The positive threshold of the immunohistochemical parameter Ki67 for predicting the recurrence of cervical cancer.

OBJECTIVE: To find the optimal threshold of Ki67 and evaluate its significance in predicting recurrence of stage I-II cervical cancer. METHODS: A total of 1130 patients were included after screening. Univariate and multivariate Cox regression analysis were used to select factors associated with recurrence of cervical cancer. The receiver operating characteristic (ROC) curve was used to assess the optimal threshold of Ki67. The differences of clinicopathological parameters and the survival analysis between the two groups divided based on the optimal threshold of Ki67 were compared. RESULTS: Multivariate Cox regression analysis showed that Ki67 (p < 0.001) was significant prognostic predictor for recurrence of cervical cancer. The optimal threshold of Ki67 was 42%. The recurrence-free survival (RFS) and the overall survival (OS) of cervical cancer patients in the high-Ki67 group (Ki67≥42%) were much lower than those in the low-Ki67 group (Ki67<42%) (p < 0.001, p < 0.001). Among the 380 patients with low-risk cervical cancer, the RFS and OS of patients in the high-Ki67 group were also lower than those in the low-Ki67 group (p < 0.001, p < 0.001). CONCLUSION: The Ki67 was a useful prognostic factor in patients with stage I-II cervical cancer, and the Ki67 labeling index 42.0% was optimal threshold for predicting recurrence.

A Nomogram Model for Predicting Recurrence of Stage I-III Endometrial Cancer Based on Inflammation-Immunity-Nutrition Score (IINS) and Traditional Classical Predictors.

Objective: The purpose of this study was to investigate the prognostic value of the inflammation-immunity-nutrition score (IINS) in patients with stage I-III endometrial cancer (EC) and establish a nomogram model to predict the recurrence of EC by combining IINS and traditional classical predictors. Methods: Seven hundred and seventy-five patients with stage I-III EC who underwent initial surgical treatment at the First Affiliated Hospital of Chongqing Medical University were included in this study as the training cohort. In the training cohort, IINS (0-3) was constructed based on preoperative C-reactive protein (CRP), lymphocytes (LYM), and albumin (ALB). Univariate and multivariate Cox regression analysis were used to screen independent predictors associated with recurrence of EC for developing the nomogram model. Internal validation of the model was performed in the training cohort by using the C-index and calibration curve, while external validation of the model was performed in another cohort (validation cohort) of 491 patients from the Second Affiliated Hospital of Chongqing Medical University. Results: IINS was successfully constructed, and survival analysis showed that patients with high IINS had a worse prognosis. Multivariate analysis showed that IINS, age, FIGO stage, pathological type, myometrial invasion, lymphatic vessel space invasion (LVSI), Ki67 expression, estrogen receptor (ER) expression, and P53 expression were significantly associated with shorter recurrence-free survival, and then a nomogram model for predicting the recurrence of EC was successfully established. The internal and external calibration curves of the model showed that the model fit well, and the C-index (0.887 in training cohort and 0.883 in validation cohort) showed that the model proposed in this study had better prediction accuracy than other prediction models. Conclusion: IINS may be a strong predictor of prognosis in patients with EC. The nomogram model incorporated into the IINS can better predict the recurrence of EC than the traditional models.

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.