Predicting recurrence of non-muscle invasive bladder urothelial carcinoma: predictive value of the optimal cut-off value of Ki67

ObjectiveTo investigate the optimal cut-off value of immunohistochemical marker Ki67 as a prognostic factor to predict the recurrence of non-muscle invasive bladder urothelial carcinoma (NMIBUC).MethodsA total of 331 patients diagnosed with NMIBUC who underwent surgery in the Yongchuan Hospital and the Second Affiliated Hospital of Chongqing Medical University from January 2012 to January 2020 were finally included in this study. The optimal cut-off value of Ki67 for predicting recurrence of NMIBUC was calculated by ROC curve and Youden index. According to the cut-off value, the patients were divided into high ratio group and low ratio group, and the clinicopathological data of the two groups were compared. Univariate and multivariate regression analysis were used to analyze the relationship between the expression of Ki67 and postoperative recurrence of NMIBUC. The Kaplan-Meier curve was used for survival analysis.Results18% is the optimal cut-off value of Ki67 for predicting postoperative recurrence of NMIBUC. High Ki67 expression (Ki67>18%) was significantly correlated with tumor stage (P=0.001), tumor grade (P=0.014), immediate postoperative instillation (P=0.001), the expression of P53 (P=0.019) and CK20 (P=0.001). Ki67 expression greater than 18% was an independent risk factor for high recurrence rate of NMIBUC (P=0.001). Moreover, the 1-year and 3-year recurrence-free survival (RFS) of the high Ki67 group were 56.6% (95%CI 51.2%-62%) and 43.6% (95%CI 37.5%-49.7%) respectively, which were significantly lower than those in low Ki67 group which present as 92.9% (95%CI 89.0%-96.8%) and 88.3% (95%CI 82.4%-94.2%) respectively, and the difference was statistically significant (P<0.001).Conclusions18% is the optimal cut-off value of Ki67 for predicting recurrence of NMIBUC. Ki67>18% is an independent risk factor for high recurrence rate of NMIBUC. This cut-off value can more accurately predict the risk of recurrence and has the potential clinical value for guiding the postoperative adjuvant treatment and follow-up strategy of NMIBUC

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

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.</jats:p

A nomogram model based on neuroendocrine markers for predicting the prognosis of neuroendocrine carcinoma of cervix

Abstract Purpose Combining traditional clinical parameters with neuroendocrine markers to construct a nomogram model to predict the postoperative recurrence of neuroendocrine carcinoma of cervix (NECC). Methods 257 patients were included in this study, of which, 171 patients diagnosed with NECC who underwent surgery at West China Second Hospital of Sichuan University were considered as part of the training cohort. The univariate and multivariate Cox regression analyses were used in screening the high-risk factors related to cancer recurrence in the training cohort to establish a nomogram model which was further independently validated in the remaining 86 patients who underwent surgery at First Affiliated Hospital of Chongqing Medical University. 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. Conclusion The established nomogram model combining traditional clinical parameters with neuroendocrine markers can reliably and accurately predict the recurrence risks in NECC patients.</jats:p

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