; 15(20):5091. https://doi.org/10.3390/cancers15205091
Chicago/Turabian StyleDora, David, Glen J. Weiss, Zsolt Megyesfalvi, Gabriella Gállfy, Edit Dulka, Anna Kerpel-Fronius, Judit Berta, Judit Moldvay, Balazs Dome, and Zoltan Lohinai. 2023. "Computed Tomography-Based Quantitative Texture Analysis and Gut Microbial Community Signatures Predict Survival in Non-Small Cell Lung Cancer" Cancers 15, no. 20: 5091. https://doi.org/10.3390/cancers15205091