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

IINS Vs CALLY Index: A Battle of Prognostic Value in NSCLC Patients Following Surgery

Objective

This research sought to assess the predictive potential of the inflammation-immunity-nutrition score (IINS) and the high-sensitivity C-reactive protein-albumin-lymphocyte (CALLY) index in individuals with NSCLC post-surgery.

Methods

The study enrolled 506 patients with NSCLC undergoing R0 resection at the First Affiliated Hospital of Xi'an Jiaotong University. The training cohort was analyzed utilizing X-tile software to identify the ideal threshold values for categorizing high-sensitivity C-reactive protein, albumin, lymphocyte count, and the CALLY index. The predictive significance of the IINS and CALLY index was evaluated through Kaplan-Meier survival curves and univariate and multivariate Cox regression analyses. Predictive capabilities of the IINS and CALLY index were compared utilizing receiver operating characteristic (ROC) curve analysis, time-dependent ROC curve analysis, and decision curve analysis (DCA). Internal validation was performed in the validation cohort and all data from both the training and validation cohorts using Kaplan-Meier curves and DCA.

Results

Patients with lower IINS exhibited prolonged overall survival (OS), whereas those with lower CALLY had shorter OS. Multivariate analysis identified N stage, NSE, and IINS as independent prognostic factors for individuals with NSCLC. ROC analysis revealed that IINS provided superior prognostic performance to CALLY and other traditional indicators (CAR, PLR, and NLR). Time-dependent ROC analyses and DCA further confirmed the superior prognostic value of IINS over the CALLY index at 1, 2, and 3 years.

Conclusion

This study reveals that both the IINS and CALLY index are effective in forecasting the prognosis of individuals with NSCLC following surgery, with the IINS demonstrating superior prognostic efficacy to the CALLY index.

Nomogram Based on Immune-Inflammatory Score and Classical Clinicopathological Parameters for Predicting the Recurrence of Endometrial Carcinoma: A Large, Multi-Center Retrospective Study

Background

Surgery is the best approach to treat endometrial cancer (EC); however, there is currently a deficiency in effective scoring systems for predicting EC recurrence post-surgical resection. This study aims to develop a clinicopathological-inflammatory parameters-based nomogram to accurately predict the postoperative recurrence-free survival (RFS) rate of EC patients.

Methods

A training set containing 1068 patients and an independent validation set consisting of 537 patients were employed in this retrospective study. The prognostic factors for RFS were identified by univariable and multivariable Cox proportional hazards regression analyses, and integrated into nomogram. The C-index, area under the curves (AUC), and calibration curves were employed to determine the predictive discriminability and accuracy of nomogram. Utilizing the nomogram, patients were stratified into low- and high-risk groups, and the Kaplan-Meier survival curve was further employed to assess the clinical efficacy of the model.

Results

Cox regression analyses revealed that age (HR = 1.769, P = 0.002), FIGO staging (HR = 1.790, P = 0.018), LVSI (HR = 1.654, P = 0.017), Ca125 (HR = 1.532, P = 0.023), myometrial invasion (HR = 1.865, P = 0.001), cervical stromal invasion (HR = 1.655, P = 0.033), histology (HR = 2.637, P < 0.001), p53 expression (HR = 1.706, P = 0.002), PLR (HR = 1.971, P = 0.003), SIRI (HR = 2.187, P = 0.003), and adjuvant treatment (HR = 0.521, P = 0.003) were independent prognostic factors for RFS in patients with EC. A combined clinicopathologic-inflammatory parameters model was constructed, which outperformed the single-indicator model and other established models in predicting the 1-, 3-, and 5-year RFS rates in patients with EC.

Conclusion

The nomogram demonstrated sufficient accuracy in predicting the RFS probabilities of EC, enabling personalized clinical decision-making for future clinical endeavors.

A nomogram model to predict recurrence of early-onset endometrial cancer after resection based on clinical parameters and immunohistochemical markers: a multi-institutional study

Objective

This study aimed to investigate the prognosis value of the clinical parameters and immunohistochemical markers of patients with early-onset endometrial cancer (EC) and establish a nomogram to accurately predict recurrence-free survival (RFS) of early-onset EC after resection.

Methods

A training dataset containing 458 patients and an independent testing dataset consisting of 170 patients were employed in this retrospective study. The independent risk factors related to RFS were confirmed using Cox regression models. A nomogram model was established to predict RFS at 3 and 5 years post-hysterectomy. The C-index, area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and calibration curve were calculated to assess the predictive accuracy of the nomogram.

Results

In all early-onset EC patients, more than half (368/628, 58.6%) were diagnosed in the age range of 45-49 years. Meanwhile, the recurrence rate of early-onset EC is approximately 10.8%. Multivariate Cox regression analyses showed that histological subtype, FIGO stage, myometrial invasion, lymphovascular space invasion (LVSI), P53 expression, and MMR status were independent prognostic factors related to RFS (all P < 0.05) and established the nomogram predicting 3- and 5-year RFS. The C-index and calibration curves of the nomogram demonstrated a close correlation between predicted and actual RFS. Patients were divided into high- and low-risk groups according to the model of RFS.

Conclusions

Combining clinical parameters and immunohistochemical markers, we developed a robust nomogram to predict RFS after surgery for early-onset EC patients. This nomogram can predict prognosis well and guide treatment decisions.

The prognostic value of co-expression of stemness markers CD44 and CD133 in endometrial cancer

Objective

The purpose of this study was to investigate the correlation between stemness markers (CD44 and CD133) and clinical pathological features, and to further explore the prognostic value of co-expression of CD44 & CD133 in endometrial cancer (EC).

Methods

Clinical data of stage I-III EC patients who underwent initial surgical treatment at two large tertiary medical centers from 2015 to 2020 were retrospectively collected. Cohen's kappa coefficient was used to show the consistency of the expression between CD44 and CD133. The correlation between co-expression of CD44 & CD133 and prognosis of EC patients was explored using univariate and multivariate Cox regression analysis. Then, the prognosis models for early-stage (stage I-II) EC patients were constructed. Finally, stratified analysis was performed for EC patients in high-intermediate-risk and high-risk groups, Kaplan-Meier analysis was used to compare the survival differences between patients with and without adjuvant therapy in different co-expression states (low expression, mixed expression, high expression) of CD44 & CD133.

Results

A total of 1168 EC patients were included in this study. The consistency of the expression between CD44 and CD133 was 70.5%, the kappa coefficient was 0.384. High expression of CD44 & CD133 was associated with early FIGO stage (P=0.017), superficial myometrial invasion (P=0.017), and negative lymphatic vessel space invasion (P=0.017). Cox regression analysis showed that the co-expression of CD44 & CD133 was significantly correlated with the prognosis of early-stage (stage I-II) patients (P=0.001 for recurrence and P=0.005 for death). Based on this, the nomogram models were successfully constructed to predict the prognosis of early-stage EC patients. Meanwhile, Kaplan-Meier analysis showed that patients with adjuvant therapy had a better overall prognosis than those without adjuvant therapy in high-intermediate-risk and high-risk groups. However, there was no statistically significant difference in survival between patients with and without adjuvant therapy in high expression of CD44 & CD133 group (P=0.681 for recurrence, P=0.621 for death).

Conclusion

High expression of CD44 & CD133 was closely related to the adverse prognosis of early-stage EC patients. Meanwhile, patients with high expression of CD44 & CD133 may not be able to achieve significant survival benefits from adjuvant therapy.

A Nomogram Model Based on the Inflammation-Immunity-Nutrition Score (IINS) and Classic Clinical Indicators for Predicting Prognosis in Extranodal Natural Killer/T-Cell Lymphoma

Background

Systemic inflammation, immunity, and nutritional status are closely related to patients' outcomes in several kinds of cancers. This study aimed to establish a new nomogram based on inflammation-immunity-nutrition score (IINS) to predict the prognosis of extranodal natural killer/T-cell lymphoma (ENKTL) patients.

Methods

The clinical data of 435 patients with ENTKL were retrospectively reviewed and randomly assigned to training cohort (n=305) and validation cohort (n=131) at a ratio of 7:3. Cox regression analysis was employed to identify independent prognostic factors and develop a nomogram in the training cohort. Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were employed to assess the performance of the nomogram and compare it with traditional prognostic systems (PINK, IPI, KPI). Internal validation was performed using 1000 bootstrap resamples in the validation cohort. Kaplan-Meier survival analyses were conducted to compare the overall survival (OS) of patients in different risk groups.

Results

In the training cohort, in addition to several classic parameters, IINS was identified as an independent prognostic factor significantly associated with the OS of patients. The nomogram established based on the independent prognostic indicators showed superior survival prediction efficacy, with C-index of 0.733 in the training cohort and 0.759 in the validation cohort compared to the PINK (0.636 and 0.737), IPI (0.81 and 0.707), and KPI (0.693 and 0.639) systems. Furthermore, compared with PINK, IPI, and IPI systems, the nomogram showed relatively superior calibration curves and more powerful prognostic discrimination ability in predicting the OS of patients. DCA curves revealed some advantages in terms of clinical applicability of the nomogram compared to the PINK, IPI, and IPI systems.

Conclusion

Compared with traditional prognostic systems, the nomogram showed promising prospects for risk stratification in ENKTL patient prognosis, providing new insights into the personalized treatment.

Prognostic significance of index (LANR) composed of preoperative lymphocytes, albumin, and neutrophils in patients with stage IB-IIA cervical cancer

BACKGROUND

The purpose of this study was to investigate the role of preoperative lymphocytes, albumin, neutrophils, and LANR in the prognosis of patients with stage IB-IIA cervical cancer (CC).

METHODS

We made a retrospective analysis of the clinical information and related materials of 202 patients with stage IB-IIA primary cervical cancer who had undergone a radical hysterectomy in the Department of Gynecology at the Affiliated Hospital of Jiangnan University between January 2017 and December 2018. The definition of LANR was as follows: LANR, lymphocyte × albumin / neutrophil. The receiver operating characteristic curve (ROC) was generated to determine the best cut-off values for these parameters, as well as the sensitivity and specificity of LANR in predicting recurrence and survival. The Kaplan-Meier method was employed to draw survival curves in our survival analysis. Univariate analysis, multifactorial analysis, and subgroup analysis were used to evaluate the prognostic significance of LANR in overall and progression-free survival.

RESULTS

The median follow-up time of the study was 55 months. In overall survival, the area under the curve for LANR was 0.704 (95% CI: 0.590-0.818, p<0.05). And in progression-free survival, the area under the curve for LANR was 0.745 (95% CI: 0.662-0.828, p<0.05). Univariate and multivariate analyses showed that the value of LANR was associated with both overall survival and progression-free survival (p< 0.05). Kaplan-Meier analysis demonstrated that OS (p< 0.001) and PFS (p< 0.001) in patients with high LANR levels were significantly higher than those with low LANR levels.

CONCLUSIONS

Our findings suggested that LANR might serve as a clinically reliable and effective independent prognostic indicator in patients with stage IB-IIA cervical cancer.

Consistency of P53 immunohistochemical expression between preoperative biopsy and final surgical specimens of endometrial cancer

Objective

The aim of this study is to explore the consistency of P53 immunohistochemical expression between preoperative biopsy and final pathology in endometrial cancer (EC), and to predict the prognosis of patients based on the 4-tier P53 expression and classic clinicopathological parameters.

Methods

The medical data of patients with stage I-III EC who received preoperative biopsy and initial surgical treatment in two medical centers was retrospectively collected. The consistency of P53 immunohistochemistry expression between preoperative biopsy and final pathology was compared using Cohen's kappa coefficient and Sankey diagram, then 4-tier P53 expression was defined (P53wt/P53wt, P53abn/P53wt, P53wt/P53abn, and P53abn/P53abn). Univariate and multivariate Cox regression analysis was used to determine the correlation between 4-tier P53 expression and the prognosis of patients. On this basis, the nomogram models were established to predict the prognosis of patients by combining 4-layer P53 expression and classic clinicopathological parameters, then risk stratification was performed on patients.

Results

A total of 1186 patients were ultimately included in this study through inclusion and exclusion criteria. Overall, the consistency of P53 expression between preoperative biopsy and final pathology was 83.8%, with a kappa coefficient of 0.624. ROC curve suggested that the AUC of 4-tier P53 expression to predict the prognosis of patients was better than AUC of P53 expression in preoperative biopsy or final pathology alone. Univariate and multivariate Cox regression analysis suggested that 4-tier P53 expression was an independent influencing factor for recurrence and death. On this basis, the nomogram models based on 4-tier P53 expression and classical clinicopathological factors were successfully established. ROC curve suggested that the AUC (AUC for recurrence and death was 0.856 and 0.838, respectively) of the models was superior to the single 4-tier P53 expression or the single classical clinicopathological parameters, which could provide a better risk stratification for patients.

Conclusion

The expression of P53 immunohistochemistry had relatively good consistency between preoperative biopsy and final pathology of EC. Due to the discrepancy of P53 immunohistochemistry between preoperative biopsy and final pathology, the prognosis of patients can be better evaluated based on the 4-layer P53 expression and classic clinical pathological parameters.

Prognostic Value of Inflammation-Immunity-Nutrition Score and Inflammatory Burden Index for Hepatocellular Carcinoma Patients After Hepatectomy

PURPOSE

The study aimed to investigate the ability of inflammation-immunity-nutrition score (IINS) and inflammatory burden index (IBI), individually or in combination, to predict prognosis of hepatocellular carcinoma (HCC) patients after hepatectomy.

METHODS

A total of 701 patients who underwent HCC resection at Guangxi Medical University Cancer Hospital were enrolled in the study. An IINS ranging from 0 to 3 was defined based on preoperative C-reactive protein (CRP), lymphocyte count, and serum albumin level, while an IBI was based on CRP and neutrophil-to-lymphocyte ratio. The prognostic value of IINS and IBI was assessed using univariate and multivariate Cox regression and Kaplan-Meier survival curves. The concordance index and calibration curve were used for internal validation of models. Decision curve analysis, net reclassification index and integrated discrimination improvement were used to compare the predictive performance of the models with traditional staging systems.

RESULTS

IINS and IBI were able to predict poor prognosis in HCC patients after hepatectomy, and a nomogram based on the IINS predicted survival at 1, 3, and 5 years better than other models or traditional staging systems.

CONCLUSION

IINS may be accurate predictors of survival in HCC patients after hepatectomy, with potentially greater prognostic value than conventional markers.

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters

Objective

The purpose of this study was to evaluate the prognostic value of hemoglobin, albumin, lymphocyte, and platelet (HALP) score in patients with operable cervical cancer, and on this basis, combined with classical clinicopathological parameters to predict the recurrence of patients.

Methods

A total of 1580 patients with stage IA-IIA cervical cancer were randomly divided into training cohort (n=1054) and validation cohort (n=526) according to the predefined ratio of 2:1. In the training cohort, the receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of HALP score for predicting cervical cancer recurrence. On this basis, the independent related factors with cervical cancer recurrence were screened through univariate and multivariate Cox regression analysis, and then a nomogram model was further established. The internal and external validation of the model was carried out in the training cohort and the validation cohort respectively through the consistency index (C-index) and calibration curve.

Results

ROC curve and Youden index showed that the optimal threshold of HALP score for predicting cervical cancer recurrence was 39.50. Multivariate analysis confirmed that HALP score and some other classic clinicopathological parameters were independently associated with cervical cancer recurrence. Based on the results of multivariate analysis, a nomogram model for predicting cervical cancer recurrence was successfully constructed. The internal and external calibration curves showed that the fitting degree of the model was good, and the C-index (the C-index of the training cohort and the validation cohort were 0.862 and 0.847, respectively) showed that the prediction accuracy of the model proposed in this study was better than other similar models.

Conclusion

HALP score may be a novel predictor for predicting the cervical cancer recurrence. Nomogram model based on HALP score and classical clinicopathological parameters can better predict the recurrence of cervical cancer.