Stereotactic radiotherapy is a useful treatment option for patients with medullary thyroid cancer

Fundamentals and recent advances in the evaluation and management of medullary thyroid carcinoma

Medullary thyroid carcinoma (MTC) is a rare primary neuroendocrine thyroid carcinoma that is distinct from other thyroid or neuroendocrine cancers. Most cases of MTC are sporadic, although MTC exhibits a high degree of heritability as part of the multiple endocrine neoplasia syndromes. REarranged during Transfection (RET) mutations are the primary oncogenic drivers and advances in molecular profiling have revealed that MTC is enriched in druggable alterations. Surgery at an early stage is the only chance for cure, but many patients present with or develop metastases. C-cell-specific calcitonin trajectory and structural doubling times are critical biomarkers to inform prognosis, extent of surgery, likelihood of residual disease, and need for additional therapy. Recent advances in the role of active surveillance, regionally directed therapies for localized disease, and systemic therapy with multi-kinase and RET-specific inhibitors for progressive/metastatic disease have significantly improved outcomes for patients with MTC.

Machine Learning for Predicting Distant Metastasis of Medullary Thyroid Carcinoma Using the SEER Database

Objectives

We aimed to establish an effective machine learning (ML) model for predicting the risk of distant metastasis (DM) in medullary thyroid carcinoma (MTC).

Methods

Demographic data of MTC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the National Institutes of Health between 2004 and 2015 to develop six ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1-score, and area under the receiver operating characteristic curve (AUC). The association between clinicopathological characteristics and target variables was interpreted. Analyses were performed using traditional logistic regression (LR).

Results

In total, 2049 patients were included and 138 developed DM. Multivariable LR showed that age, sex, tumor size, extrathyroidal extension, and lymph node metastasis were predictive features for DM in MTC. Among the six ML models, the random forest (RF) had the best predictability in assessing the risk of DM in MTC, with an accuracy, precision, recall rate, F1-score, and AUC higher than those of the traditional binary LR model.

Conclusion

RF was superior to traditional LR in predicting the risk of DM in MTC and can provide a valuable reference for clinicians in decision-making.

A prediction model for the 5-year, 10-year and 20-year mortality of medullary thyroid carcinoma patients based on lymph node ratio and other predictors

Aim

To explore the predictive value of lymph node ratio (LNR) for the prognosis of medullary thyroid carcinoma (MTC) patients, and constructed prediction models for the 5-year, 10-year and 20-year mortality of MTC patients based on LNR and other predictors.

Methods

This cohort study extracted the data of 2,093 MTC patients aged ≥18 years undergoing total thyroidectomy and neck lymph nodes dissection. Kaplan-Meier curves and log-rank tests were performed to compare survival curves between LNR < 15% group and LNR ≥ 15% group. All data was divided into the training set (n = 1,465) and the testing set (n = 628). The random survival forest model was constructed in the training set and validated in the testing set. The area under the curve (AUC) was employed for evaluating the predictive ability of the model.

Results

The 5-year, 10-year and 20-year overall survival (OS) and cause-specific survival (CSS) of MTC patients with LNR <15% were higher than those with LNR ≥15%. The OS was 46% and the CSS was 75% after 20 years' follow-up. The AUC of the model for the 5-year, 10-year, and 20-year OS in MTC patients was 0.878 (95%CI: 0.856-0.900), 0.859 (95%CI: 0.838-0.879) and 0.843 (95%CI: 0.823-0.862) in the training set and 0.845 (95%CI: 0.807-0.883), 0.841 (95%CI: 0.807-0.875) and 0.841 (95%CI: 0.811-0.872) in the testing set. In the training set, the AUCs were 0.869 (95%CI: 0.845-0.892), 0.843 (95%CI: 0.821-0.865), 0.819 (95%CI: 0.798-0.840) for the 5-year, 10-year and 20-year CCS in MTC patients, respectively. In the testing set, the AUCs were 0.857 (95%CI: 0.822-0.892), 0.839 (95%CI: 0.805-0.873) and 0.826 (95%CI: 0.794-0.857) for the 5-year CCS, 10-year CCS and 20-year CCS in MTC patients, respectively.

Conclusion

The models displayed good predictive performance, which might help identify MTC patients might have poor outcomes and appropriate interventions should be applied in these patients.