TY - JOUR
T1 - Clinical analysis and artificial intelligence survival prediction of serous ovarian cancer based on preoperative circulating leukocytes
AU - Feng, Ying
AU - Wang, Zhixiang
AU - Cui, Ran
AU - Xiao, Meizhu
AU - Gao, Huiqiao
AU - Bai, Huimin
AU - Delvoux, Bert
AU - Zhang, Zhen
AU - Dekker, Andre
AU - Romano, Andrea
AU - Wang, Shuzhen
AU - Traverso, Alberto
AU - Liu, Chongdong
AU - Zhang, Zhenyu
N1 - © 2022. The Author(s).
PY - 2022/5/24
Y1 - 2022/5/24
N2 - Circulating leukocytes are an important part of the immune system. The aim of this work is to explore the role of preoperative circulating leukocytes in serous ovarian carcinoma and investigate whether they can be used to predict survival prognosis. Routine blood test results and clinical information of patients with serous ovarian carcinoma were retrospectively collected. And to predict survival according to the blood routine test result the decision tree method was applied to build a machine learning model.The results showed that the number of preoperative white blood cells (p = 0.022), monocytes (p < 0.001), lymphocytes (p < 0.001), neutrophils (p < 0.001), and eosinophils (p < 0.001) and the monocyte to lymphocyte (MO/LY) ratio in the serous ovarian cancer group were significantly different from those in the control group. These factors also showed a correlation with other clinicopathological characteristics. The MO/LY was the root node of the decision tree, and the predictive AUC for survival was 0.69. The features involved in the decision tree were the MO/LY, differentiation status, CA125 level, neutrophils (NE,) ascites cytology, LY% and age.In conclusion, the number and percentage of preoperative leukocytes in patients with ovarian cancer is changed significantly compared to those in the normal control group, as well as the MO/LY. A decision tree was built to predict the survival of patients with serous ovarian cancer based on the CA125 level, white blood cell (WBC) count, presence of lymph node metastasis (LNM), MO count, the MO/LY ratio, differentiation status, stage, LY%, ascites cytology, and age.
AB - Circulating leukocytes are an important part of the immune system. The aim of this work is to explore the role of preoperative circulating leukocytes in serous ovarian carcinoma and investigate whether they can be used to predict survival prognosis. Routine blood test results and clinical information of patients with serous ovarian carcinoma were retrospectively collected. And to predict survival according to the blood routine test result the decision tree method was applied to build a machine learning model.The results showed that the number of preoperative white blood cells (p = 0.022), monocytes (p < 0.001), lymphocytes (p < 0.001), neutrophils (p < 0.001), and eosinophils (p < 0.001) and the monocyte to lymphocyte (MO/LY) ratio in the serous ovarian cancer group were significantly different from those in the control group. These factors also showed a correlation with other clinicopathological characteristics. The MO/LY was the root node of the decision tree, and the predictive AUC for survival was 0.69. The features involved in the decision tree were the MO/LY, differentiation status, CA125 level, neutrophils (NE,) ascites cytology, LY% and age.In conclusion, the number and percentage of preoperative leukocytes in patients with ovarian cancer is changed significantly compared to those in the normal control group, as well as the MO/LY. A decision tree was built to predict the survival of patients with serous ovarian cancer based on the CA125 level, white blood cell (WBC) count, presence of lymph node metastasis (LNM), MO count, the MO/LY ratio, differentiation status, stage, LY%, ascites cytology, and age.
KW - Artificial Intelligence
KW - Ascites
KW - CA-125 Antigen
KW - Carcinoma, Ovarian Epithelial/pathology
KW - Cystadenocarcinoma, Serous/pathology
KW - Female
KW - Humans
KW - Lymphocytes
KW - Ovarian Neoplasms/pathology
KW - Prognosis
KW - Retrospective Studies
KW - RATIOS
KW - Recurrence
KW - LYMPHOCYTE
KW - MONOCYTES
KW - Leukocytes
KW - Survival
KW - INFLAMMATORY MARKERS
KW - And prediction
KW - Machine learning
KW - Serous ovarian cancer
KW - UTILITY
KW - CELL
U2 - 10.1186/s13048-022-00994-2
DO - 10.1186/s13048-022-00994-2
M3 - Article
C2 - 35610701
SN - 1757-2215
VL - 15
JO - Journal of Ovarian Research
JF - Journal of Ovarian Research
IS - 1
M1 - 64
ER -