Construction and validation of nomograms based on the log odds of positive lymph nodes to predict the prognosis of lung neuroendocrine tumors

Construction and validation of novel nomograms based on the log odds of positive lymph nodes to predict the prognosis of papillary thyroid cancer: a retrospective cohort study

Objective

This study aims to assess the long-term prognostic significance of the log odds of positive lymph nodes (LODDS) in patients diagnosed with papillary thyroid cancer (PTC) and to develop a novel nomogram for predicting long-term overall survival (OS).

Methods

The cohort was randomly divided at a ratio of 7:3 from the Surveillance, Epidemiology, and End Results (SEER) database. Additionally, patient data from a medical center in China served as an external validation cohort. Nomograms were constructed using data from the training cohort and subsequently validated using both internal and external validation cohorts to predict 120- and 180-month OS in PTC patients. The predictive performance and clinical utility of the nomogram were assessed using various metrics, including the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), Integrated Discriminant Improvement Index (IDI), and Net Reclassification Improvement Index (NRI).

Results

LODDS is an independent prognostic factor for PTC, a nomogram demonstrating high accuracy in predicting long-term OS. The C-index values, and time-dependent area under the curve (AUC) indicated well discriminatory ability of the nomogram. Calibration plots exhibited high concordance, while DCA, NRI, and IDI analyses revealed superior performance of the nomogram compared to AJCC staging system.

Conclusion

The clinical prediction model incorporating LODDS exhibits robust predictive performance, aiding in the assessment of long-term prognosis post-surgery in PTC patients. It serves as a valuable adjunct to the AJCC system, offering a scientific basis for guiding interventions and rehabilitation strategies for PTC patients following surgery.

Rachel score: a nomogram model for predicting the prognosis of lung neuroendocrine tumors

BACKGROUND

Lung NET, classified in typical carcinoids (TC) and atypical carcinoids (AC), are highly heterogeneous in their biology and prognosis. The histological subtype and TNM stage are well-established prognostic factors for lung NET. In a previous work by our group, we demonstrated a significant impact of laterality on lung NET survival outcomes.

MATERIALS AND METHODS

We developed a nomogram that integrates relevant prognostic factors to predict lung NET outcomes. By adding the scores for each of the variables included in the model, it was possible to obtain a prognostic score (Rachel score). Wilcoxon non-parametric statistical test was applied among parameters and Harrell's concordance index was used to measure the models' predictive power. To test the discriminatory power and the predictive accuracy of the model, we calculated Gonen and Heller concordance index. Time-dependent ROC curves and their area under the curve (AUC) were used to evaluate the models' predictive performance.

RESULTS

By applying Rachel score, we were able to identify three prognostic groups (specifically, high, medium and low risk). These three groups were associate to well-defined ranges of points according to the obtained nomogram (I: 0-90, II: 91-130; III: > 130 points), providing a useful tool for prognostic stratification. The overall survival (OS) and progression free survival (PFS) Kaplan-Meier curves confirmed significant differences (p < 0.0001) among the three groups identified by Rachel score.

CONCLUSIONS

A prognostic nomogram was developed, incorporating variables with significant impact on lung NET survival. The nomogram showed a satisfactory and stable ability to predict OS and PFS in this population, confirming the heterogeneity beyond the histopathological diagnosis of TC vs AC.