Comparison of three different risk-stratification models for predicting lymph node involvement in endometrioid endometrial cancer clinically confined to the uterus

LRP1B mutation is associated with lymph node metastasis in endometrial carcinoma: A clinical next-generation sequencing study

BACKGROUND

This study aims to investigate the mutation status and protein expression of low-density lipoprotein receptor-related protein 1B (LRP1B) in endometrial cancer, and analyze its association with lymph node metastasis (LNM) in endometrial cancer.

METHODS

Targeted next-generation sequencing (NGS) was conducted on both tumor tissues and paired blood DNA obtained from 94 endometrial cancer patients, followed by comprehensive analysis. Additionally, immunohistochemistry (IHC) was used to explore the correlation between LRP1B protein expression levels, its gene mutation status, and LNM.

RESULTS

LRP1B mutation was observed in 19 patients (20.2%). Our results revealed that LRP1B mutation frequencies were significantly different between endometrial cancer with or without LNM (P = 0.038). Multivariate analysis indicated that LRP1B mutation was a favorable predictor (odds ratio 0.09; 95% confidence interval 0.01-0.95; P = 0.045) for LNM in endometrial cancer. Further analysis revealed that combination of LRP1B mutation with clinical variants (LVSI and histological subtype) yielded a higher area under the curve value of 0.871) and patients harboring LRP1B mutated-type were less likely to develop LNM. On integrated analysis, the concordance between LRP1B NGS and LRP1B IHC was 73.3%.

CONCLUSIONS

This study utilizes targeted NGS to uncover the relationship between LRP1B mutation and LNM status, contributing to the development of primary prevention and proactive treatment strategies.

Preoperative prediction of lymph node metastasis in endometrial cancer patients via an intratumoral and peritumoral multiparameter MRI radiomics nomogram

Introduction

While lymph node metastasis (LNM) plays a critical role in determining treatment options for endometrial cancer (EC) patients, the existing preoperative methods for evaluating the lymph node state are not always satisfactory. This study aimed to develop and validate a nomogram based on intra- and peritumoral radiomics features and multiparameter magnetic resonance imaging (MRI) features to preoperatively predict LNM in EC patients.

Methods

Three hundred and seventy-four women with histologically confirmed EC were divided into training (n = 220), test (n = 94), and independent validation (n = 60) cohorts. Radiomic features were extracted from intra- and peritumoral regions via axial T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) mapping. A radiomics model (annotated as the Radscore) was established using the selected features from different regions. The clinical parameters were statistically analyzed. A nomogram was developed by combining the Radscore and the most predictive clinical parameters. Decision curve analysis (DCA) and the net reclassification index (NRI) were used to assess the clinical benefit of using the nomogram.

Results

Nine radiomics features were ultimately selected from the intra- and peritumoral regions via ADC mapping and T2WI. The nomogram combining the Radscore, serum CA125 level, and tumor area ratio achieved the highest AUCs in the training, test and independent validation sets (nomogram vs. Radscore vs. clinical model: 0.878 vs. 0.850 vs. 0.674 (training), 0.877 vs. 0.838 vs. 0.668 (test), and 0.864 vs. 0.836 vs. 0.618 (independent validation)). The DCA and NRI results revealed the nomogram had greater diagnostic performance and net clinical benefits than the Radscore alone.

Conclusion

The combined intra- and peritumoral region multiparameter MRI radiomics nomogram showed good diagnostic performance and could be used to preoperatively predict LNM in patients with EC.

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer

BACKGROUND

Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC.

AIM

To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.

METHODS

The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.

RESULTS

Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature.

CONCLUSION

The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

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.

Can the ADC Value Be Used as an Imaging "Biopsy" in Endometrial Cancer?

BACKGROUND

The tumor histological grade is closely related to the prognosis of patients with endometrial cancer (EC). Multiparametric MRI, including diffusion-weighted imaging (DWI), provides information about the cellular density that may be useful to differentiate between benign and malignant uterine lesions. However, correlations between apparent diffusion coefficient (ADC) values and histopathological grading in endometrial cancer remain controversial.

MATERIAL AND METHODS

We retrospectively evaluated 92 patients with endometrial cancers, including both endometrioid adenocarcinomas (64) and non-endometrioid adenocarcinomas (28). All patients underwent DWI procedures, and mean ADC values were calculated in a region of interest. These values were then correlated with the tumor grading offered by the histopathological examination, which was considered the gold standard. In this way, the patients were divided into three groups (G1, G2, and G3). The ADC values were then compared to the results offered by the biopsy to see if the DWI sequence and ADC map could replace this procedure. We also compared the mean ADC values to the myometrial invasion (50%) and lymphovascular space invasion.

RESULTS

We have divided the ADC values into three categories corresponding to three grades: >0.850 × 10-3 mm2/s (ADC1), 0.730-0.849 × 10-3 mm2/s (ADC2) and <0.730 × 10-3 mm2/s (ADC3). The diagnostic accuracy of the ADC value was 85.71% for ADC1, 75.76% for ADC2, and 91.66% for ADC3. In 77 cases out of 92, the category in which they were placed using the ADC value corresponded to the result offered by the histopathological exam with an accuracy of 83.69%. For only 56.52% of patients, the biopsy result included the grading system. For each grading category, the mean ADC value showed better results than the biopsy; for G1 patients, the mean ADC value had an accuracy of 85.71% compared to 66.66% in the biopsy, G2 had 75.76% compared to 68.42%, and G3 had 91.66 compared to 75%. For both deep myometrial invasion and lymphovascular space invasion, there is a close, inversely proportional correlation with the mean ADC value.

CONCLUSIONS

Mean endometrial tumor ADC on MR-DWI is inversely related to the histological grade, deep myometrial invasion and lymphovascular space invasion. Using this method, the patients could be better divided into risk categories for personalized treatment.

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.

Metastases and Recurrence Risk Factors in Endometrial Cancer-The Role of Selected Molecular Changes, Hormonal Factors, Diagnostic Methods and Surgery Procedures

The presence of metastatic endometrial cancer (EC) is a key problem in treatment failure associated with reduced overall survival rates. The most common metastatic location is the pelvic lymph nodes, and the least common is the brain. The presence of metastasis depends on many factors, including the molecular profile of cancer (according to the TCGA-Genome Atlas), the activity of certain hormones (estrogen, prolactin), and pro-inflammatory adipocytokines. Additionally, an altered expression of microRNAs affecting the regulation of numerous genes is also related to the spread of cancer. This paper also discusses the value of imaging methods in detecting metastases; the primary role is attributed to the standard transvaginal USG with the tumor-free distance (uTFD) option. The influence of diagnostic and therapeutic methods on EC spread is also described. Hysteroscopy, according to the analysis discussed above, may increase the risk of metastases through a fluid medium, mainly performed in advanced stages of EC. According to another analysis, laparoscopic hysterectomy performed with particular attention to avoiding risky procedures (trocar flushing, tissue traumatization, preserving a margin of normal tissue) was not found to increase the risk of EC dissemination.

BUB1, BUB1B, CCNA2, and CDCA8, along with miR-524-5p, as clinically relevant biomarkers for the diagnosis and treatment of endometrial carcinoma

BACKGROUND

Endometrial carcinoma (EC) is a malignant tumor of the female reproductive tract that has been associated with increased morbidity and mortality. This study aimed to identify biomarkers and potential therapeutic targets for EC.

METHODS

A publicly available transcriptome data set comprising 587 EC cases was subjected to a comprehensive bioinformatics analysis to identify candidate genes responsible for EC occurrence and development. Next, we used clinical samples and cell experiments for validation.

RESULTS

A total of 1,617 differentially expressed genes (DEGs) were identified. Analysis of patient survival outcomes revealed that BUB1, BUB1B, CCNA2, and CDCA8 were correlated with prognosis in patients with EC. Moreover, assessment of clinical samples confirmed that BUB1, BUB1B, CCNA2 and CDCA8 were strongly expressed in EC tissues. Additionally, bioinformatics and luciferase reporter assays confirmed that miR-524-5p can target and regulate these four genes. Overexpression of miR-524-5p significantly inhibited EC Ishikawa cells viability, migration and invasion. Inhibition of miR-524-5p showed the opposite results.

CONCLUSIONS

Expression of miR-524-5p reduced the migration and invasion of Ishikawa EC cells, and decreased BUB1, BUB1B, CCNA2, and CDCA8 expression. miR-524-5p, as well as BUB1, BUB1B, CCNA2, and CDCA8, may be clinically relevant biomarkers for the diagnosis and treatment of EC.

The Significance of International Federation of Gynecology and Obstetrics Grading in Microsatellite Instability-High and POLE-Mutant Endometrioid Endometrial Carcinoma

With the advancement of diagnostic molecular technology and the molecular classification of endometrial endometrioid carcinoma (EEC), it remains to be seen whether conventional International Federation of Gynecology and Obstetrics (FIGO) grading retains clinical significance in certain molecular subtypes of EECs. In this study, we explored the clinical significance of FIGO grading in microsatellite instability-high (MSI-H) and POLE-mutant EECs. A total of 162 cases of MSI-H EECs and 50 cases of POLE-mutant EECs were included in the analysis. Significant differences in tumor mutation burden (TMB), progression-free survival, and disease-specific survival were seen between the MSI-H and POLE-mutant cohorts. Within the MSI-H cohort, there were statistically significant differences in TMB and stage at presentation across FIGO grades, but not survival. Within the POLE-mutant cohort, there was significantly greater TMB with increasing FIGO grade, but there were no significant differences in stage or survival. In both the MSI-H and POLE-mutant cohorts, log-rank survival analysis showed no statistically significant difference in progression-free and disease-specific survival across FIGO grades. Similar findings were also seen when a binary grading system was utilized. Since FIGO grade was not associated with survival, we conclude that the intrinsic biology of these tumors, characterized by their molecular profile, may override the significance of FIGO grading.

A systematic review about the role of preoperative 18F-FDG PET/CT for prognosis and risk stratification in patients with endometrial cancer

OBJECTIVE

To analyse the available literature on the prognostic value of preoperative ¹⁸F-FDG PET/CT metabolic parameters and their usefulness in risk stratification in patients with endometrial cancer (EC).

MATERIAL AND METHODS

Pubmed searches used "(endometr* OR uter*) AND (PET OR FDG)" as keywords from January-2000 to June-2020. References in included articles were checked for possible publications not included in the first search. Studies evaluating the prognostic value of preoperative ¹⁸F-FDG PET/CT and its role for risk stratification in patients with EC were included. Non-original articles (reviews, editorials, letters, legal cases, interviews, case reports, etc.) were not included.

RESULTS

Twenty-six studies (1918 patients) were selected according to the inclusion criteria in this review. Thirteen studies (939 patients) related to the prognostic role of preoperative ¹⁸F-FDG PET/CT and 14 studies (1036 patients) related to its role in risk stratification were included. Parameters such as SUVmax, metabolic tumour volume (MTV) and total lesion glycolysis (TLG) of the primary tumour were analysed.

CONCLUSIONS

Preoperative SUVmax is useful for non-invasive diagnosis and for deciding the appropriate therapeutic strategy, as it could be used as an independent prognostic marker for recurrence and survival in EC. In addition, both preoperative VTM and GTL could be independent prognostic factors for predicting recurrence and survival, but there is still insufficient scientific evidence. The usefulness of SUVmax for risk stratification is limited (there is insufficient literature that ¹⁸F-FDG PET/CT can replace surgical staging), although VTM and GTL are more accurate and have a valuable role in risk stratification of EC. However, larger multicentre studies with adequate follow-up time are needed to confirm these findings.

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

Lymph node metastasis (LNM) is a well-established prognostic factor in endometrial cancer (EC). We aimed to construct a model that predicts LNM and prognosis using preoperative factors such as myometrial invasion (MI), enlarged lymph nodes (LNs), histological grade determined by endometrial biopsy, and serum cancer antigen 125 (CA125) level using two independent cohorts consisting of 254 EC patients. The area under the receiver operating characteristic curve (AUC) of the constructed model was 0.80 regardless of the machine learning techniques. Enlarged LNs and higher serum CA125 levels were more significant in patients with low-grade EC (LGEC) and LNM than in patients without LNM, whereas deep MI and higher CA125 levels were more significant in patients with high-grade EC (HGEC) and LNM than in patients without LNM. The predictive performance of LNM in the HGEC group was higher than that in the LGEC group (AUC = 0.84 and 0.75, respectively). Patients in the group without postoperative pathological LNM and positive LNM prediction had significantly worse relapse-free and overall survival than patients with negative LNM prediction (log-rank test, P < 0.01). This study showed that preoperative clinicopathological factors can predict LNM with high precision and detect patients with poor prognoses. Furthermore, clinicopathological factors associated with LNM were different between HGEC and LGEC patients.

Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: An NRG Oncology/Gynecologic Oncology Group Study

Objectives: A risk assessment model for metastasis in endometrioid endometrial cancer (EEC) was developed using molecular and clinical features, and prognostic association was examined. Methods: Patients had stage I, IIIC, or IV EEC with tumor-derived RNA-sequencing or microarray-based data. Metastasis-associated transcripts and platform-centric diagnostic algorithms were selected and evaluated using regression modeling and receiver operating characteristic curves. Results: Seven metastasis-associated transcripts were selected from analysis in the training cohorts using 10-fold cross validation and incorporated into an MS7 classifier using platform-specific coefficients. The predictive accuracy of the MS7 classifier in Training-1 was superior to that of other clinical and molecular features, with an area under the curve (95% confidence interval) of 0.89 (0.80-0.98) for MS7 compared with 0.69 (0.59-0.80) and 0.71 (0.58-0.83) for the top evaluated clinical and molecular features, respectively. The performance of MS7 was independently validated in 245 patients using RNA sequencing and in 81 patients using microarray-based data. MS7 + MI (myometrial invasion) was preferrable to individual features and exhibited 100% sensitivity and negative predictive value. The MS7 classifier was associated with lower progression-free and overall survival (p ≤ 0.003). Conclusion: A risk assessment classifier for metastasis and prognosis in EEC patients with primary tumor derived MS7 + MI is available for further development and optimization as a companion clinical support tool.

Sentinel lymph node biopsy in high-risk endometrial cancer: performance, outcomes, and future avenues

Endometrial cancer is the second most common gynecological malignancy worldwide, with an overall favorable prognosis. However, a subgroup of patients has a high risk of recurrence and poor prognosis. This review summarizes recently published articles that examined sentinel lymph node (SLN) biopsy in patients with high-risk endometrial cancer. We focused on the performance and outcomes of SLN biopsy, and examined potential methods for improving the management of this high-risk subset. Few studies have examined the long-term outcomes of SLN in patients with high-risk endometrial cancer. Thus, we reviewed recently published retrospective studies that have adopted statistical techniques, such as inverse probability weighting or propensity score matching, to examine the outcome of SLN biopsy compared to conventional lymphadenectomy. Potential avenues for future research to fine-tune decision making for this patient subgroup were also discussed.

Radiomics Nomogram in Assisting Lymphadenectomy Decisions by Predicting Lymph Node Metastasis in Early-Stage Endometrial Cancer

Background

Lymph node metastasis (LNM) is an important risk factor affecting treatment strategy and prognosis for endometrial cancer (EC) patients. A radiomics nomogram was established in assisting lymphadenectomy decisions preoperatively by predicting LNM status in early-stage EC patients.

Methods

A total of 707 retrospective clinical early-stage EC patients were enrolled and randomly divided into a training cohort and a test cohort. Radiomics features were extracted from MR imaging. Three models were built, including a guideline-recommended clinical model (grade 1-2 endometrioid tumors by dilatation and curettage and less than 50% myometrial invasion on MRI without cervical infiltration), a radiomics model (selected radiomics features), and a radiomics nomogram model (combing the selected radiomics features, myometrial invasion on MRI, and cancer antigen 125). The predictive performance of the three models was assessed by the area under the receiver operating characteristic (ROC) curves (AUC). The clinical decision curves, net reclassification index (NRI), and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of the clinical model and the radiomics nomogram were calculated.

Results

The predictive ability of the clinical model, the radiomics model, and the radiomics nomogram between LNM and non-LNM were 0.66 [95% CI: 0.55-0.77], 0.82 [95% CI: 0.74-0.90], and 0.85 [95% CI: 0.77-0.93] in the training cohort, and 0.67 [95% CI: 0.56-0.78], 0.81 [95% CI: 0.72-0.90], and 0.83 [95% CI: 0.74-0.92] in the test cohort, respectively. The decision curve analysis, NRI (1.06 [95% CI: 0.81-1.32]), and IDI (0.05 [95% CI: 0.03-0.07]) demonstrated the clinical usefulness of the radiomics nomogram.

Conclusions

The predictive radiomics nomogram could be conveniently used for individualized prediction of LNM and assisting lymphadenectomy decisions in early-stage EC patients.

Preoperative risk stratification in women with endometrial cancer: A comparison of contrast-enhanced MR imaging and diffusion-weighted MR imaging

PURPOSE

To compare CE MRI and DWI in the risk stratification of women with endometrial cancer for lymph node metastasis.

METHOD

Two readers independently assessed the degree of myometrial invasion on two separate occasions in a retrospective cohort of 84 women with endometrial cancers: once with CE MRI and standard anatomic sequences and another time with DWI and standard anatomic sequences. Participants were stratified according to their risk of lymph node metastasis following the European Society for Medical Oncology guidelines. The rate of lymph node metastasis was compared between the risk stratification groups obtained using CE MRI or DWI by generalized estimating equations.

RESULTS

In the low-risk group, the rate of lymph node metastasis was 1.9% (1/53) when using CE MRI and 1.9% (1/54) when using DWI for reader 1, and 3.8% (2/52) when using CE MRI and 1.9% (1/52) when using DWI for reader 2. The rate of lymph node metastasis in the high-risk group was 25.8% (8/31) when using CE MRI and 26.7% (8/30) when using DWI for reader 1, and 21.9% (7/32) when using CE MRI and 25.0% (8/32) when using DWI for reader 2. There was no significant difference between CE MRI and DWI in the rate of lymph node metastasis according to the risk stratification (p > .05 in both low- and high-risk groups for both readers).

CONCLUSION

DWI might be a comparable alternative to CE MRI in the preoperative risk stratification of women with endometrial cancer for lymph node metastasis.

Ultrasound Measurement of Tumor-Free Distance from the Serosal Surface as the Alternative to Measuring the Depth of Myometrial Invasion in Predicting Lymph Node Metastases in Endometrial Cancer

Background: Ultrasonography’s usefulness in endometrial cancer (EC) diagnosis consists in its roles in staging and prediction of metastasis. Ultrasound-measured tumor-free distance from the tumor to the uterine serosa (uTFD) is a promising marker for these diagnostic and prognostic variables. The aim of the study was to determine the usefulness of this biomarker in locoregional staging, and thus in the prediction of lymph node metastasis (LNM). Methods: We conducted a single-institutional, prospective study on 116 consecutive patients with EC who underwent 2D transvaginal ultrasound examination. The uTFD marker was compared with the depth of ultrasound-measured myometrial invasion (uMI). Univariable and multivariable logit models were evaluated to assess the predictive power of the uTFD and uMI in regard to LNM. The reference standard was a final histopathology result. Survival was assessed by the Kaplan–Meier method. Results: LNM was found in 17% of the patients (20/116). In the univariable analysis, uMI and uTFD were significant predictors of LNM. The accuracy was 70.7%, and the NPV was 92.68% (OR 4.746, 95% CI 1.710–13.174) for uMI (p = 0.002), and they were 63.8% and 89.02% (OR 0.842, 95% CI 0.736–0.963), respectively, for uTFD (p = 0.01). The cutoff value for uTFD in the prediction of LNM was 5.2 mm. The association between absence of LNM and biomarker values of uMI < 1/2 and uTFD ≥ 5.2 mm was greater than that between the presence of metastases and uMI > 1/2 and uTFD values <5.2 mm. In the multivariable analysis, the accuracy of the uMI–uTFD model was 74%, and its NPV was 90.24% (p = non-significant). Neither uMI nor uTFD were surrogates for overall and recurrence-free survivals in endometrial cancer. Conclusions: Both uMI and uTFD, either alone or in combination, were valuable tools for gaining additional preoperative information on expected lymph node status. Negative lymph nodes status was better described by ultrasound biomarkers than a positive status. It was easier to use the uTFD rather than the uMI measurement as a biomarker of EC invasion, and the former still maintained a similar predictive value for lymph node metastases to the latter at diagnosis.

Pre-surgical staging in endometrial cancer: An opportunity for risk stratification and triage?

BACKGROUND

Endometrial cancer (EC) is treated by comprehensive surgical staging that includes a systematic lymphadenectomy. The low rates of lymph node metastasis (LNM) in early stages question the benefit of routine lymphadenectomy in low-risk disease, but the absence of a reliable method to identify these patients in whom lymphadenectomy could be omitted makes complete staging the standard of care. This study evaluated a method of preoperative staging in EC to identify patients at low risk of LNM and adjuvant treatment.

METHODS

This prospective observational study compared the presurgical staging and risk triage based on endometrial biopsy (EB) and imaging (magnetic resonance imaging [MRI], Positron Emission Tomography [PET] scan) in 94 cases of EC with the final surgicopathological staging and evaluated the role of each modality in presurgical evaluation and triage.

RESULTS

Ninety-four cases were triaged into 42 low-risk and 52 non-low-risk cases preoperatively. EB showed a sensitivity, specificity, and accuracy of 51.55%, 89.83%, and 75.53%, respectively, in identifying high-risk grade and histology. MRI was effective for local staging and identified tumor size, myometrial invasion, and cervical involvement with accuracy ranging from 82.20% to 97.78% for these parameters. MRI detected LNM with an accuracy of 85.11%, whereas PET exhibited an accuracy of 86.17%. The combined presurgical staging could identify low-risk disease with a sensitivity, specificity, and accuracy of 85.37%, 86.79%, and 86.17%, respectively.

CONCLUSION

Preoperative staging may triage patients into low-risk and non-low-risk cases, thereby facilitating a conscious decision to omit lymphadenectomy in low-risk cases, thus avoiding unnecessary morbidity without compromising oncological safety.

Computer-Aided Segmentation and Machine Learning of Integrated Clinical and Diffusion-Weighted Imaging Parameters for Predicting Lymph Node Metastasis in Endometrial Cancer

Simple Summary

Computer-aided segmentation and machine learning added values of clinical parameters and diffusion-weighted imaging radiomics for predicting nodal metastasis in endometrial cancer, with a diagnostic performance superior to criteria based on lymph node size or apparent diffusion coefficient.

Abstract

Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10⁻³ mm²/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10⁻²), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria.

MRI-Based Radiomic Model for Preoperative Risk stratification in Stage I Endometrial Cancer

Introduction: Preoperative risk stratification is crucial for clinical treatment of endometrial cancer (EC). This study aimed to establish a model based on magnetic resonance imaging (MRI) and clinical factors for risk classification of EC.

Materials and Methods: A total of 102 patients with pathologically proven Stage I EC were included. Preoperative MRI examinations were performed in all the patients. 720 radiomic features were extracted from T2-weighted images. Least absolute shrinkage and selection operator (LASSO) regression model was performed to reduce irrelevant features. Logistic regression was used to build clinical, radiomic and combined predictive models. A nomogram was developed for clinical application.

Results: The radiomic model has a better performance than the model based on clinical and conventional MRI characteristics [AUC of 0.946 (95% CI: 0.882-0.973) vs AUC of 0.756 (95% CI: 0.65, 0.86)]. The combined model consisting of radiomic features and tumor size showed the best predictive performance in the training cohort with AUC of 0.955 in the training and 0.889 in the validation cohorts.

Conclusions: MRI-based radiomic model has great potential in prediction of low-risk ECs.

Prediction of lymphovascular space invasion in patients with endometrial cancer

Objective: Predict the presence of lymphovascular space invasion (LVSI), using uterine factors such as tumor diameter (TD), grade, and depth of myometrial invasion (MMI). Develop a predictive model that could serve as a marker of LVSI in women with endometrial cancer (EC). Methods: Data from 888 patients with endometrioid EC who were treated between January 2009 and December 2018 were reviewed. The patients' data were retrieved from six institutions. We assessed the differences in the clinicopathological characteristics between patients with and without LVSI. We performed logistic regression analysis to determine which clinicopathological characteristics were the risk factors for positive LVSI status and to estimate the odds ratio (OR) for each covariate. Using the risk factors and OR identified through this process, we created a model that could predict LVSI and analyzed it further using receiver operating characteristic curve analysis. Results: In multivariate logistic regression analysis, tumor size (P = 0.027), percentage of MMI (P < 0.001), and presence of cervical stromal invasion (P = 0.002) were identified as the risk factors for LVSI. Based on the results of multivariate logistic regression analysis, we developed a simplified LVSI prediction model for clinical use. We defined the "LVSI index" as "TD×%MMI×tumor grade×cervical stromal involvement." The area under curve was 0.839 (95% CI= 0.809-0.869; sensitivity, 74.1%; specificity, 80.5%; negative predictive value, 47.3%; positive predictive value, 8.6%; P < 0.001), and the optimal cut-off value was 200. Conclusion: Using the modified risk index of LVSI, it is possible to predict the presence of LVSI in women with endometrioid endometrial cancer. Our prediction model may be an appropriate tool for integration into the clinical decision-making process when assessed either preoperatively or intraoperatively.

Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer

OBJECTIVES

To compare the diagnostic accuracy of preoperative 18F-FDG PET/CT and MRI tumor markers for prediction of lymph node metastases (LNM) and aggressive disease in endometrial cancer (EC).

METHODS

Preoperative whole-body 18F-FDG PET/CT and pelvic MRI were performed in 215 consecutive patients with histologically confirmed EC. PET/CT-based tumor standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and PET-positive lymph nodes (LNs) (SUVmax > 2.5) were analyzed together with the MRI-based tumor volume (VMRI), mean apparent diffusion coefficient (ADCmean), and MRI-positive LN (maximum short-axis diameter ≥ 10 mm). Imaging parameters were explored in relation to surgicopathological stage and tumor grade. Receiver operating characteristic (ROC) curves were generated yielding optimal cutoff values for imaging parameters, and regression analyses were used to assess their diagnostic performance for prediction of LNM and progression-free survival.

RESULTS

For prediction of LNM, MTV yielded the largest area under the ROC curve (AUC) (AUC = 0.80), whereas VMRI had lower AUC (AUC = 0.72) (p = 0.03). Furthermore, MTV > 27 ml yielded significantly higher specificity (74%, p < 0.001) and accuracy (75%, p < 0.001) and also higher odds ratio (12.2) for predicting LNM, compared with VMRI > 10 ml (58%, 62%, and 9.7, respectively). MTV > 27 ml also tended to yield higher sensitivity than PET-positive LN (81% vs 50%, p = 0.13). Both VMRI > 10 ml and MTV > 27 ml were significantly associated with reduced progression-free survival.

CONCLUSIONS

Tumor markers from 18F-FDG PET/CT outperform MRI markers for the prediction of LNM. MTV > 27 ml yields a high diagnostic performance for predicting aggressive disease and represents a promising supplement to conventional PET/CT reading in EC.

KEY POINTS

• Metabolic tumor volume (MTV) outperforms other 18F-FDG PET/CT and MRI markers for preoperative prediction of lymph node metastases (LNM) in endometrial cancer patients. • Using cutoff values for tumor volume for prediction of LNM, MTV > 27 ml yielded higher specificity and accuracy than VMRI> 10 ml. • MTV represents a promising supplement to conventional PET/CT reading for predicting aggressive disease in EC.

Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer

Introduction: Assessment of lymph node metastasis (LNM) is crucial for treatment decision and prognosis prediction for endometrial cancer (EC). However, the sensitivity of the routinely used magnetic resonance imaging (MRI) is low in assessing normal-sized LNM (diameter, 0-0.8 cm). We aimed to develop a predictive model based on magnetic resonance (MR) images and clinical parameters to predict LNM in normal-sized lymph nodes (LNs). Materials and Methods: A total of 200 retrospective patients were enrolled and divided into a training cohort (n = 140) and a test cohort (n = 60). All patients underwent preoperative MRI and had pathological result of LNM status. In total, 4,179 radiomic features were extracted. Four models including a clinical model, a radiomic model, and two combined models were built. Area under the receiver operating characteristic (ROC) curves (AUC) and calibration curves were used to assess these models. Subgroup analysis was performed according to LN size. All patients underwent surgical staging and had pathological results. Results: All of the four models showed predictive ability in LNM. One of the combined models, ModelCR1, consisting of radiomic features, LN size, and cancer antigen 125, showed the best discrimination ability on the training cohort [AUC, 0.892; 95% confidence interval [CI], 0.834-0.951] and test cohort (AUC, 0.883; 95% CI, 0.786-0.980). The subgroup analysis showed that this model also indicated good predictive ability in normal-sized LNs (0.3-0.8 cm group, accuracy = 0.846; <0.3 cm group, accuracy = 0.849). Furthermore, compared with the routinely preoperative MR report, the sensitivity and accuracy of this model had a great improvement. Conclusions: A predictive model was proposed based on MR radiomic features and clinical parameters for LNM in EC. The model had a good discrimination ability, especially for normal-sized LNs.

A proposal for a new classification of "unfavorable risk criteria" in patients with stage I endometrial cancer

BACKGROUND

Randomized trials describe differing sets of high-intermediate risk criteria.

OBJECTIVE

To use the National Cancer Database to compare the impact of radiation therapy in patients with stage I endometrial cancer meeting different criteria, and define a classification of "unfavorable risk."

METHODS

Patients with stage I endometrial cancer between January 2010 and December 2014 were identified in the National Cancer Database and stratified into two cohorts: (1) patients meeting Gynecologic Oncology Group (GOG)-99 criteria only for high-intermediate risk, but not Post-Operative Radiation Therapy in Endometrial Carcinoma (PORTEC)-1 criteria and (2) those meeting PORTEC-1 criteria only. High-risk stage I patients with both FIGO stage IB (under FIGO 2009 staging) and grade 3 disease were excluded. In each cohort, propensity score-matched survival analyses were performed. Based on these analyses, we propose a new classification of unfavorable risk. We then analyzed the association of adjuvant radiation with survival, stratified by this classification.

RESULTS

We identified 117,272 patients with stage I endometrial cancer. Of these, 11,207 patients met GOG-99 criteria only and 5,920 patients met PORTEC-1 criteria only. After propensity score matching, adjuvant radiation therapy improved survival (HR=0.73; 95% CI 0.60 to 0.89; p=0.002) in the GOG-99 only cohort. However, there was no benefit of adjuvant radiation (HR=0.89; 95% CI 0.69 to 1.14; p=0.355) in the PORTEC-1 only cohort. We, therefore, defined unfavorable risk stage I endometrial cancer as two or more of the following risk factors: lymphovascular invasion, age ≥70, grade 2-3 disease, and FIGO stage IB. Adjuvant radiation improved survival in stage I patients with adverse risk factors (HR=0.74; 95% CI 0.68 to 0.80; p<0.001), but not in other stage I patients (HR=1.02; 95% CI 0.91 to 1.15; p=0.710; p interaction <0.001).

CONCLUSION

Our study showed that adjuvant radiation was associated with an overall survival benefit in patients meeting GOG-99 criteria only; however, no survival benefit was seen in patients meeting PORTEC-1 criteria only. We propose a definition of unfavorable risk stage I endometrial cancer: ≥2 risk factors from among lymphovascular invasion, age ≥70, grade 2-3 disease, and FIGO stage IB disease.

Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer

Background and Objective

Endometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes.

Methods and Results

An mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p < 0.001) and time-dependent receiver-operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729, and 0.647 for 1, 3, 5, and 10-year overall survival (OS) prediction, respectively.

Conclusion

This study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction.