Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Study Design and Patient Population
2.2. NCRT
2.3. Pathological Assessment
2.4. PET/CT Image Acquisition
2.5. Data Pre-Processing
2.6. Metric Learning
2.7. Uniform Manifold Approximation and Projection for Dimensionality Reduction
2.8. Support Vector Machine
2.9. Statistical Analysis
3. Results
3.1. Patient Characteristics
3.2. Partitioning of Patients in the Training Cohort
3.3. Image-Based Prediction
3.4. Validation and Comparison
3.5. Heat Map
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Appendix A
Appendix B
AUC (95% CI) | Sensitivity (95% CI) | Specificity (95% CI) | Accuracy (95% CI) | |||||
---|---|---|---|---|---|---|---|---|
Training cohort (n = 202) | 0.960 (0.951–0.993) | 0.983 (0.962–1.000) | 0.965 (0.936–0.993) | 0.975 (0.960–0.991) | ||||
Validation cohort (n = 34) | 0.962 (0.935–0.999) | 0.950 (0.910–0.990) | 1.000 (1.000–1.000) | 0.982 (0.969–0.997) | ||||
Fold_1 | Fold_2 | Fold_3 | Fold_4 | Fold_5 | Summation | |||
Training AUC | 0.993 | 0.944 | 1.000 | 0.964 | 0.959 | 0.972 | ||
Validation AUC | 1.000 | 0.92 | 0.939 | 0.977 | 1.000 | 0.967 | ||
Training accuracy | 0.976 | 0.976 | 1.000 | 0.950 | 0.975 | 0.975 | ||
Validation accuracy | 1.000 | 0.971 | 0.971 | 0.971 | 1.000 | 0.982 | ||
Training sensitivity | 1.000 | 1.000 | 1.000 | 0.957 | 0.957 | 0.983 | ||
Validation sensitivity | 1.000 | 0.917 | 0.917 | 0.917 | 1.000 | 0.950 | ||
Training specificity | 0.941 | 0.941 | 1.000 | 0.941 | 1.000 | 0.965 | ||
Validation specificity | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
AUC (95% CI) | Sensitivity (95% CI) | Specificity (95% CI) | Accuracy (95% CI) | |||||
---|---|---|---|---|---|---|---|---|
Training cohort (n = 202) | 0.618 (0.576–0.704) | 0.630 (0.449–0.810) | 0.588 (0.349–0.827) | 0.614 (0.591–0.636) | ||||
Validation cohort (n = 34) | 0.606 (0.511–0.704) | 0.467 (0.238–0.695) | 0.745 (0.645–0.846) | 0.647 (0.611–0.683) | ||||
Fold_1 | Fold_2 | Fold_3 | Fold_4 | Fold_5 | Summation SUM | |||
Training AUC | 0.645 | 0.588 | 0.573 | 0.760 | 0.634 | 0.640 | ||
Validation AUC | 0.542 | 0.614 | 0.716 | 0.458 | 0.708 | 0.608 | ||
Training accuracy | 0.634 | 0.634 | 0.575 | 0.625 | 0.600 | 0.614 | ||
Validation accuracy | 0.618 | 0.618 | 0.706 | 0.618 | 0.676 | 0.647 | ||
Training sensitivity | 0.625 | 0.958 | 0.565 | 0.391 | 0.609 | 0.630 | ||
Validation sensitivity | 0.333 | 0.583 | 0.583 | 0.083 | 0.750 | 0.467 | ||
Training specificity | 0.647 | 0.176 | 0.588 | 0.941 | 0.588 | 0.588 | ||
Validation specificity | 0.773 | 0.636 | 0.773 | 0.909 | 0.636 | 0.745 |
Number | Correlation | p-Value | |
---|---|---|---|
Training cohort | 202 | 0.215 | 0.002 |
Test cohort | 34 | 0.308 | 0.076 |
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Characteristic | Value |
---|---|
Age (years) | 31–86 (median, 58) |
Gender | Male:139, Female:63 |
Primary lesion location | |
low rectum | 83 |
middle rectum | 103 |
upper rectum or rectosigmoid junction | 16 |
CEA (ng/dL) | 17.08 ± 37.92(0.48–241.88) |
Pretreatment clinical staging (AJCC 7th ed.) | |
T stage | T2:26, T3:148, T4:28 |
N stage | N0:60, N1:80; N2:62 |
M stage | M0:198, M1:4 |
Differentiation | |
W-D | 5 |
M-D | 39 |
P-D | 4 |
unknown | 154 |
Concurrent chemotherapy regimen (%) | |
Capecitabine | 174 |
Uracil-Tegafur | 21 |
Intravenous 5-Fluorouracil based regimen | 7 |
Interval from the end of radiation to surgery | |
>4 and <8 week | 102 |
≥8 and <12 week | 100 |
Tumor regression grade (%) | |
Grade 0 | 0 |
Grade 1 | 31 |
Grade 2 | 54 |
Grade 3 | 93 |
Grade 4 | 24 |
Prediction | RG Grade 3 or 4 Response | Indices | |
---|---|---|---|
Positive | Negative | ||
Positive | 115 | 2 | 98.3% |
Negative | 3 | 82 | 96.5% |
Indices | 97.5% | 97.6% | 97.5% |
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Wu, K.-C.; Chen, S.-W.; Hsieh, T.-C.; Yen, K.-Y.; Law, K.-M.; Kuo, Y.-C.; Chang, R.-F.; Kao, C.-H. Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography. Cancers 2021, 13, 6350. https://doi.org/10.3390/cancers13246350
Wu K-C, Chen S-W, Hsieh T-C, Yen K-Y, Law K-M, Kuo Y-C, Chang R-F, Kao C-H. Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography. Cancers. 2021; 13(24):6350. https://doi.org/10.3390/cancers13246350
Chicago/Turabian StyleWu, Kuo-Chen, Shang-Wen Chen, Te-Chun Hsieh, Kuo-Yang Yen, Kin-Man Law, Yu-Chieh Kuo, Ruey-Feng Chang, and Chia-Hung Kao. 2021. "Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography" Cancers 13, no. 24: 6350. https://doi.org/10.3390/cancers13246350