Serology-Based Model for Personalized Epithelial Ovarian Cancer Risk Evaluation
Abstract
:1. Introduction
2. Patients and Methods
2.1. Patients
2.2. Data Collection
2.3. Definitions
2.4. Statistical Analysis
3. Results
3.1. Demographic Parameters
3.2. Clinical and Pathological Parameters Based on the RMS before Surgery
3.3. Survival Rates Postoperatively Based on the RMS
3.4. Univariable and Multivariable Cox Regression Analyses of Parameters Related to OS in EOC Patients Undergoing Curative Resection
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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Variables | Number of Patients |
---|---|
Age (years, median, IQR) | 54 (48.00–62.00) |
BMI (kg/m2, median, IQR) | 23 (20.70–24.50) |
Menopause (%) | |
No | 225 (35.40) |
Yes | 410 (64.60) |
FIGO stage (%) | |
Early | 138 (21.70) |
Advanced | 497 (78.30) |
Grade (%) | |
G3 | 575 (90.60) |
G1/G2 | 60 (9.40) |
Histology (%) | |
Serous | 544 (85.70) |
Non-serous | 91 (14.30) |
Lymphatic metastasis (%) | |
No | 460 (72.40) |
Yes | 175 (27.60) |
Ascites (mL, %) | |
<1000 | 427 (67.20) |
≥1000 | 208 (32.80) |
CA125 (U/mL, median, IQR) | 521.00 (161.50–1478.00) |
HE4 (pmol/L, median, IQR) | 285.80 (133.40–683.98) |
NLR (median, IQR) | 2.90 (2.00–4.15) |
PLR (median, IQR) | 193.50 (137.06–278.89) |
MLR (median, IQR) | 0.27 (0.20–0.38) |
FAR (median, IQR) | 0.09 (0.07–0.12) |
D-dimer (μg/mL, median, IQR) | 3.16 (1.02–6.92) |
Follow-up time (months, median, IQR) | 34.90 (24.83–50.53) |
OS time (months, median, IQR) | 31.40 (21.30–47.63) |
Variables | Risk Model Score (RMS) | ||
---|---|---|---|
<3 (n = 232) | ≥3 (n = 403) | p Value | |
Age (years) | |||
<50 | 80(34.5) | 124(30.8) | |
≥50 | 152(65.5) | 279(69.2) | 0.335 |
BMI (kg/m2) | |||
<23 | 117(50.4) | 182(45.2) | |
≥23 | 115(49.6) | 221(54.8) | 0.200 |
Menopause | |||
No | 85(36.6) | 140(34.7) | |
Yes | 147(63.4) | 263(65.3) | 0.630 |
FIGO stage | |||
Early | 85(36.6) | 53(13.2) | |
Advanced | 147(63.4) | 350(86.8) | <0.0001 |
Grade | |||
G3 | 191(82.3) | 384(95.3) | |
G1/G2 | 41(17.7) | 19(4.7) | <0.0001 |
Histology | |||
Serous | 176(75.9) | 368(91.3) | |
Non-serous | 56(24.1) | 35(9.7) | <0.0001 |
Lymphatic metastasis | |||
No | 187(80.6) | 273(67.7) | |
Yes | 45(19.4) | 130(32.3) | <0.0001 |
Ascites (mL) | |||
<1000 | 201(86.6) | 226(56.1) | |
≥1000 | 31(13.4) | 177(43.9) | <0.0001 |
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Yan, T.; Ma, X.; Hu, H.; Gong, Z.; Zheng, H.; Xie, S.; Guo, L.; Lu, R. Serology-Based Model for Personalized Epithelial Ovarian Cancer Risk Evaluation. Curr. Oncol. 2022, 29, 2695-2705. https://doi.org/10.3390/curroncol29040220
Yan T, Ma X, Hu H, Gong Z, Zheng H, Xie S, Guo L, Lu R. Serology-Based Model for Personalized Epithelial Ovarian Cancer Risk Evaluation. Current Oncology. 2022; 29(4):2695-2705. https://doi.org/10.3390/curroncol29040220
Chicago/Turabian StyleYan, Tianqing, Xiaolu Ma, Haoyun Hu, Zhiyun Gong, Hui Zheng, Suhong Xie, Lin Guo, and Renquan Lu. 2022. "Serology-Based Model for Personalized Epithelial Ovarian Cancer Risk Evaluation" Current Oncology 29, no. 4: 2695-2705. https://doi.org/10.3390/curroncol29040220