Chenyan Guo

Machine Learning for Preoperative Assessment and Postoperative Prediction in Cervical Cancer: Multicenter Retrospective Model Integrating MRI and Clinicopathological Data

Abstract Background Machine learning (ML) has been increasingly applied to cervical cancer (CC) research. However, few studies have combined both clinical parameters and imaging data. At the same time, there remains an urgent need for more robust and accurate preoperative assessment of parametrial invasion and lymph node metastasis, as well as postoperative prognosis prediction. Objective The objective of this study is to develop an integrated ML model combining clinicopathological variables and magnetic resonance image features for (1) preoperative parametrial invasion and lymph node metastasis detection and (2) postoperative recurrence and survival prediction. Methods Retrospective data from 250 patients with CC (2014‐2022; 2 tertiary hospitals) were analyzed. Variables were assessed for their predictive value regarding parametrial invasion, lymph node metastasis, survival, and recurrence using 7 ML models: K-nearest neighbor (KNN), support vector machine, decision tree, random forest (RF), balanced RF, weighted DT, and weighted KNN. Performance was assessed via 5-fold cross-validation using accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). The optimal models were deployed in an artificial intelligence–assisted contouring and prognosis prediction system. Results Among 250 women, there were 11 deaths and 24 recurrences. (1) For preoperative evaluation, the integrated model using balanced RF achieved optimal performance (sensitivity 0.81, specificity 0.85) for parametrial invasion, while weighted KNN achieved the best performance for lymph node metastasis (sensitivity 0.98, AUC 0.72). (2) For postoperative prognosis, weighted KNN also demonstrated high accuracy for recurrence (accuracy 0.94, AUC 0.86) and mortality (accuracy 0.97, AUC 0.77), with relatively balanced sensitivity of 0.80 and 0.33, respectively. (3) An artificial intelligence–assisted contouring and prognosis prediction system was developed to support preoperative evaluation and postoperative prognosis prediction. Conclusions The integration of clinical data and magnetic resonance images provides enhanced diagnostic capability to preoperatively detect parametrial invasion and lymph node metastasis detection and prognostic capability to predict recurrence and mortality for CC, facilitating personalized, precise treatment strategies.

Identification of a novel six‐gene signature with potential prognostic and therapeutic value in cervical cancer

AbstractIntroductionCervical cancer has high mortality, high recurrence and poor prognosis. Although prognostic biomarkers such as clinicopathological features have been proposed, their accuracy and precision are far from satisfactory. Therefore, novel biomarkers are urgently needed for disease surveillance, prognosis prediction and treatment selection.MaterialsDifferentially expressed genes (DEGs) between cervical cancer and normal tissues from three microarray datasets extracted from the Gene Expression Omnibus platform were identified and screened. Based on these DEGs, a six‐gene prognostic signature was constructed using cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas. Next, the molecular functions and related pathways of the six genes were investigated through gene set enrichment analysis and co‐expression analysis. Additionally, immunophenoscore analysis and the QuartataWeb Server were employed to explore the therapeutic value of the six‐gene signature.ResultsWe discovered 178 overlapping DEGs in three microarray datasets and established a six‐gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) prognostic signature with stable and excellent performance in predicting overall survival in different subgroups. Intriguingly, the six‐gene signature was closely associated with the immune response and tumour immune microenvironment. The six‐gene signature might be used for predicting response to immune checkpoint inhibitors (ICIs) and the six genes may serve as new drug targets for cervical cancer.ConclusionOur study established a novel six‐gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) signature that was closely associated with the immune response and tumour immune microenvironment. The six‐gene signature was indicative of aggressive features of cervical cancer and therefore might serve as a promising biomarker for predicting not only overall survival but also ICI treatment effectiveness. Moreover, three genes (UPP1, ISG20 and GLTP) within the six‐gene signature have the potential to become novel drug targets.

Effect of the surgical approach on survival outcomes in patients undergoing radical hysterectomy for cervical cancer: A real‐world multicenter study of a large Chinese cohort from 2006 to 2017

AbstractObjectiveTo compare survival outcomes of minimally invasive surgery (MIS) and laparotomy in early‐stage cervical cancer (CC) patients.MethodsA multicenter retrospective cohort study was conducted with International Federation of Gynecology and Obstetrics (FIGO, 2009) stage IA1 (lymphovascular invasion)‐IIA1 CC patients undergoing MIS or laparotomy at four tertiary hospitals from 2006 to 2017. Propensity score matching and weighting and multivariate Cox regression analyses were performed. Survival was compared in various matched cohorts and subgroups.ResultsThree thousand two hundred and fifty‐two patients (2439 MIS and 813 laparotomy) were included after matching. (1) The 2‐ and 5‐year recurrence‐free survival (RFS) (2‐year, hazard ratio [HR], 1.81;95% confidence interval [CI], 1.09‐3.0; 5‐year, HR, 2.17; 95% CI, 1.21‐3.89) or overall survival (OS) (2‐year, HR, 1.87; 95% CI, 1.03‐3.40; 5‐year, HR, 2.57; 95% CI, 1.29‐5.10) were significantly worse for MIS in patients with stage I B1, but not the cohort overall (2‐year RFS, HR, 1.04; 95% CI, 0.76‐1.42; 2‐year OS, HR, 0.99; 95% CI, 0.70‐1.41; 5‐year RFS, HR, 1.12; 95% CI, 0.76‐1.65; 5‐year OS, HR, 1.20; 95% CI, 0.79‐1.83) or other stages (2) In a subgroup analysis, MIS exhibited poorer survival in many population subsets, even in patients with less risk factors, such as patients with squamous cell carcinoma, negative for parametrial involvement, with negative surgical margins, negative for lymph node metastasis, and deep stromal invasion < 2/3. (3) In the cohort treated with (2172, 54%) or without adjuvant treatment (1814, 46%), MIS showed worse RFS than laparotomy in patients treated without adjuvant treatment, whereas no differences in RFS and OS were observed in adjuvant‐treatment cohort. (4) Inadequate surgeon proficiency strongly correlated with poor RFS and OS in patients receiving MIS compared with laparotomy.ConclusionsMIS exhibited poorer survival outcomes than laparotomy group in many population subsets, even in low‐risk subgroups. Therefore, laparotomy should be the recommended approach for CC patients.