A nomogram prediction model for lymph node metastasis in endometrial cancer patients

Advancing Tailored Treatments: A Predictive Nomogram, Based on Ultrasound and Laboratory Data, for Assessing Nodal Involvement in Endometrial Cancer Patients

Assessing lymph node metastasis is crucial in determining the optimal therapeutic approach for endometrial cancer (EC). Considering the impact of lymphadenectomy, there is an urgent need for a cost-effective and easily applicable method to evaluate the risk of lymph node metastasis in cases of sentinel lymph node (SLN) biopsy failure. This retrospective monocentric study enrolled EC patients, who underwent surgical staging with nodal assessment. Data concerning demographic, clinicopathological, ultrasound, and surgical characteristics were collected from medical records. Ultrasound examinations were conducted in accordance with the IETA statement. We identified 425 patients, and, after applying exclusion criteria, the analysis included 313 women. Parameters incorporated into the nomogram were selected via univariate and multivariable analyses, including platelet count, myometrial infiltration, minimal tumor-free margin, and CA 125. The nomogram exhibited good accuracy in predicting lymph node involvement, with an AUC of 0.88. Using a cutoff of 10% likelihood of nodal involvement, the nomogram displayed a low false-negative rate of 0.04 (95% CI 0.00-0.19) in the training set. The adaptability of this straightforward model renders it suitable for implementation across diverse clinical settings, aiding gynecological oncologists in preoperative patient evaluations and facilitating the design of personalized treatments. However, external validation is mandatory for confirming diagnostic accuracy.

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.

Triage method for endometrial biopsy in postmenopausal women: a multicenter retrospective cohort study

OBJECTIVE

To identify the optimal triage procedure for endometrial biopsies in postmenopausal women.

METHODS

The clinical information of 470 postmenopausal women with endometrial biopsy results and postmenopausal bleeding (PMB) and/or transvaginal ultrasonography (TVU) abnormalities were collected at the gynecology departments of four general hospitals from March 2021 to March 2022. In the validation cohort, 112 women with TVU abnormalities who underwent endometrial biopsy at Xiangya hospital between May 2022 and May 2023 were enrolled. The endpoint was the final diagnosis based on hysteroscopy reports and biopsy pathology results. The sensitivity, specificity, positive predictive value, and negative predictive value were compared among the three triage methods. A nomogram prediction model was developed and validated.

RESULTS

Referring women with TVU abnormalities for endometrial biopsy identified 100% malignant/premalignant lesions despite low specificity (19.7%). Among women with measurable endometrial thickness (ET), we suggest that the ET cutoff value for biopsy referral should be ≥4 mm. The PMB (odds ratio [OR], 3.241; 95% confidence interval [CI], 1.073-9.789), diabetes (OR, 10.915; 95% CI, 3.389-35.156), and endometrial thickness (OR, 1.277; 95% CI, 1.156-1.409) were independent predictive factors for endometrial (pre)malignancy. A nomogram prediction model was constructed (area under curve [AUC] = 0.802, 95% CI: 0.715 to 0.889). The ideal cutoff point was 22.5, with a sensitivity of 100.0% and a specificity of 15.7%. The external validation achieved an AUC of 0.798 (95% CI, 0.685-0.911).

CONCLUSIONS

It was possible to refer all postmenopausal women with TVU abnormity (ET ≥ 4 mm or other abnormal findings) for endometrial biopsy. Among women with TVU abnormalities, a nomogram was constructed, and a score greater than 22.5 suggested the need for referral for endometrial biopsy, while a score less than 22.5 suggested that regular follow-up was required, further improving the triage procedure.

Analysis of prognostic factors for cervical mucinous adenocarcinoma and establishment and validation a nomogram: a SEER-based study

Up to now, there are no relevant studies on prognostic factors of cervical mucinous adenocarcinoma. Therefore, we explored the prognostic factors for cervical mucinous adenocarcinoma, and established and validated the prognostic model using the SEER database. We selected the independent factors through univariate and multivariate analyses. LASSO regression analysis was conducted to identify potential risk factors. In conjunction with LASSO and multivariate analysis, the nomogram incorporated three variables, including age, tumour size, and AJCC stage for OS. The c-index was 0.794 and 0.831 in development and validated cohorts, indicating that this prediction model showed adequate discriminative ability in the development cohort. Besides, calibration curves showed good concordance for the development cohort, as well as the validation cohort. We constructed a first-of-its-kind nomogram to predict cervical mucinous adenocarcinomas OS and it showed better performance than AJCC and FIGO stages. Patients with cervical mucinous adenocarcinoma might benefit from using this model to develop tailored treatments.IMPACT STATEMENTWhat is already known on this subject? Cervical cancer has a variety of pathological types. The biological behaviour of each type is different, and the prognosis is quite different.What do the results of this study add? We analysed and explored the relevant factors affecting the prognosis of cervical mucinous adenocarcinoma.What are the implications of these findings for clinical practice and/or further research? Through the analysis of the SEER dataset, the prognostic factors affecting cervical mucinous adenocarcinoma were identified, and the first predictive model was created to predict the prognosis to help doctors develop individualised treatment plans and follow-up plans.

Investigating the influence of primary uterine tumor site on pelvic and para-aortic lymph node metastatic pattern and evaluating the risk factors for lymph node metastases in endometrial carcinoma: A retrospective study

To assess the metastatic pattern in pelvic and para-aortic lymph nodes in relation with the primary uterine tumor site and to evaluate risk factors for lymph node metastases. 212 patients with endometrial cancer who underwent surgical treatment from December 2014 to December 2019 were selected. The clinical and pathological data were retrospectively analyzed. The factors and uterine primary tumor site related to lymph node metastasis were analyzed by univariate and multivariate analysis. Among the 212 patients with endometrial cancer, 17 cases had lymph node metastasis, and thus the metastasis rate was 8.02%. Univariate analysis revealed that lymph node metastasis was significantly correlated with Federation of Gynecology and Obstetrics stage, depth of myometrial invasion, tumor size, pathological grade, and lymphovascular space invasion (P < .05) and was not correlated with age, pathological type, and cervical involvement (P > .05). Primary uterine tumor site (fundus, horns, body or lower uterine segment) with or without cervical involvement was associated with different lymph nodes' metastatic sites. The lymph node metastatic pathways of endometrial cancer mainly include obturator lymph nodes and para-aortic lymph nodes, and skip metastasis may occur; endometrial carcinoma may jump and metastasize to para-aortic lymph nodes, specially when the lesion is located in the uterine fundus and uterine horns (cornua of uterus); there is a significant correlation between the location of lymph node metastasis and the location of primary uterine malignant tumor.

Correlation of several forms of folic acid with endometrial cancer: cross-sectional data from the National Health and Nutrition Examination Surveys (NHANES) 2011-2018

OBJECTIVE

Endometrial cancer (EC) is a common malignancy of the female reproductive system and although most patients have a good prognosis, 20-30% of patients with advanced disease have a poor prognosis. There are currently no reliable biomarkers for early diagnosis and effective prognostic improvement of the disease. The purpose of this study was to explore the correlation between different forms of folic acid and endometrial cancer.

METHODS

This study included 8809 female subjects aged ≥ 20 years in the NHANES database from 2011 to 2018, including 8738 non-oncology patients and 71 EC patients. Selection bias was reduced using 1:1 propensity score matching (PSM) method. Restricted cubic spline (RCS) was plotted to explore the non-linear relationship between different forms of folic acid and EC.

RESULT

Using data from the NHANES database from 2011 to 2018, the association between folic acid and the risk of developing EC was assessed. The results of the 1:1 ratio propensity score matching (PSM) showed 68 each for EC patients and non-oncology participants. Total serum folate, 5-methyltetrahydrofolate (5-methylTHF), 5-formyltetrahydrofolate (5-formylTHF), tetrahydrofolate (THF) and 5,10-methylenetetrahydrofolate (5,10-methenylTHF) were significantly correlated with EC (p < 0.05). In addition, the RCS showed a significant non-linear correlation between THF and 5,10-formyl THF and the risk of developing EC.

CONCLUSION

The results of this study showed that changes in serum total folate, 5-methylTHF, 5-formylTHF, THF and 5,10-methenylTHF were related to EC.

A Nomogram for Predicting Progression-free Survival in Patients with Endometrial Cancer

AIMS

Endometrial cancer is one of the most widely known gynaecological malignancies that lacks a prognostic prediction model. This study aimed to develop a nomogram to predict progression-free survival (PFS) in patients with endometrial cancer.

MATERIALS AND METHODS

Information for endometrial cancer patients diagnosed and treated from 1 January 2005 to 30 June 2018 was collected. The Kaplan-Meier survival analysis and multivariate Cox regression analysis were carried out to determine the independent risk factors and a nomogram was constructed by R based on analytical factors. Internal and external validation were then carried out to predict the probability of 3- and 5-year PFS.

RESULTS

In total, 1020 patients with endometrial cancer were included in the study and the relationship between 25 factors and prognosis was analysed. Postmenopause (hazard ratio = 2.476, 95% confidence interval 1.023-5.994), lymph node metastasis (hazard ratio = 6.242, 95% confidence interval 2.815-13.843), lymphovascular space invasion (hazard ratio = 4.263, 95% confidence interval 1.802-10.087), histological type (hazard ratio = 2.713, 95% confidence interval 1.374-5.356), histological differentiation (hazard ratio = 2.601, 95% confidence interval 1.141-5.927) and parametrial involvement (hazard ratio = 3.596, 95% confidence interval 1.622-7.973) were found to be independent prognostic risk factors; these factors were selected to establish a nomogram. The consistency index for 3-year PFS were 0.88 (95% confidence interval 0.81-0.95) in the training cohort and 0.93 (95% confidence interval 0.87-0.99) in the verification set. The areas under the receiver operating characteristic curve for the 3- and 5-year PFS predictions are 0.891 and 0.842 in the training set; the same conclusion also appeared in the verification set [0.835 (3-year), 0.803(5-year)].

CONCLUSIONS

This study established a prognostic nomogram for endometrial cancer that provides a more individualised and accurate estimation of PFS for patients, which will help physicians make follow-up strategies and risk stratification.

TMEM41A overexpression correlates with poor prognosis and immune alterations in patients with endometrial carcinoma

BACKGROUND

Expression levels of transmembrane protein 41A (TMEM41A) are related to the progression of malignant tumors. However, the association between TMEM41A expression and endometrial carcinoma (EC) remains unclear. This study aims to identify the roles of TMEM41A expression in the prognosis of patients with EC and its correlation with EC progression.

METHODS

The TMEM41A expression and its correlation with the survival of patients with EC were assessed. Cox regression analysis was used to identify the prognostic factors, while nomograms were used to examine the association between the prognostic factors and the survival of patients with EC. Finally, the link between TMEM41A level and immune microenvironment and RNA modifications was investigated in EC.

RESULTS

TMEM41A was overexpressed in EC. TMEM41A overexpression could diagnose the EC and evaluate the poor prognosis of patients. Overexpression of TMEM41A was associated with clinical stage, age, weight, histological subtype, tumor grade, and survival status of patients with EC. Clinical stage, age, tumor grade, radiotherapy, and TMEM41A overexpression were factors of poor prognosis in patients with EC. The nomograms revealed the correlation between the TMEM41A level and survival time of patients with EC at 1, 3, and 5 years. Furthermore, TMEM41A overexpression was significantly correlated with the level of the stromal score, immune score, estimate score, NK CD56 bright cells, iDC, NK cells, eosinophils, pDC, T cells, TReg, cytotoxic cells, mast cells, Th17 cells, neutrophils, aDC, NK CD56 dim cells, TFH, Th2 cells, CD8 T cells, macrophages, immune cell markers, and RNA modifications.

CONCLUSIONS

TMEM41A is overexpressed in EC tissues and is associated with the prognosis, immune microenvironment, and RNA modification. Our preliminary studies indicate that overexpression of TMEM41A can potentially serve as a biomarker for EC treatment.

Metabolic syndrome score as an indicator in a predictive nomogram for lymph node metastasis in endometrial cancer

BACKGROUND

Lymph node metastasis (LNM) is an important factor affecting endometrial cancer (EC) prognosis. Current controversy exists as to how to accurately assess the risk of lymphatic metastasis. Metabolic syndrome has been considered a risk factor for endometrial cancer, yet its effect on LNM remains elusive. We developed a nomogram integrating metabolic syndrome indicators with other crucial variables to predict lymph node metastasis in endometrial cancer.

METHODS

This study is based on patients diagnosed with EC in Peking University People's Hospital between January 2004 and December 2020. A total of 1076 patients diagnosed with EC and who underwent staging surgery were divided into training and validation cohorts according to the ratio of 2:1. Univariate and multivariate logistic regression analyses were used to determine the significant predictive factors.

RESULTS

The prediction nomogram included MSR, positive peritoneal cytology, lymph vascular space invasion, endometrioid histological type, tumor size >  = 2 cm, myometrial invasion >  = 50%, cervical stromal invasion, and tumor grade. In the training group, the area under the curve (AUC) of the nomogram and Mayo criteria were 0.85 (95% CI: 0.81-0.90) and 0.77 (95% CI: 0.77-0.83), respectively (P < 0.01). In the validation group (N = 359), the AUC was 0.87 (95% CI: 0.82-0.93) and 0.80 (95% CI: 0.74-0.87) for the nomogram and the Mayo criteria, respectively (P = 0.01). Calibration plots revealed the satisfactory performance of the nomogram. Decision curve analysis showed a positive net benefit of this nomogram, which indicated clinical value.

CONCLUSION

This model may promote risk stratification and individualized treatment, thus improving the prognosis.

A preoperative nomogram model for the prediction of lymph node metastasis in buccal mucosa cancer

OBJECTIVES

We sought to construct a nomogram model predicting lymph node metastasis (LNM) in patients with squamous cell carcinoma of the buccal mucosa based on preoperative clinical characteristics.

METHODS

Patients who underwent radical resection of a primary tumor in the buccal mucosa with neck dissection were enrolled. Clinical characteristics independently associated with LNM in multivariate analyses were adopted to build the model. Patients at low risk of LNM were defined by a predicted probability of LNM of less than 5%.

RESULTS

Patients who underwent surgery in an earlier period (January 2015-November 2019) were defined as the model development cohort (n = 325), and those who underwent surgery later (November 2019-March 2021) were defined as the validation cohort (n = 140). Age, tumor differentiation, tumor thickness, and clinical N stage assessed by computed tomography/magnetic resonance imaging (cN) were independent predictors of LNM. The nomogram model based on these four predictors showed good discrimination accuracy in both the model development and validation cohorts, with areas under the receiver-operating characteristic curve (AUC) of 0.814 and 0.828, respectively. LNM prediction by the nomogram model was superior to cN in AUC comparisons (0.815 vs. 0.753) and decision curve analysis of the whole cohort. Seventy-one patients were defined as having a low risk of LNM, among whom the actual metastasis rate was only 1.4%.

CONCLUSIONS

A robust nomogram model for preoperative LNM prediction is built.

Analysis of prognostic factors of metastatic endometrial cancer based on surveillance, epidemiology, and end results database

Objective

To explore the risk factors for survival and prognosis of patients with metastatic endometrial cancer and to build and verify a reliable prediction model.

Methods

We retrospectively analyzed patients diagnosed with metastatic endometrial cancer in the US Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2015. Univariate and multivariate Cox regression analyses were used to assess clinical variables impact on survival and to construct nomograms. The results of the consistency index (C-index), subject operating characteristic (ROC) curve, and calibration curve were used to evaluate the predictive ability of the nomogram.

Results

This study included 3,878 patients with metastatic endometrial cancer. In the univariate analysis, variables associated with overall survival (OS) and cancer-specific survival (CSS) included age, race, marital status, pathological type, pathological grade, T-stage, N-stage, surgery, radiotherapy, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. In the multivariate analysis, age, race, pathological type, pathological grade, T-stage, N-stage, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, and lung metastasis were independent risk factors for OS and CSS (all P < 0.05). Combined with the results of the multiple factors, the 1-, 3-, 5-, and 8-year nomograms were constructed. For OS and CSS, T-stage had the greatest impact on the adverse prognosis of patients with metastatic endometrial cancer. The C-indexes of the OS and CSS nomograms in the training cohort were 0.749 (95% CI, 0.739-0.760) and 0.746 (95% CI, 0.736-0.756), respectively. The C-indices of OS and CSS in the validation cohort were 0.730 (95% CI, 0.714-0.746) and 0.728 (95% CI, 0.712-0.744), respectively. The ROC curve revealed our model's good prediction accuracy and clinical practicability. The calibration curve also confirmed the consistency between the model and actual existence. The Kaplan-Meier curves revealed statistically significant differences between the risk subgroups (P < 0.05).

Conclusion

Our SEER-based nomograms for predicting survival in patients with metastatic endometrial cancer were helpful for the clinical evaluation of patient prognosis.

Cervical lymph node metastasis prediction of postoperative papillary thyroid carcinoma before 131I therapy based on clinical and ultrasound characteristics

BACKGROUND

The status of lymph nodes is crucial to determine the dose of radioiodine-131(¹³¹I) for postoperative papillary thyroid carcinoma (PTC). We aimed to develop a nomogram for predicting residual and recurrent cervical lymph node metastasis (CLNM) in postoperative PTC before ¹³¹I therapy.

METHOD

Data from 612 postoperative PTC patients who underwent ¹³¹I therapy from May 2019 to December 2020 were retrospectively analyzed. Clinical and ultrasound features were collected. Univariate and multivariate logistic regression analyses were performed to determine the risk factors of CLNM. Receiver operating characteristic (ROC) analysis was used to weigh the discrimination of prediction models. To generate nomograms, models with high area under the curves (AUC) were selected. Bootstrap internal validation, calibration curves and decision curves were used to assess the prediction model's discrimination, calibration, and clinical usefulness.

RESULTS

A total of 18.79% (115/612) of postoperative PTC patients had CLNM. Univariate logistic regression analysis found serum thyroglobulin (Tg), serum thyroglobulin antibodies (TgAb), overall ultrasound diagnosis and seven ultrasound features (aspect transverse ratio, cystic change, microcalcification, mass hyperecho, echogenicity, lymphatic hilum structure and vascularity) were significantly associated with CLNM. Multivariate analysis revealed higher Tg, higher TgAb, positive overall ultrasound and ultrasound features such as aspect transverse ratio ≥ 2, microcalcification, heterogeneous echogenicity, absence of lymphatic hilum structure and abundant vascularity were independent risk factors for CLNM. ROC analysis showed the use of Tg and TgAb combined with ultrasound (AUC = 0.903 for "Tg+TgAb+Overall ultrasound" model, AUC = 0.921 for "Tg+TgAb+Seven ultrasound features" model) was superior to any single variant. Nomograms constructed for the above two models were validated internally and the C-index were 0.899 and 0.914, respectively. Calibration curves showed satisfied discrimination and calibration of the two nomograms. DCA also proved that the two nomograms were clinically useful.

CONCLUSION

Through the two accurate and easy-to-use nomograms, the possibility of CLNM can be objectively quantified before ¹³¹I therapy. Clinicians can use the nomograms to evaluate the status of lymph nodes in postoperative PTC patients and consider a higher dose of ¹³¹I for those with high scores.

Development and Validation of a Nomogram Based on Metabolic Risk Score for Assessing Lymphovascular Space Invasion in Patients with Endometrial Cancer

OBJECTIVE

This study assessed the predictive value of the metabolic risk score (MRS) for lymphovascular space invasion (LVSI) in endometrial cancer (EC) patients.

METHODS

We included 1076 patients who were diagnosed with EC between January 2006 and December 2020 in Peking University People's Hospital. All patients were randomly divided into the training and validation cohorts in a ratio of 2:1. Data on clinicopathological indicators were collected. Univariable and multivariable logistic regression analysis was used to define candidate factors for LVSI. A backward stepwise selection was then used to select variables for inclusion in a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical usefulness.

RESULTS

Independent predictors of LVSI included differentiation grades (G2: OR = 1.800, 95% CI: 1.050-3.070, p = 0.032) (G3: OR = 3.49, 95% CI: 1.870-6.520, p < 0.001), histology (OR = 2.723, 95% CI: 1.370-5.415, p = 0.004), MI (OR = 4.286, 95% CI: 2.663-6.896, p < 0.001), and MRS (OR = 1.124, 95% CI: 1.067-1.185, p < 0.001) in the training cohort. A nomogram was established to predict a patient's probability of developing LVSI based on these factors. The ROC curve analysis showed that an MRS-based nomogram significantly improved the efficiency of diagnosing LVSI compared with the nomogram based on clinicopathological factors (p = 0.0376 and p = 0.0386 in the training and validation cohort, respectively). Subsequently, the calibration plot showed a favorable consistency in both groups. Moreover, we conducted a decision curve analysis, showing the great clinical benefit obtained from the application of our nomogram. However, our study faced several limitations. Further external validation and a larger sample size are needed in future studies.

CONCLUSION

MRS-based nomograms are useful for predicting LVSI in patients with EC and may facilitate better clinical decision-making.

Use of Nomogram to Predict the Risk of Lymph Node Metastasis among Patients with Cervical Adenocarcinoma

Background

The objective of this study was to develop a nomogram that can predict lymph node metastasis (LNM) in patients with cervical adenocarcinoma (cervical AC).

Methods

A total of 219 patients with cervical AC who had undergone radical hysterectomy and lymphadenopathy between 2005 and 2021 were selected for this study. Both univariate and multivariate logistic regression analyses were performed to analyze the selected key clinicopathologic features and develop a nomogram and underwent internal validation to predict the probability of LNM.

Results

Lymphovascular invasion (LVI), tumor size ≥ 4 cm, and depth of cervical stromal infiltration were independent predictors of LNM in cervical AC. However, the Silva pattern was not found to be a significant predictor in the multivariate model. The Silva pattern was still included in the model based on the improved predictive performance of the model observed in the previous studies. The concordance index (C-index) of the model increased from 0.786 to 0.794 after the inclusion of the Silva pattern. The Silva pattern was found to be the strongest predictor of LNM among all the pathological factors investigated, with an OR of 4.37 in the nomogram model. The nomogram developed by incorporation of these four predictors performed well in terms of discrimination and calibration capabilities (C − index = 0.794; 95% confidence interval (CI), 0.727–0.862; Brier score = 0.127). Decision curve analysis demonstrated that the nomogram was clinically effective in the prediction of LNM.

Conclusion

In this study, a nomogram was developed based on the pathologic features, which helped to screen individuals with a higher risk of occult LNM. As a result, this tool may be specifically useful in the management of individuals with cervical AC and help gynecologists to guide clinical individualized treatment plan.