A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patients
2.2. Development of the Algorithm
2.3. Statistical Analysis
2.4. Five-Fold Cross-Validation
3. Results
3.1. Baseline Characteristics
3.2. The Comparison of Three Methods in the Patient Summary
3.3. The Model Performance of DCRNet in Pathological Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristics | Category | All Patients, n (%) | ypCR, n (%) | Non-ypCR, n (%) | p |
---|---|---|---|---|---|
Sex | Male | 76 (95) | 20 (87.0) | 56 (98.2) | 0.04 |
Female | 4 (5) | 3 (13.0) | 1 (1.8) | ||
Age (year) | Mean (SD) | 55.68 ± 9.5 | 55.48 ± 7.3 | 55.76 ± 10.3 | 0.28 |
Location | Proximal | 5 (8.9) | 3 (13.1) | 2 (6.1) | 0.16 |
Middle | 23 (41.1) | 12 (52.2) | 11 (33.3) | ||
Distal | 28 (50) | 8 (34.8) | 20 (60.6) | ||
Clinical stage | II | 5 (6.3) | 2 (8.7) | 3 (5.3) | 0.70 |
III | 57 (71.2) | 17 (73.9) | 40 (70.1) | ||
IVA | 18 (22.5) | 4 (17.4) | 14 (24.6) | ||
Total radiation dose (Gy) | 44.0 ± 2.1 | 44.5 ± 2.4 | 43.8 ± 2.8 | 0.81 | |
OS (month) | 24.1 ± 13.7 | 27.7 ± 16.2 | 22.6 ± 14.8 | 0.08 |
AUC (95% CI) | ypCR | ypT0 | ypN0 |
---|---|---|---|
HRN + DCR | 0.928 (0.884–0.972) | 0.939 (0.928–0.950) | 0.891 (0.881–0.901) |
HRN | 0.865 (0.856–0.873) | 0.877 (0.865–0.889) | 0.859 (0.846–0.872) |
RN + DCR | 0.829 (0.809–0.849) | 0.845 (0.828–0.862) | 0.813 (0.799–0.827) |
RN | 0.763 (0.751–0.775) | 0.775 (0.759–0.791) | 0.751 (0.733–0.769) |
EN + DCR | 0.836 (0.818–0.854) | 0.847 (0.825–0.869) | 0.825 (0.806–0.844) |
EN | 0.766 (0.749–0.783) | 0.780 (0.756–0.804) | 0.752 (0.736–0.768) |
DN + DCR | 0.832 (0.812–0.852) | 0.844 (0.826–0.862) | 0.820 (0.803–0.837) |
DN | 0.761 (0.742–0.780) | 0.776 (0.756–0.796) | 0.746 (0.731–0.761) |
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Yap, W.-K.; Hsiao, I.-T.; Yap, W.-L.; Tsai, T.-Y.; Lu, Y.-A.; Yang, C.-K.; Peng, M.-T.; Su, E.-L.; Cheng, S.-C. A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery. Biomedicines 2023, 11, 3072. https://doi.org/10.3390/biomedicines11113072
Yap W-K, Hsiao I-T, Yap W-L, Tsai T-Y, Lu Y-A, Yang C-K, Peng M-T, Su E-L, Cheng S-C. A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery. Biomedicines. 2023; 11(11):3072. https://doi.org/10.3390/biomedicines11113072
Chicago/Turabian StyleYap, Wing-Keen, Ing-Tsung Hsiao, Wing-Lake Yap, Tsung-You Tsai, Yi-An Lu, Chan-Keng Yang, Meng-Ting Peng, En-Lin Su, and Shih-Chun Cheng. 2023. "A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery" Biomedicines 11, no. 11: 3072. https://doi.org/10.3390/biomedicines11113072