The Potential Value of Ki-67 in Prognostic Classification in Early Low-Risk Endometrial Cancer.

PURPOSE: This study aims to determine the optimal cut-off value of Ki-67 to better predict the recurrence of early low-risk endometrial cancer (EC). METHODS: Seven hundred and forty-eight patients diagnosed with low-risk EC from West China Second Hospital of Sichuan University and the First Affiliated Hospital of Chongqing Medical University were retrospectively analyzed. The receiver operating characteristic curve (ROC) and Youden index were used to calculate the optimal cut-off value of Ki-67 expression. The clinicopathological indexes between two groups divided by cut-off value of Ki-67 were compared. The univariate and multivariate regression analyses were performed to investigate risk factors connected to the recurrence of early low-risk EC. The survival analysis was shown in Kaplan-Meier curve. RESULT: Thirty-three patients were detected with tumor recurrence after primary surgery (4.4%); 33% was the optimal cut-off value of the Ki-67 index. A high Ki-67 was significantly associated with age (P = .002), myometrial invasion (P < .001), and the expression of P53 (P = .007). The multivariate regression analysis verified that Ki67 ≥ 33% was an independent prognostic factor for predicting recurrence. The recurrence-free survival (RFS) and the overall survival (OS) in high Ki-67 group was significantly lower than that in low Ki-67 group (P < .001 and P = .029, respectively). The prognostic values of ER, PR, and P53 in combination with Ki-67 were superior to each single predictor. CONCLUSIONS: The optimal cut-off value of Ki-67 for predicting recurrence is 33%, which quantitatively defines the specific value of Ki-67 that causes high-risk recurrence in early low-risk EC.

The relationship between inflammatory response markers and the prognosis of non-muscle invasive bladder cancer and the development of a nomogram model.

Purpose: Patients with non-muscle invasive bladder cancer (NMIBC) have a high possibility of recurrence after surgery. We aimed to assess the factors associated with tumor recurrence and to construct a nomogram model that can contribute to personalized treatment plans of each patient. Methods: 496 patients with primary bladder cancer (BC) from 2 centers were retrospectively analyzed. Preoperative neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and traditional clinical parameters were collected, then using univariate and multivariate Cox regression analysis to find out the independent risk factors associated with tumor recurrence among them, and then these independent factors were incorporated into the nomogram model. The internal calibration curves and the external calibration curves were used to verify their usefulness. Results: In the training cohort, 150 patients (43.1%) experienced recurrence. After Cox regression analysis, the independent risk factors affecting recurrence-free survival (RFS) were tumor grade, immediate postoperative instillation therapy (IPPIT), NLR, and SII. These factors were used to construct a model to predict RFS 1, 2, 3, and 5 years of NMIBC patients after surgery. And then, we found that the constructed model outperforms the conventional model in terms of accuracy and predictability, the results were verified by statistical tests. Conclusion: Preoperative inflammatory response markers have a predictive value for postoperative recurrence in patients with NMIBC. The constructed nomogram model can be helpful in guiding personalized clinical evaluation and subsequent treatment.

A Nomogram Model Based on Neuroendocrine Markers for Predicting the Prognosis of Neuroendocrine Carcinoma of Cervix.

BACKGROUND: Combining traditional clinical parameters with neuroendocrine markers to construct a nomogram model to predict the postoperative recurrence of neuroendocrine carcinoma of cervix (NECC). METHODS: A total of 257 patients were included in this study. Univariate and multivariate Cox regression analyses were used to establish a nomogram model in the training cohorts, which was further validated in the validation cohorts. The calibration curve was used to conduct the internal and external verification of the model. RESULTS: Overall, 41 relapse cases were observed in the training (23 cases) and validation (18 cases) cohorts. The univariate analysis preliminarily showed that FIGO stage, stromal invasion, nerve invasion, lymph vascular space invasion, lymph node involvement, cervical-uterine junction invasion and CgA were correlated with NECC recurrence. The multivariate analysis further confirmed that FIGO stage (p = 0.023), stromal invasion (p = 0.002), lymph vascular space invasion (p = 0.039) and lymph node involvement (p = 0.00) were independent risk factors for NECC recurrence, which were ultimately included in the nomogram model. In addition, superior consistency indices were demonstrated in the training (0.863, 95% CI 0.784-0.942) and validation (0.884, 95% CI 0.758-1.010) cohorts. CONCLUSIONS: The established nomogram model combining traditional clinical parameters with neuroendocrine markers can reliably and accurately predict the recurrence risks in NECC patients.

A Nomogram Model to Predict Recurrence of Non-Muscle Invasive Bladder Urothelial Carcinoma After Resection Based on Clinical Parameters and Immunohistochemical Markers.

OBJECTIVE: This study aims to establish a nomogram model by combining traditional clinical parameters with immunohistochemical markers to predict the recurrence of non-muscle invasive bladder urothelial carcinoma (NMIBUC) after resection. METHODS: In total, 504 patients were included in this study. Of these patients, 353 underwent transurethral resection of bladder tumor (TURBT) in the Yongchuan Hospital of Chongqing Medical University and were identified as a training cohort. Univariate and multivariate Cox regression analyses were used to determine the risk factors associated with recurrence in the training cohort and to establish a nomogram model. A total of 151 patients who were hospitalized in the Second Affiliated Hospital of Chongqing Medical University (validation cohort) were used for further validation. The calibration curve was generated for internal and external model validation. The clinical practicability of this model was further verified by comparing the consistency index (C-index) among various models. RESULTS: The mean follow-up time of the training cohort was 45.6 months (range 4-90). In total, 146 patients relapsed in training cohort. After univariate analysis, multivariate analysis further confirmed tumor grade (p=.034), immediate postoperative instillation therapy (p=.025), Ki67 (p=.047), P53 (p=.038) and CK20 (p=.049) as independent risk factors for recurrence, and these factors were included in the nomogram model. The model more accurately predicted recurrence compared with other models based on the highest C-index of 0.82 (95% CI, 0.78-0.86) in the training cohort and 0.80 (95% CI, 0.77-0.83) in the validation cohort. CONCLUSIONS: This proposed nomogram model based on traditional clinical parameters and immunohistochemical markers can more accurately predict postoperative recurrence in patients with NMIBUC.