Clinical analysis and artificial intelligence survival prediction of serous ovarian cancer based on preoperative circulating leukocytes

Abstract

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.

The Use of Artificial Intelligence for Complete Cytoreduction Prediction in Epithelial Ovarian Cancer: A Narrative Review

“…Comparing these results with the logistic regression models data, AI revealed greater accuracy in 75% of analysis thanks to its ability to predict survival more or less than the median for each cohort outperforming logistic regression, confirming that AI is a better predictor of outcome than a traditional statistic system. A similar predicting survival AUC (.69) was described in the study of Feng et al 29 in which it is explored the role of preoperative circulating leukocytes in predicting survival prognosis of serous ovarian carcinoma. Another interesting finding of this paper is the relationship between the rising of monocytes and leucocytes and the worsening of prognosis, especially in terms of monocytes-to-leucocytes ratio: it confirms the fundamental importance of immune environment in EOC and this parameter should achieve more attention in AI algorithm.…”

“…Different AI methods were used in the single studies. In 4 out of 6 studies the authors declared to use one AI system to analyze data: Laios et al 25 applied k. NN (k-Nearest Neighbor), Laios et al 26 applied XGBoost Model (eXtreme Gradient Boosting Model), Bogani et al 27 applied ANN (Artificial Neural Network), Feng Y et al 29 applied MLDTA (Machine Learning Based Decision Tree Algorithm). Otherwise, multiple systems were used in Enshaei et al 24 and Laios et al 28 studies.…”

“… 41 In the study of Feng et al. 29 MO/LY ratio resulted significantly increased in the blood of patients with EOC and the authors concluded that the reduction of lymphocites was related to poorly immunological response to the cancer.…”

Lymphocyte-to-monocyte ratio after primary surgery is an independent prognostic factor for patients with epithelial ovarian cancer: A propensity score matching analysis

“…Previous studies have demonstrated the predictive potential of LMR in evaluating the prognosis of patients with various types of malignancies, including resectable ovarian cancer ( 8 , 9 , 27 – 29 ). In 2016, Eo et al.…”

“…Previous studies have demonstrated the predictive potential of LMR in evaluating the prognosis of patients with various types of malignancies, including resectable ovarian cancer (8,9,(27)(28)(29). In 2016, Eo et al first reported a correlation between low preoperative LMR and poor prognosis in ovarian cancer (9).…”