Management of lymph nodes in medullary thyroid carcinoma: A review

Shaghaghi, Ali; Salari, Abolfazl; Jalaeefar, Amirmohsen; Shirkhoda, Mohammad

doi:10.1016/j.amsu.2022.104538

Cited by 4 publications

(1 citation statement)

References 39 publications

(52 reference statements)

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“…In patents with locally advanced or metastatic MTC, in addition to thyroidectomy, dissection of compartments with involved lymph nodes is often recommended. For this reason, during dissection of the central and lateral zones of the neck, proceedings that cause damage to speech, swallowing, shoulder movements and parathyroid glands should be avoided [ 2 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Competing-risks model for predicting the prognostic value of lymph nodes in medullary thyroid carcinoma

Shang,

Liu,

Ren

et al. 2023

PLoS ONE

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Background Medullary thyroid carcinoma (MTC) is an infrequent form malignant tumor with a poor prognosis. Because of the influence of competitive risk, there may suffer from bias in the analysis of prognostic factors of MTC. Methods By extracting the data of patients diagnosed with MTC registered in the Surveillance, Epidemiology, and End Results (SEER) database from 1998 to 2016, we established the Cox proportional-hazards and competing-risks model to retrospectively analyze the impact of related factors on lymph nodes statistically. Results A total of 2,435 patients were included in the analysis, of which 198 died of MTC. The results of the multifactor competing-risk model showed that the number of total lymph nodes (19–89), positive lymph nodes (1–10,11–75) and positive lymph node ratio (25%-53%,>54%), age (46–60,>61), chemotherapy, mode of radiotherapy (others), tumor size(2-4cm,>4cm), number of lesions greater than 1 were poor prognostic factors for MTC. For the number of total lymph nodes, unlike the multivariate Cox proportional-hazards model results, we found that it became an independent risk factor after excluding competitive risk factors. Competitive risk factors have little effect on the number of positive lymph nodes. For the proportion of positive lymph nodes, we found that after excluding competitive risk factors, the Cox proportional-hazards model overestimates its impact on prognosis. The competitive risk model is often more accurate in analyzing the effects of prognostic factors. Conclusions After excluding the competitive risk, the number of lymph nodes, the number of positive and the positive proportion are the poor prognostic factors of medullary thyroid cancer, which can help clinicians more accurately evaluate the prognosis of patients with medullary thyroid cancer and provide a reference for treatment decision-making.

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Section: Discussionmentioning

confidence: 99%

Competing-risks model for predicting the prognostic value of lymph nodes in medullary thyroid carcinoma

Shang,

Liu,

Ren

et al. 2023

PLoS ONE

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Diagnosis and therapy of thyroid cancer

Schüle¹,

Darr²,

Anneken³

et al. 2023

MMW - Fortschritte der Medizin

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

Luvhengo,

Moeng,

Sishuba

et al. 2024

Cancers

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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.

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Management of lymph nodes in medullary thyroid carcinoma: A review

Cited by 4 publications

References 39 publications

Competing-risks model for predicting the prognostic value of lymph nodes in medullary thyroid carcinoma

Competing-risks model for predicting the prognostic value of lymph nodes in medullary thyroid carcinoma

Diagnosis and therapy of thyroid cancer

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

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