Prognosis of radiotherapy in medullary thyroid carcinoma patients without distant metastasis

Limited Thyroidectomy Achieves Equivalent Survival to Total Thyroidectomy for Early Localized Medullary Thyroid Cancer

BACKGROUND

The optimal surgical approach for localized T1 medullary thyroid cancer remains unclear. Total thyroidectomy is standard, but lobectomy and subtotal thyroidectomy may minimize mortality while maintaining oncologic control.

METHODS

This retrospective analysis utilized the National Cancer Institute's Surveillance, Epidemiology, and End Results registry to identify 2702 MTC patients including 398 patients with T1N0/1M0 MTC treated with total thyroidectomy or lobectomy/subtotal thyroidectomy from 2000 to 2019. Cox regression analyses assessed thyroid cancer-specific and overall mortality.

RESULTS

The majority (89.7%) underwent total thyroidectomy, while 10.3% had lobectomy/subtotal thyroidectomy. Nodal metastases were present in 29.6%. Over a median follow-up of 8.75 years, no significant difference was observed in cancer-specific mortality (5.7% vs. 8.1%, p = 0.47) or overall mortality (13.2% vs. 12.8%, p = 0.95). On multivariate analysis, undergoing cancer-directed surgery was associated with significantly improved overall survival (HR 0.18, p < 0.001) and cancer-specific survival (HR 0.17, p < 0.001) compared to no surgery. However, no significant survival difference was seen between total thyroidectomy and lobectomy/subtotal thyroidectomy for overall mortality (HR 0.77, p = 0.60) or cancer-specific mortality (HR 0.44, p = 0.23). The extent of surgery also did not impact outcomes within subgroups stratified by age, gender, T stage, or nodal status. Delayed surgery >1 month after diagnosis was associated with worse overall survival (p = 0.012).

CONCLUSIONS

For localized T1 MTC, lobectomy/subtotal thyroidectomy appears to achieve comparable long-term survival to total thyroidectomy in this population-based analysis. The selective use of limited thyroidectomy may be reasonable for low-risk T1N0/1M0 MTC patients. Delayed surgery is associated with worse survival and additional neck dissection showed no benefit for this select group of patients.

Holomics and Artificial Intelligence-Driven Precision Oncology for Medullary Thyroid Carcinoma: Addressing Challenges of a Rare and Aggressive Disease

Background/Objective: Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer comprising a disproportionate share of thyroid cancer-related mortalities, despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, which are designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for patients with MTC. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, which is an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. Methods: This article reviews how AI-driven precision oncology can enhance the diagnostic workup, staging, risk stratification, management, and follow-up care of patients with MTC by processing vast amounts of complex data quickly and accurately. Articles published in English language and indexed in Pubmed were searched. Results: AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in the management of MTC enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. Conclusions: The integration of holomics and AI in the management of patients with MTC represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of treatment and care of patients with MTC. By leveraging AI and holomics, we can strive toward making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being at all ages.

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.