Nomogram for individually predicting overall survival in rectal neuroendocrine tumours

Evaluation of the GATIS score for predicting prognosis in rectal neuroendocrine neoplasms

The GATIS score, developed by Zeng et al, represents a significant advancement in predicting the prognosis of patients with rectal neuroendocrine neoplasms (R-NENs). This study, which included 1408 patients from 17 major medical centres in China over 12 years, introduces a novel prognostic model based on the tumour grade, T stage, tumour size, age, and the prognostic nutritional index. Compared with traditional methods such as the World Health Organization classification and TNM staging systems, the GATIS score has superior predictive power for overall survival and progression-free survival. With a C-index of 0.915 in the training set and 0.812 in the external validation set, the GATIS score’s robustness and reliability are evident. The study’s use of a large, multi-centre cohort and rigorous validation processes underscore its significance. The GATIS score offers clinicians a powerful tool to accurately predict patient outcomes, guide treatment decisions, and improve follow-up strategies. This development represents a crucial step forwards in the management of R-NENs, addressing the complexity and variability of these tumours and setting a new benchmark for future research and clinical practice.

GATIS score for predicting the prognosis of rectal neuroendocrine neoplasms: A Chinese multicenter study of 12-year experience

BACKGROUND

There is currently a shortage of accurate, efficient, and precise predictive instruments for rectal neuroendocrine neoplasms (NENs).

AIM

To develop a predictive model for individuals with rectal NENs (R-NENs) using data from a large cohort.

METHODS

Data from patients with primary R-NENs were retrospectively collected from 17 large-scale referral medical centers in China. Random forest and Cox proportional hazard models were used to identify the risk factors for overall survival and progression-free survival, and two nomograms were constructed.

RESULTS

A total of 1408 patients with R-NENs were included. Tumor grade, T stage, tumor size, age, and a prognostic nutritional index were important risk factors for prognosis. The GATIS score was calculated based on these five indicators. For overall survival prediction, the respective C-indexes in the training set were 0.915 (95% confidence interval: 0.866-0.964) for overall survival prediction and 0.908 (95% confidence interval: 0.872-0.944) for progression-free survival prediction. According to decision curve analysis, net benefit of the GATIS score was higher than that of a single factor. The time-dependent area under the receiver operating characteristic curve showed that the predictive power of the GATIS score was higher than that of the TNM stage and pathological grade at all time periods.

CONCLUSION

The GATIS score had a good predictive effect on the prognosis of patients with R-NENs, with efficacy superior to that of the World Health Organization grade and TNM stage.

Nomogram for the prediction of lymph node metastasis and survival outcomes in rectal neuroendocrine tumour patients undergoing resection

BACKGROUND

The current study analysed rectal neuroendocrine tumour (RNET) patients undergoing resection to identify predictive factors and construct nomograms for lymph node metastasis, cancer-specific survival (CSS) and overall survival (OS).

METHODS

RNET patients registered in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Multivariable logistic regression analysis was used to investigate the relationships between clinicopathological factors and lymph node metastasis. A multivariate competing risk model was applied to investigate factors independently associated with CSS. Through the Cox regression model, a multivariable analysis of OS was performed. Nomograms were established based on independent predictive factors. Calibration plots, receiver operating characteristic (ROC) curves and Brier scores were used to evaluate the predictive accuracy of the nomograms.

RESULTS

In this study, 1,253 RNET patients were included for further analysis. Tumour size ≥12 mm (P<0.001), T3/T4 stage (P<0.001) and M1 stage (P=0.001) were independently associated with lymph node metastasis. The performance of the nomogram was acceptable for predicting lymph node metastasis, with an area under the ROC curve (AUC) of 0.937 [95% confidence interval (CI): 0.874-1.000]. Calibration curves and the Hosmer-Lemeshow test revealed desirable model calibration (P=0.99996). The multivariate competing risk model analysis showed that grade II (P=0.017), tumour size ≥12 mm (P=0.007), AJCC TNM stage II (P=0.002), stage III (P<0.001) and stage IV (P<0.001) were significantly associated with worse CSS. In the competing risk nomogram model, the time-dependent AUC revealed good discriminatory ability of the model (time from 1 to 107 months, AUC >0.900), and the Brier score showed good accuracy of the nomogram, which was greater than that of the AJCC TNM stage. Multivariate Cox analysis showed that age >60 years (P=0.002), median income ≥$65,000 (P=0.013), AJCC TNM stage III (P=0.038) and AJCC TNM stage IV (P<0.001) were independently associated with worse OS. In the nomogram for the prediction of OS, the C-statistic was 0.703 (95% CI: 0.615-0.792), which was significantly better than that of the AJCC TNM stage (0.703 vs. 0.607, P=0.009). A calibration plot for the probability of survival demonstrated good calibration.

CONCLUSIONS

The present study is the first to establish nomograms with great discrimination and accuracy for the prediction of lymph node metastases, CSS and OS in RNET patients, which can be used to guide treatment decision-making and surveillance.

Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy

Objectives

To develop a prognostic prediction MRI-based nomogram model for locally advanced rectal cancer (LARC) treated with neoadjuvant therapy.

Methods

This was a retrospective analysis of 233 LARC (MRI-T stage 3-4 (mrT) and/or MRI-N stage 1-2 (mrN), M0) patients who had undergone neoadjuvant radiotherapy and total mesorectal excision (TME) surgery with baseline MRI and operative pathology assessments at our institution from March 2015 to March 2018. The patients were sequentially allocated to training and validation cohorts at a ratio of 4:3 based on the image examination date. A nomogram model was developed based on the univariate logistic regression analysis and multivariable Cox regression analysis results of the training cohort for disease-free survival (DFS). To evaluate the clinical usefulness of the nomogram, Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were conducted in both cohorts.

Results

The median follow-up times were 43.2 months (13.3–61.3 months) and 32.0 months (12.3–39.5 months) in the training and validation cohorts. Multivariate Cox regression analysis identified MRI-detected extramural vascular invasion (mrEMVI), pathological T stage (ypT) and perineural invasion (PNI) as independent predictors. Lymphovascular invasion (LVI) (which almost reached statistical significance in multivariate regression analysis) and three other independent predictors were included in the nomogram model. The nomogram showed the best predictive ability for DFS (C-index: 0.769 (training cohort) and 0.776 (validation cohort)). It had a good 3-year DFS predictive capacity [area under the curve, AUC=0.843 (training cohort) and 0.771 (validation cohort)]. DCA revealed that the use of the nomogram model was associated with benefits for the prediction of 3-year DFS in both cohorts.

Conclusion

We developed and validated a novel nomogram model based on MRI factors and pathological factors for predicting DFS in LARC treated with neoadjuvant therapy. This model has good predictive value for prognosis, which could improve the risk stratification and individual treatment of LARC patients.

Predicting Survival of Patients With Rectal Neuroendocrine Tumors Using Machine Learning: A SEER-Based Population Study

Background: The number of patients diagnosed with rectal neuroendocrine tumors (R-NETs) is increasing year by year. An integrated survival predictive model is required to predict the prognosis of R-NETs. The present study is aimed at exploring epidemiological characteristics of R-NETs based on a retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database and predicting survival of R-NETs with machine learning.

Methods: Data of patients with R-NETs were extracted from the SEER database (2000–2017), and data were also retrospectively collected from a single medical center in China. The main outcome measure was the 5-year survival status. Risk factors affecting survival were analyzed by Cox regression analysis, and six common machine learning algorithms were chosen to build the predictive models. Data from the SEER database were divided into a training set and an internal validation set according to the year 2010 as a time point. Data from China were chosen as an external validation set. The best machine learning predictive model was compared with the American Joint Committee on Cancer (AJCC) seventh staging system to evaluate its predictive performance in the internal validation dataset and external validation dataset.

Results: A total of 10,580 patients from the SEER database and 68 patients from a single medical center were included in the analysis. Age, gender, race, histologic type, tumor size, tumor number, summary stage, and surgical treatment were risk factors affecting survival status. After the adjustment of parameters and algorithms comparison, the predictive model using the eXtreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in the training set [area under the curve (AUC) = 0.87, 95%CI: 0.86–0.88]. In the internal validation, the predictive ability of XGBoost was better than that of the AJCC seventh staging system (AUC: 0.90 vs. 0.78). In the external validation, the XGBoost predictive model (AUC = 0.89) performed better than the AJCC seventh staging system (AUC = 0.83).

Conclusions: The XGBoost algorithm had better predictive power than the AJCC seventh staging system, which had a potential value of the clinical application.

Biliary Neuroendocrine Neoplasms: Analysis of Prognostic Factors and Development and Validation of a Nomogram

Background

For this study, we explored the prognostic profiles of biliary neuroendocrine neoplasms (NENs) patients and identified factors related to prognosis. Further, we developed and validated an effective nomogram to predict the overall survival (OS) of individual patients with biliary NENs.

Methods

We included a total of 446 biliary NENs patients from the SEER database. We used Kaplan-Meier curves to determine survival time. We employed univariate and multivariate Cox analyses to estimate hazard ratios to identify prognostic factors. We constructed a predictive nomogram based on the results of the multivariate analyses. In addition, we included 28 biliary NENs cases from our center as an external validation cohort.

Results

The median survival time of biliary NENs from the SEER database was 31 months, and the value of gallbladder NENs (23 months) was significantly shorter than that of the bile duct (45 months) and ampulla of Vater (33.5 months, p=0.023). Multivariate Cox analyses indicated that age, tumor size, pathological classification, SEER stage, and surgery were independent variables associated with survival. The constructed prognostic nomogram demonstrated good calibration and discrimination C-index values of 0.783 and 0.795 in the training and validation dataset, respectively.

Conclusion

Age, tumor size, pathological classification, SEER stage, and surgery were predictors for the survival of biliary NENs. We developed a nomogram that could determine the 3-year and 5-year OS rates. Through validation of our central database, the novel nomogram is a useful tool for clinicians in estimating individual survival among biliary NENs patients.