Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. OCT Image Acquisition
2.3. Region of Interest Segmentation and Parameter Measurement
2.4. Feature Extraction and Selection
2.5. Development of OCT-Omics Models
- sampling_strategy = “auto”,
- random_state = None,
- k_neighbors = 5,
- n_jobs = None
2.6. Training and Validation of Machine Learning Models
- SVM: penalty factor C = 1; kernel function, linear; maximum of iterations = 1000; 5-fold cross-validation;
- BPNN: activation function, rectified linear unit (ReLU); solver, Limited-memory Broyden–Fletche–Goldfarb–Shanno (LBFGS); L2 regularization term = 1; number of iterations = 1000; 5-fold cross-validation.
2.7. Statistical Analysis
3. Results
3.1. Summary of Study Design and Baseline Characteristics
3.2. OCT-Omics Model Development and Clinical Implication Evaluation
3.3. Evaluation of Overall Performance in Multiple Models
3.4. Clinical Implications of the OCT-Omics Model
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
BASE | MIN | N | T | H | HFF | MHI | DHI | THI | OCT-Omics | |
---|---|---|---|---|---|---|---|---|---|---|
BASE | 1 *** | 0.653 *** | 0.719 *** | 0.766 *** | 0.324 *** | −0.131 | −0.805 *** | −0.2 ** | −0.442 *** | 0.217 *** |
MIN | 0.653 *** | 1 *** | 0.141 * | 0.22 *** | −0.04 | −0.658 *** | −0.718 *** | 0.518 *** | −0.904 *** | 0.255 *** |
N | 0.719 *** | 0.141 * | 1 *** | 0.865 *** | 0.52 *** | 0.46 *** | −0.411 *** | −0.591 *** | 0.074 | 0.153 * |
T | 0.766 *** | 0.22 *** | 0.865 *** | 1 *** | 0.557 *** | 0.398 *** | −0.437 *** | −0.532 *** | 0.016 | 0.137 |
H | 0.324 *** | −0.04 | 0.52 *** | 0.557 *** | 1 *** | 0.442 *** | 0.208 ** | −0.384 *** | 0.39 *** | 0.103 |
HFF | −0.131 | −0.658 *** | 0.46 *** | 0.398 *** | 0.442 *** | 1 *** | 0.436 *** | −0.733 *** | 0.787 *** | −0.103 |
MHI | −0.805 *** | −0.718 *** | −0.411 *** | −0.437 *** | 0.208 ** | 0.436 *** | 1 *** | −0.067 | 0.737 *** | −0.2 ** |
DHI | −0.2 ** | 0.518 *** | −0.591 *** | −0.532 *** | −0.384 *** | −0.733 *** | −0.067 | 1 *** | −0.646 *** | 0.055 |
THI | −0.442 *** | −0.904 *** | 0.074 | 0.016 | 0.39 *** | 0.787 *** | 0.737 *** | −0.646 *** | 1 *** | −0.215 ** |
OCT-omics | 0.217 *** | 0.255 *** | 0.153 * | 0.137 | 0.103 | −0.103 | −0.2 ** | 0.055 | −0.215 ** | 1 *** |
Variables | Total (n = 200) | ILM Peeling (n = 136) | ILM Flap (n = 64) | Statistic | p |
---|---|---|---|---|---|
BASE, M (IQR) | 965.50 (591) | 914.00 (565) | 1029.00 (574) | Z = −2.10 | 0.035 ** |
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Variables | Total (n = 200) | Training Set (n = 140) | Test Set (n = 60) | p |
---|---|---|---|---|
Age, years, M (IQR) | 61.00 (12.00) | 61.00 (13.25) | 61.00 (10.00) | 0.393 f |
Age groups, n (%) | 0.086 g | |||
<40 | 17 (8.50) | 2 (2.33) | 15 (10.71) | |
≥40 | 183 (91.50) | 58 (96.67) | 125 (89.29) | |
Sex, n (%) | 0.492 g | |||
Female | 133 (66.50) | 91 (65.00) | 42 (70.00) | |
Male | 67 (33.50) | 49 (35.00) | 18 (30.00) | |
Laterality, n (%) | 0.951 g | |||
Right | 106 (53.00) | 74 (52.86) | 32 (53.33) | |
Left | 94 (47.00) | 66 (47.14) | 28 (46.67) | |
Outcome, n (%) | 0.619 g | |||
Closed | 173 (86.50) | 53 (88.33) | 120 (85.71) | |
Open | 27 (13.50) | 20 (14.29) | 7 (11.67) | |
High myopia, n (%) | 0.387 g | |||
No | 170 (85.00) | 121 (86.43) | 49 (81.67) | |
Yes | 30 (15.00) | 19 (13.57) | 11 (18.33) | |
Surgery, n (%) | 0.055 g | |||
ILM peeling a | 136 (68.00) | 101 (72.14) | 35 (58.33) | |
ILM flap b | 64 (32.00) | 39 (27.86) | 25 (41.67) | |
Size of FTMH c, n (%) | 0.947 g | |||
Large | 122 (61.00) | 36 (60.00) | 86 (61.43) | |
Medium | 43 (17.00) | 11 (18.33) | 23 (16.43) | |
Small | 44 (22.00) | 13 (21.67) | 31 (2214) | |
Examination interval d, day, M (IQR) | 59.00 (194.25) | 70.50 (230.75) | 55.50 (168.5) | 0.156 f |
BCVA (logMAR) e, M (IQR) | 1.00 (0.57) | 1.00 (0.60) | 0.92 (0.40) | 0.895 f |
BASE, μm, M (IQR) | 965.50 (591.00) | 953.00 (546.5) | 1059.50 (598.5) | 0.570 f |
MIN, μm, M (IQR) | 478.00 (377.75) | 478.00 (318.50) | 486.00 427.75) | 0.657 f |
N, μm, M (IQR) | 330.50 (198.00) | 334.00 (195.25) | 327.00 (215.75) | 0.915 f |
T, μm, M (IQR) | 325.00 (196.75) | 327.00 (195.25) | 318.00 (214.25) | 0.832 f |
H, μm, M (IQR) | 428.50 (108.50) | 428.00 (106.00) | 430.50 (126.25) | 0.719 f |
HFF, M (IQR) | 0.73 (0.27) | 0.72 (0.26) | 0.74 (0.27) | 0.897 f |
MHI, M (IQR) | 0.44 (0.24) | 0.45 (0.23) | 0.43 (0.24) | 0.617 f |
DHI, M (IQR) | 0.51 (0.27) | 0.51 (0.26) | 0.47 (0.28) | 0.542 f |
THI, M (IQR) | 0.92 (0.90) | 0.88 (0.86) | 0.95 (0.96) | 0.996 f |
Training Set | Test Set | |||||
---|---|---|---|---|---|---|
Logistic | SVM | BPNN | Logistic | SVM | BPNN | |
SEN | 0.967 | 0.942 | 0.942 | 0.925 | 0.887 | 0.925 |
SPE | 0.975 | 0.975 | 0.958 | 0.857 | 0.857 | 1.000 |
ACC | 0.971 | 0.958 | 0.950 | 0.917 | 0.883 | 0.933 |
PPV | 0.975 | 0.974 | 0.958 | 0.980 | 0.979 | 1.000 |
NPV | 0.967 | 0.944 | 0.943 | 0.600 | 0.500 | 0.636 |
F1 | 0.971 | 0.958 | 0.950 | 0.951 | 0.931 | 0.961 |
AUC | 0.998 | 0.988 | 0.995 | 0.941 | 0.943 | 0.968 |
Algorithm | MH | Surgery Type | Eyes (Patients) (n) | Country | SEN | SPE | ACC | AUC | |
---|---|---|---|---|---|---|---|---|---|
Xiao (2021) [33] | Random forest | Full-thickness IMH | ILM peeling | 288 | China | 0.973 | 0.904 | 0.892 | 0.951 |
ZGOLLI (2022) [34] | MDSS | Full-thickness IMH | IML flap | 120 (114) | Tunis | \ | \ | \ | 0.967 |
Xiao (2023) [35] | MDFN | Full-thickness IMH | ILM peeling | 209 | China | 0.979 | 0.815 | 0.875 | 0.947 |
Xiao (2023) [35] | MDFN | FTMH | ILM peeling | 330 | China | 0.766 | 0.977 | 0.825 | 0.904 |
Our models | Logistic | FTMH | ILM peeling or IML flap | 200 (193) | China | 0.925 | 0.857 | 0.917 | 0.941 |
SVM | 0.887 | 0.857 | 0.883 | 0.943 | |||||
BPNN | 0.925 | 1 | 0.933 | 0.968 |
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Hu, Y.; Meng, Y.; Liang, Y.; Zhang, Y.; Chen, B.; Qiu, J.; Meng, Z.; Luo, J. Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole. Bioengineering 2024, 11, 949. https://doi.org/10.3390/bioengineering11090949
Hu Y, Meng Y, Liang Y, Zhang Y, Chen B, Qiu J, Meng Z, Luo J. Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole. Bioengineering. 2024; 11(9):949. https://doi.org/10.3390/bioengineering11090949
Chicago/Turabian StyleHu, Yuqian, Yongan Meng, Youling Liang, Yiwei Zhang, Biying Chen, Jianing Qiu, Zhishang Meng, and Jing Luo. 2024. "Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole" Bioengineering 11, no. 9: 949. https://doi.org/10.3390/bioengineering11090949
APA StyleHu, Y., Meng, Y., Liang, Y., Zhang, Y., Chen, B., Qiu, J., Meng, Z., & Luo, J. (2024). Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole. Bioengineering, 11(9), 949. https://doi.org/10.3390/bioengineering11090949