Chenyan Guo

Background Postoperative recurrence remains a major challenge in cervical cancer (CC). Current risk stratification based mainly on clinicopathological factors has limited predictive capability due to incomplete representation of molecular and imaging heterogeneity. This study aimed to develop a multimodal framework integrating clinicopathological features, transcriptomic profiles, and magnetic resonance imaging (MRI) radiomics for individualized recurrence prediction in CC. Methods This retrospective study included 219 CC patients who underwent radical surgery between 2018 and 2022. Clinicopathological data, preoperative MRI, and tumor transcriptomic profiles were collected. Unimodal models were developed using each data modality separately, while a multimodal fusion model was constructed using a stacking ensemble approach. Model performance was evaluated using nested cross-validation, discrimination metrics, calibration analysis, and decision curve analysis. Results Among unimodal models, transcriptomic and MRI radiomics models achieved higher discriminative performance than the clinicopathological model (AUC: 0.75, 0.73, and 0.65, respectively). The multimodal fusion model further improved recurrence prediction, achieving an AUC of 0.85, with accuracy, sensitivity, and specificity of 0.80, 0.75, and 0.81, respectively. The integrated model showed improved calibration and greater clinical net benefit compared with traditional Sedlis criteria. Ablation analysis demonstrated that each modality contributed complementary predictive information. Conclusions Integration of clinicopathological, transcriptomic, and MRI radiomic biomarkers improved postoperative recurrence prediction in CC, providing a potential approach for personalized risk stratification and postoperative management.

Background: Machine learning (ML) has been increasingly applied to cervical cancer (CC) research. However, few studies have combined both clinical parameters and imaging data. Additionally, 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: To develop an integrated ML model combining clinicopathological variables and MR images features for: (1) preoperative detection of parametrial invasion and lymph node metastasis; and (2) postoperative recurrence and survival prediction. Methods: Retrospective data from 250 patients with CC (2014-2022; at two tertiary hospitals) were analyzed. Variables were assessed for their predictive value regarding parametrial invasion, lymph node metastasis, survival, and recurrence using seven ML models: K-nearest neighbors (KNN), Support vector machine (SVM), Decision Tree (DT), 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 AI-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 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 AI-assisted contouring and prognosis prediction system was developed to support preoperative evaluation, and postoperative prognosis prediction. Conclusions: The integration of clinical data and MR images provides enhanced diagnostic capability for preoperatively detection of parametrial invasion and lymph node metastasis detection, as well as prognostic capability for predicting recurrence and mortality for CC, facilitating personalized, precise treatment strategies.

Background: The mechanism underlying cervical carcinogenesis that is mediated by persistent human papillomavirus (HPV) infection remains elusive.Aims: Here, for the first time, we deciphered both the temporal transition and spatial distribution of cellular subsets during disease progression from normal cervix tissues to precursor lesions to cervical cancer.Materials & Methods: We generated scRNA‐seq profiles and spatial transcriptomics data from nine patient samples, including two HPV‐negative normal, two HPV‐positive normal, two HPV‐positive HSIL and three HPV‐positive cancer samples.Results: We not only identified three ‘HPV‐related epithelial clusters’ that are unique to normal, high‐grade squamous intraepithelial lesions (HSIL) and cervical cancer tissues but also discovered node genes that potentially regulate disease progression. Moreover, we observed the gradual transition of multiple immune cells that exhibited positive immune responses, followed by dysregulation and exhaustion, and ultimately established an immune‐suppressive microenvironment during the malignant program. In addition, analysis of cellular interactions further verified that a ‘homeostasis‐balance‐malignancy’ change occurred within the cervical microenvironment during disease progression.Discussion: We for the first time presented a spatiotemporal atlas that systematically described the cellular heterogeneity and spatial map along the four developmental steps of HPV‐related cervical oncogenesis, including normal, HPV‐positive normal, HSIL and cancer. We identified three unique HPV‐related clusters, discovered critical node genes that determined the cell fate and uncovered the immune remodeling during disease escalation.Conclusion: Together, these findings provided novel possibilities for accurate diagnosis, precise treatment and prognosis evaluation of patients with precancer and cervical cancer.

Introduction: Cervical 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.Materials: Differentially 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.Results: We 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.Conclusion: Our 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.

Objective: To compare survival outcomes of minimally invasive surgery (MIS) and laparotomy in early‐stage cervical cancer (CC) patients.Methods: A 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.Results: Three 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.Conclusions: MIS 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.