Clinical Value of Hematological Biomarkers in Uterine Cervical Cancer

Gavrilescu, M; Huțanu, Ionuț; Ioanid, Nicolae; Muşină, Ana-Maria; Mihaela, Buna Arvinte; Moscalu, Mihaela; Scripcariu, Ioana Sadiye; Scripcariu, Viorel

doi:10.21614/chirurgia.111.6.493

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“…For example, increased preoperative plasma squamous cell carcinoma antigen (SCC-Ag) levels may predict an increased incidence of CC LNM [19,20] . Moreover, Gavrilescu et al [21] demonstrated that CC patients without LNM had a signi cantly higher neutrophil-lymphocyte ratio (NLR) than CC patients with LNM. To our knowledge, no studies have used pure hematological indicators to build machine learning models for the preoperative prediction of LNM status in CC patients.…”

Section: Introductionmentioning

confidence: 99%

Hematological Indicator-Based Machine Learning Models for Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Zhao

Wang

Sun

et al. 2023

Preprint

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Background Lymph node metastasis (LNM) is an important prognostic factor for cervical cancer (CC) and determines the treatment strategy. Hematological indicators have been reported as being useful biomarkers for the prognosis of a variety of cancers. This study aimed to evaluate the feasibility of machine learning models characterized by preoperative hematological indicators to predict the LNM status of CC patients before surgery. Methods The clinical data of 236 patients with pathologically confirmed CC were retrospectively analyzed at the Gynecology Oncology Department of the First Affiliated Hospital of Bengbu Medical College from November 2020 to August 2022. Recursive feature elimination (RFE) was used to select 12 features from 35 hematological indicators and for the construction of 6 machine learning predictive models, including Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GNB), and Logistic Regression (LR), as well as Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost). Evaluation metrics of predictive models included the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. Results There was no significant difference in the 10-fold cross-validated AUC between XGBoost and RF (XGBoost, AUC = 0.903 vs. RF, AUC = 0.908; P = 0.889, DeLong test). XGBoost showed the best overall predictive performance. The specific performance indicators of XGBoost were AUC (0.903, 95% CI: 0.829–0.977), accuracy (0.841, 95% CI: 0.759–0.923), precision (0.850, 95% CI: 0.774–0.926), recall (0.837, 95% CI: 0.755–0.919), and F1-score (0.832, 95% CI: 0.739–0.925). Conclusions XGBoost and RF based on preoperative hematological indicators that are easily available in clinical practice showed superior performance in the preoperative prediction of CC LNM. However, investigations on larger external cohorts of patients are required for further validation of our findings.

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Section: Introductionmentioning

confidence: 99%