The combined ratio of estrogen, progesterone, Ki-67, and P53 to predict the recurrence of endometrial cancer

Construction and validation of a prediction model for preoperative prediction of Ki-67 expression in endometrial cancer patients by apparent diffusion coefficient

AIM

Ki-67 is a marker of cell proliferation and is increasingly being used as a primary outcome measure in preoperative window studies of endometrial cancer (EC). This study explored the feasibility of using apparent diffusion coefficient (ADC) values in noninvasive prediction of Ki-67 expression levels in EC patients before surgery, and constructs a nomogram by combining clinical data.

MATERIAL AND METHODS

This study retrospectively analyzed 280 EC patients who underwent preoperative magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in our hospital from January 2017 to February 2023. Evaluate the potential nonlinear relationship between ADC values and Ki-67 expression using the nomogram. The included patients were randomized into a training set (n = 186) and a validation set (n = 84). Using a combination of logistic regression and LASSO regression results, from which the four best predictors were identified for the construction of the nomogram. The accuracy and clinical applicability of the nomogram were assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

The results of this study showed a nonlinear correlation between ADCmin and Ki-67 expression (nonlinear P = 0.019), and the nonlinear correlation between ADCmean and Ki-67 expression (nonlinear P = 0.019). In addition, this study constructed the nomogram by incorporating ADCmax, International Federation of Gynecology and Obstetrics (FIGO), and chemotherapy. The area under the curve (AUC) values of the ROC for nomogram, ADCmax, FIGO, chemotherapy and grade in the training set were 0.783, 0.718, 0.579, 0.636, and 0.654, respectively. In the validation set, the AUC values for nomogram, ADCmax, FIGO, chemotherapy, and grade were 0.820, 0.746, 0.558, 0.542, and 0.738, respectively. In addition, the calibration curves and the DCA curves suggested a better predictive efficacy of the model.

CONCLUSION

A nomogram prediction model constructed on the basis of ADCmax values combined with clinical data can be used as an effective method to noninvasively assess Ki-67 expression in EC patients before surgery.

Development and validation of a nomogram for predicting recurrence-free survival in endometrial cancer: a multicenter study

Recurrence is the main cause of death in patients with endometrial cancer (EC). This study aimed to construct and validate a nomogram to predict the recurrence-free survival of patients with EC. This was a multicenter retrospective study. A total of 812 patients from Wuhan Tongji Hospital were divided into training and validation cohorts, and 347 and 580 patients from People's Hospital of Peking University and Qilu Hospital of Shandong, respectively, were used for validation. Univariate and multivariate Cox regression analyses were used to construct a nomogram for predicting recurrence-free survival of EC. Calibration curves, receiver operating characteristic (ROC) curves, and consistency indexes (C-indexes) were used to estimate the performance of the model. Decision curve analysis (DCA) curves were used to assess the clinical utility of the model. Age (P = 0.013), cancer antigen 125 level (P = 0.014), lymphovascular space invasion (P = 0.004), International Federation of Gynecology and Obstetrics stage (P = 0.034), and P53 (P < 0.001) were independently associated with recurrence, and we constructed a nomogram based on these variables. The C-indexes of the validation cohorts were 0.880, 0.835, and 0.875, respectively. The calibration, ROC, and DCA curves revealed that this model had excellent performance and clinical utility. Combining clinical data, clinicopathological factors, serological indicators, and immunohistochemical marks, a multicenter externally verified nomogram with robust performance was constructed to predict the recurrence of patients with EC.

The prognostic value of P53 index in predicting the recurrence of early low-risk endometrial cancer

AIM

We aimed to clarify the clinical value of P53 index in patients with early low-risk endometrial cancer (EC) and find an optimal cut-off value of P53 index for predicting the recurrence of these patients.

METHODS

The clinicopathological data of 157 patients with early low-risk EC (stage 1A with grade 1 or 2 endometrioid EC) were analyzed. The optimal cut-off value of the P53 index was calculated by the receiver operating characteristic curve analysis and Youden index. Cox regression model was used to evaluate the independent prognostic predictors of recurrence of EC. Then all patients were divided into two groups according to the optimal cut-off value of the P53 index. Differences of the clinicopathological parameters between the two groups were compared.

RESULTS

Multivariate analysis showed age PR (p = 0.020) and P53 (p = 0.001) were independent prognostic factors for the recurrence of EC. The value of P53 index was found to be the optimal cut-off point of 17.5% in estimating the recurrence of EC. The 5-year recurrence-free survival rates of patients in the low P53 index group (<17.5%) and the high P53 index group (≥17.5%) were 94.6% and 65.4% (p < 0.001).

CONCLUSION

It has been revealed that the P53 index is a prognostic factor for recurrence in early low-risk EC. The optimal cut-off value of P53 index may contribute to the postoperative individualized treatment options for early low-risk EC patients.

The Feasibility of Combining ADC Value With Texture Analysis of T2WI, DWI and CE-T1WI to Preoperatively Predict the Expression Levels of Ki-67 and p53 of Endometrial Carcinoma

PURPOSE

To evaluate the feasibility of apparent diffusion coefficient (ADC) value combined with texture analysis (TA) in preoperatively predicting the expression levels of Ki-67 and p53 in endometrial carcinoma (EC) patients.

METHODS

Clinical, pathological and MRI findings of 110 EC patients were analyzed retrospectively. The expression levels of Ki-67 and p53 in EC tissues were detected by immunohistochemistry. ADC value was calculated, and three-dimensional (3D) texture features were measured on T₂-weighted images (T₂WI), diffusion-weighted images (DWI), and contrast-enhanced T₁-weighted images (CE-T₁WI). The univariate and multivariate logistic regression and cross-validations were used for the selection of texture features. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic efficiency of prediction model by the area under the curve (AUC) in the training and validation cohorts.

RESULTS

Significant differences of the ADC values were found in predicting Ki-67 and p53 (P=0.039, P=0.007). The AUC of the ADC value in predicting the expression levels of Ki-67 and p53 were 0.698, 0.853 and 0.626, 0.702 in the training and validation cohorts. The AUC of the TA model based on T₂WI, DWI, CE-T₁WI, and ADC value combined with T₂WI + DWI + CE-T₁WI in the training and validation cohorts for predicting the expression of Ki-67 were 0.741, 0.765, 0.733, 0.922 and 0.688, 0.691, 0.651, 0.938, respectively, and for predicting the expression of p53 were 0.763, 0.805, 0.781, 0.901 and 0.796, 0.713, 0.657, 0.922, respectively.

CONCLUSION

ADC values combined with TA are beneficial for predicting the expression levels of Ki-67 and p53 in EC patients before surgery, and they provide higher auxiliary diagnostic values for clinical application.

Analysis of immunohistochemical characteristics and recurrence after complete remission with fertility preservation treatment in patients with endometrial carcinoma and endometrial atypical hyperplasia

OBJECTIVE

To investigate the relationship between immunohistochemical characteristics and recurrence after complete remission (CR) with fertility preservation treatment in patients with endometrial cancer (EC) and endometrial atypical hyperplasia (AH).

METHODS

The clinical data and immunohistochemical results of 53 patients with EC and 68 patients with AH admitted to Peking University People's Hospital from January 2010 to January 2021 were retrospectively analyzed. Patients were divided into two groups according to whether recurrence after complete remission (CR): group 1: recurrence after CR; group 2: no recurrence after CR, for statistical analysis.

RESULTS

(1) The expression rate of ER in group 1 was lower than that in group 2, (P < 0.05). The expression rate of Ki-67 in group 1 was significantly higher than that in group 2, (P < 0.01). The expression rates of PR, P16, P53, and PTEN were not significantly different between the two groups (P > 0.05); (2) combination index ER/ Ki-67 row ROC curve analysis, there was a significant difference (P < 0.01), the best cut-off value was 3.55, sensitivity 0.730, specificity 1.000, Youden index 0.730. The 3-year RFS of high rate patients was 100%, and that of low rate patients was 42.3%, P < 0.01.

CONCLUSIONS

The expression rate of Ki-67 is of great significance in predicting the recurrence of EC after fertility preservation therapy. The best cut-off value of combination index ER/ Ki-67 (3.55) was better than a single immunohistochemical marker in predicting recurrence of EC after fertility preservation treatment.

Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma

Background

Lymph node metastasis (LNM) is a critical unfavorable prognostic factor in endometrial cancer (EC). At present, models involving molecular indicators that accurately predict LNM are still uncommon. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group for LNM.

Methods

In all, 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into a training cohort (used for building the model) and a validation cohort (used for validating the model) according to a predefined ratio of 7:3. Logistics regression analysis was used in the training cohort to screen out predictors related to LNM, after which a nomogram was developed to predict LNM in patients with EC. A calibration curve and consistency index (C-index) were used to estimate the performance of the model. A receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of the risk probability of LNM predicted by the model proposed in this study. Then, the prediction performance of different models and their discrimination abilities for identifying low-risk patients were compared.

Result

LNM occurred in 87 and 42 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis showed that histological grade (P=0.022), myometrial invasion (P=0.002), lymphovascular space invasion (LVSI) (P=0.001), serum CA125 (P=0.008), Ki67 (P=0.012), estrogen receptor (ER) (0.009), and P53 (P=0.003) were associated with LNM; a nomogram was then successfully established on this basis. The internal and external calibration curves showed that the model fits well, and the C-index showed that the prediction accuracy of the model proposed in this study was better than that of the other models (the C-index of the training and validation cohorts was 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. Based on this threshold, the model showed good discrimination for identifying low-risk patients.

Conclusion

Combining molecular indicators based on classical clinical parameters can predict LNM of patients with EC more accurately. The nomogram proposed in this study showed good discrimination for identifying low-risk patients with LNM.

A Nomogram Model Involving Immunohistochemical Markers for Predicting the Recurrence of Stage I-II Endometrial Cancer

Background

The purpose of this study was to establish a nomogram combining classical parameters and immunohistochemical markers to predict the recurrence of patients with stage I-II endometrial cancer (EC).

Methods

419 patients with stage I-II endometrial cancer who received primary surgical treatment at the First Affiliated Hospital of Chongqing Medical University were involved in this study as a training cohort. Univariate and multivariate Cox regression analysis of screening prognostic factors were performed in the training cohort to develop a nomogram model, which was further validated in 248 patients (validation cohort) from the Second Affiliated Hospital of Chongqing Medical University. The calibration curve was used for internal and external verification of the model, and the C-index was used for comparison among different models.

Results

There were 51 recurrent cases in the training cohort while 31 cases in the validation cohort. Univariate analysis showed that age, histological type, histological grade, myometrial invasion, cervical stromal invasion, postoperative adjuvant treatment, and four immunohistochemical makers (Ki67, estrogen receptor, progesterone receptor, P53) were the related factors for recurrence of EC. Multivariate analysis demonstrated that histological type (P = 0.029), myometrial invasion (P = 0.003), cervical stromal invasion (P = 0.001), Ki67 (P < 0.001), ER (P = 0.009) and P53 expression (P = 0.041) were statistically correlated with recurrence of EC. Recurrence-free survival was better predicted by the proposed nomogram with a C-index of 0.832 (95% CI, 0.752–0.912) in the training cohort, and the validation set confirmed the finding with a C-index of 0.861 (95% CI, 0.755–0.967).

Conclusion

The nomogram model combining classical parameters and immunohistochemical markers can better predict the recurrence in patients with FIGO stage I-II EC.

Postoperative Systemic Immune-Inflammation Index (SII): A Superior Prognostic Factor of Endometrial Cancer

Objective: This study evaluates the preoperative and postoperative systemic immune-inflammation index (SII) capacity to predict the prognosis of patients with endometrial carcinoma after the operation and build a nomogram model to assist clinical practice. Methods: The retrospective study included 362 consecutive patients with surgically resected endometrial cancer between January 2010 and June 2015 at The Affiliated Cancer Hospital of Shantou University Medical College. Blood routine was examined within 1 week before surgery to calculate SII, NLR, PLR, and MLR and 3 days after surgery to measure SII. The Pearson's χ²-test or Fisher's exact test was used to explore their relationship to clinical variables. The univariate and multivariate survival analyses were performed by Cox regression to identify the independent prognostic indicators. The Kaplan-Meier method with the log-rank test was used to generate the overall survival (OS) curves. R software was used to generate the receiver operating characteristic (ROC) curve and then it got the optimum cutoff value through the maximum Youden index. A nomogram model was formed with systemic immune inflammation and clinical factors. Results: The preoperative SII was related to age (p = 0.009), FIGO stage (p = 0.02) and menopause (p = 0.014). The postoperative SII was associated with menopause (p = 0.014). Univariate analysis indicated that FIGO stage, lymphatic invasion, depth of myometrial invasion, postoperative chemotherapy, postoperative radiotherapy, preoperative SII, NLR, PLR, MLR, CRP, CA125, and postoperative SII were predictors of OS (p < 0.05). Multivariate analysis showed that lymphatic invasion and postoperative SII were independent prognostic factors of OS (p < 0.05). The nomogram model was visualized precisely to reflect the prognosis with a C-index value of 0.866 in this model. Conclusion: The postoperative SII is the independent prognostic factor in patients with endometrial carcinoma after the operation and contributes to poor outcomes. However, after surgery, the preoperative SII and preoperative NLR, PLR, and MLR are not associated with OS endometrial carcinoma. Making good use of the nomogram model would contribute to better subsequent therapy.

Therapeutic Anticancer Uses of the Active Principles of "Rhopalurus junceus" Venom

The Rhopalurus junceus is a scorpion belonging to the Buthidae family that finds its habitat in Cuba. This scorpion is known by the common name of "Blue Scorpion". The venom is used on the island of Cuba as an alternative cure for cancer and, more recently, in the research of active components for biomedicine. Recently, the venom has been tested in several studies to investigate its effects on cancer cell lines, and the initial results of in vitro studies demonstrated how this poison can be effective on certain carcinoma cell lines (Hela, SiHa, Hep-2, NCI-H292, A549, MDA-MB-231, MDA-MB-468, and HT-29). The aim of this review is, therefore, to describe the effects of the venom on carcinoma lines and to investigate all anti-cancer properties studied in the literature. The research was conducted using four databases, Pub Med, Scopus, EBSCO, and Web of Science, through the use of keywords, by two independent reviewers following the PRISMA protocol, identifying 57 records. The results led to a total of 13 articles that met the eligibility criteria. The data extracted for the purpose of meta-analysis included the IC₅₀ of the venom on carcinoma cell lines. The results of the meta-analysis provided a pooled mean of the IC₅₀ of 0.645 mg/mL (95% CI: 0.557, 0.733), with a standard error (SE) = 0.045, p < 0.001. The analysis of the subgroups, differentiated by the type of cell line used, provided insight regarding how the scorpion venom was effective on the cell lines of lung origin (NCI-H292, A549, and MRC-5) with a pooled mean of IC50 0.460 mg/mL (95% CI: 0.290, 0.631) SE (0.087) p < 0.001. The results described in the literature for in vitro studies are encouraging, and further investigations should be carried out and deepened.