Development and validation of prognostic nomograms for medullary thyroid cancer

Novel prognostic nomogram for predicting recurrence-free survival in medullary thyroid carcinoma

AIMS

Recently, there have been attempts to improve prognostication and therefore better guide treatment for patients with medullary thyroid carcinoma (MTC). In 2022, the International MTC Grading System (IMTCGS) was developed and validated using a multi-institutional cohort of 327 patients. The aim of the current study was to build upon the findings of the IMTCGS to develop and validate a prognostic nomogram to predict recurrence-free survival (RFS) in MTC.

METHODS AND RESULTS

Data from 300 patients with MTC from five centres across the USA, Europe, and Australia were used to develop a prognostic nomogram that included the following variables: age, sex, AJCC stage, tumour size, mitotic count, necrosis, Ki67 index, lymphovascular invasion, microscopic extrathyroidal extension, and margin status. A process of 10-fold cross-validation was used to optimize the model's performance. To assess discrimination and calibration, the area-under-the-curve (AUC) of a receiver operating characteristic (ROC) curve, concordance-index (C-index), and dissimilarity index (D-index) were calculated. Finally, the model was externally validated using a separate cohort of 87 MTC patients. The model demonstrated very strong performance, with an AUC of 0.94, a C-index of 0.876, and a D-index of 19.06. When applied to the external validation cohort, the model had an AUC of 0.9.

CONCLUSIONS

Using well-established clinicopathological prognostic variables, we developed and externally validated a robust multivariate prediction model for RFS in patients with resected MTC. The model demonstrates excellent predictive capability and may help guide decisions on patient management. The nomogram is freely available online at https://nomograms.shinyapps.io/MTC_ML_DFS/.

Construction and Validation of a Prediction Model for Identifying Clinical Risk Factors of Lateral Lymph Node Metastasis in Medullary Thyroid Carcinoma

Purpose

Medullary thyroid carcinoma (MTC) is a rare but highly invasive malignancy, especially in terms of cervical lymph node metastasis. However, the role of prophylactic lateral lymph node dissection (LLND) is still controversial. We hereby aim to explore the risk factors of lateral lymph node metastasis (LLNM) in patients with MTC to guide clinical practice.

Patients and Methods

The clinicopathological characteristics of patients with MTC from the Surveillance, Epidemiology, and End Results (SEER) Program and the Second Affiliated Hospital of Chongqing Medical University were reviewed and analyzed. Univariate and multivariate logistics regression analyses were used to screen the risk factors of LLNM in patients with MTC.

Results

Four variables, including male gender, multifocality, extrathyroidal invasion (EI), and large tumor size (all p < 0.05), were identified as potential independent factors of LLNM in patients with MTC. Based on these results, an individualized prediction model was subsequently developed with a satisfied C-index of 0.798, supported by both internal and external validation with a C-index of 0.816 and 0.896, respectively. We also performed the decision curve analysis (DCA) and calibration curve, which indicated a remarkable agreement in our model for predicting the risk of LLNM.

Conclusion

We determined that various clinical characteristics, male gender, multifocality, EI, and large tumor size, were significantly associated with LLNM in patients with MTC. Thus, a validated prediction model utilizing readily available variables was successfully established to help clinicians make individualized clinical decisions on MTC management, especially regarding whether the LLND is necessary for patients with clinical negative lateral lymph node involvement and the frequency of follow-up without LLND.

Prognostic Nomogram for Patients with Hepatitis E Virus-related Acute Liver Failure: A Multicenter Study in China

BACKGROUND AND AIMS

Timely and effective assessment scoring systems for predicting the mortality of patients with hepatitis E virus-related acute liver failure (HEV-ALF) are urgently needed. The present study aimed to establish an effective nomogram for predicting the mortality of HEV-ALF patients.

METHODS

The nomogram was based on a cross-sectional set of 404 HEV-ALF patients who were identified and enrolled from a cohort of 650 patients with liver failure. To compare the performance with that of the model for end-stage liver disease (MELD) scoring and CLIF-Consortium-acute-on-chronic liver failure score (CLIF-C-ACLFs) models, we assessed the predictive accuracy of the nomogram using the concordance index (C-index), and its discriminative ability using time-dependent receiver operating characteristics (td-ROC) analysis, respectively.

RESULTS

Multivariate logistic regression analysis of the development set carried out to predict mortality revealed that γ-glutamyl transpeptidase, albumin, total bilirubin, urea nitrogen, creatinine, international normalized ratio, and neutrophil-to-lymphocyte ratio were independent factors, all of which were incorporated into the new nomogram to predict the mortality of HEV-ALF patients. The area under the curve of this nomogram for mortality prediction was 0.671 (95% confidence interval: 0.602-0.740), which was higher than that of the MELD and CLIF-C-ACLFs models. Moreover, the td-ROC and decision curves analysis showed that both discriminative ability and threshold probabilities of the nomogram were superior to those of the MELD and CLIF-C-ACLFs models. A similar trend was observed in the validation set.

CONCLUSIONS

The novel nomogram is an accurate and efficient mortality prediction method for HEV-ALF patients.

Construction of a nomogram for the prediction of prognosis in patients with resectable gastric cancer undergoing fewer than sixteen lymph node biopsies

Background

Lymph node metastases evaluation is important for assessing gastric cancer prognosis. In patients not undergoing adequate lymph node biopsy, lymph node stage migration occurs with the use of the existing staging system. This study established a prediction model to improve prognostication in patients undergoing fewer than 16 lymph nodes biopsy.

Patients and methods

In total, 3036 eligible patients from the Surveillance, Epidemiology, and End Results Program database were evaluated. They were randomized into development and validation sets in a 1:1 ratio (n=1520 and 1516, respectively). To avoid model overfitting and loss of important factors, prognostic factors related to overall survival (OS) were screened according to the Akaike information criterion. The nomogram was assessed using discrimination and consistency tests in the development and validation sets; the concordance index (C-index), calibration curves, and receiver operating characteristic (ROC) curves were also evaluated. Comparison with the 7th American Joint Committee on Cancer (AJCC) staging system was based on Kaplan–Meier curves, ROC, risk stratification, and decision curve analysis (DCA).

Results

Age, race, degree of differentiation, invasion depth, chemotherapy, radiotherapy, and lymph node ratio were independent prognostic factors in OS. C-indices of the development and validation sets were 0.759 (95% CI: 0.741–0.777) and 0.742 (95% CI: 0.713–0.771), respectively; calibration curves were approximately 45° diagonal, indicating good predictive ability of the nomogram. In contrast to the 7th AJCC staging system, the Kaplan–Meier curves and risk stratification of the nomogram had better discrimination ability, the ROC curves of the nomogram achieved more predictive accuracy, and the DCA indicated that the nomogram conferred higher net benefit.

Conclusion

Our constructed nomogram predicts the prognosis of patients with resectable gastric cancer undergoing biopsy of fewer than 16 lymph nodes more precisely and has better clinical applicability than the 7th AJCC staging system.