Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer

Prognostic significance of lymphovascular space invasion in early-stage low-grade endometrioid endometrial cancer: a fifteen-year retrospective Chinese cohort study

OBJECTIVE

In 2016, the ESMO-ESGO-ESTRO consensus included LVSI (Lymph-vascular space invasion, LVSI) status as a risk stratification factor for stage I endometrioid endometrial cancer (EEC) patients and as one of the indications for adjuvant therapy. Furthermore, LVSI is included in the new FIGO staging of endometrial cancer (EC) in 2023. However, the data contribution of the Chinese population in this regard is limited. The present study aimed to further comfirm the influence of LVSI on the prognosis of early-stage low-grade EEC in a fifteen-year retrospective Chinese cohort study.

METHODS

This retrospective analysis cohort included 702 EEC patients who underwent TAH/BSO surgery, total abdominal hysterectomy, bilateral salpingooophorectomy in Peking University People's Hospital from 2006 to 2020. Patients were stratified based on LVSI expression status as: LVSI negative group and LVSI positive group. Clinical outcome measures related to LVSI, assessed with a univariate and multivariate Cox proportional hazards regression model.

RESULTS

702 EEC patients with stage I and grade 1-2 were analyzed. 58 patients (8.3%) were LVSI-positive and 14 patients (2.0%) was relapse. Recurrence rates in LVSI-negative and LVSI-positive were 1.6% and 6.9%, respectively. 5-year disease-free survival (DFS) rate in LVSI-negative and LVSI-positive were 98.4% and 93.1%, respectively. These rates for 5-year overall (OS) survival in LVSI-negative were 98.9% while it was 94.8% in LVSI-positive. Multivariate analysis showed that LVSI is an independent risk factor for 5-year DFS (HR = 4.60, p = 0.010). LVSI has a similar result for 5-year OS(HR = 4.39, p = 0.028).

CONCLUSIONS

LVSI is an independent predictor of relapse and poor prognosis in early-stage low-grade endometrioid endometrial cancer in the Chinese cohort.

Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer

This study aimed to identify factors that affect lymphovascular space invasion (LVSI) in endometrial cancer (EC) using machine learning technology, and to build a clinical risk assessment model based on these factors. Samples were collected from May 2017 to March 2022, including 312 EC patients who received treatment at Xuzhou Medical University Affiliated Hospital of Lianyungang. Of these, 219 cases were collected for the training group and 93 for the validation group. Clinical data and laboratory indicators were analyzed. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used to analyze risk factors and construct risk models. The LVSI and non-LVSI groups showed statistical significance in clinical data and laboratory indicators (P < 0.05). Multivariable logistic regression analysis identified independent risk factors for LVSI in EC, which were myometrial infiltration depth, cervical stromal invasion, lymphocyte count (LYM), monocyte count (MONO), albumin (ALB), and fibrinogen (FIB) (P < 0.05). LASSO regression identified 19 key feature factors for model construction. In the training and validation groups, the risk scores for the logistic and LASSO models were significantly higher in the LVSI group compared with that in the non-LVSI group (P < 0.001). The model was built based on machine learning and can effectively predict LVSI in EC and enhance preoperative decision-making. The reliability of the model was demonstrated by the significant difference in risk scores between LVSI and non-LVSI patients in both the training and validation groups.

Precise lymph node biopsy for endometrial cancer confined to the uterus: Analysis of 43 clinical cases

OBJECTIVE

To explore a precise association between tumor location and lymph node (LN) biopsy algorithm in uterine confined endometrial cancer (EC).

MATERIALS AND METHODS

Patients with EC treated in the Department of Obstetrics and Gynecology, South Branch of Fujian Provincial Hospital were included in this observational retrospective study. Based on the procedure of treatment, patients were separated to stage I (2015.07-2019.09) and stage II (2019.09-2021.9). In each stage, patients were separated to high and low-risk group by the predicted results. Patients in the high-risk group received systematic lymphadenectomy in stage I and sentinel lymph node (SLN) dissection in stage II. The efficiency of lymph node metastasis (LNM) detection rates was compared between stage I and stage II cases. Precise lymph node biopsy algorithm was also constructed based on the outcomes of stage II.

RESULTS

Overall, 43 patients, 28 in stage I and 15 in stage II, were included in the study. No recurrence or death cases had been found within follow-up terms. Based on the difference in the detection efficiency of LNM (p > 0.05), there was no difference between two stages. Thus, systematic lymphadenectomy and SLN biopsy provided similar success rates. The location of tumor site was also important for deciding whether pelvic or para-aortic SLN should be sampled for LNM.

CONCLUSIONS

Precise SLN biopsy for EC confined to the uterus showed comparable LNM detection rate as systematic lymphadenectomy. EC location may be used to determine whether pelvic or para-aortic SLN sampling should be conducted for treatment.

Predictive models for lymph node metastasis in endometrial cancer: A systematic review and bibliometric analysis

BACKGROUND

Lymph node metastasis is associated with a poorer prognosis in endometrial cancer.

OBJECTIVE

The objective was to synthesize and critically appraise existing predictive models for lymph node metastasis risk stratification in endometrial cancer.

DESIGN

This study is a systematic review.

DATA SOURCES AND METHODS

We searched the Web of Science for articles reporting models predicting lymph node metastasis in endometrial cancer, with a systematic review and bibliometric analysis conducted based upon which. Risk of bias was assessed by the Prediction model Risk Of BiAS assessment Tool (PROBAST).

RESULTS

A total of 64 articles were included in the systematic review, published between 2010 and 2023. The most common articles were "development only." Traditional clinicopathological parameters remained the mainstream in models, for example, serum tumor marker, myometrial invasion and tumor grade. Also, models based upon gene-signatures, radiomics and digital histopathological images exhibited an acceptable self-reported performance. The most frequently validated models were the Mayo criteria, which reached a negative predictive value of 97.1%-98.2%. Substantial variability and inconsistency were observed through PROBAST, indicating significant between-study heterogeneity. A further bibliometric analysis revealed a relatively weak link between authors and organizations on models predicting lymph node metastasis in endometrial cancer.

CONCLUSION

A number of predictive models for lymph node metastasis in endometrial cancer have been developed. Although some exhibited promising performance as they demonstrated adequate to good discrimination, few models can currently be recommended for clinical practice due to lack of independent validation, high risk of bias and low consistency in measured predictors. Collaborations between authors, organizations and countries were weak. Model updating, external validation and collaborative research are urgently needed.

REGISTRATION

None.

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Background

The early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.

Methods

A systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.

Results

This systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.

Conclusion

Although the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.