Nomogram for preoperative estimation risk of cervical lymph node metastasis in medullary thyroid carcinoma

Luo, Zhi-yan; Hong, Yanjun; Yan, Caoxin; Ye, Qin; Wang, Yong; Huang, Pintong

doi:10.3389/fonc.2022.883429

Cited by 3 publications

(9 citation statements)

References 40 publications

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“…Similar to our article, Luo et al. 16 developed a prediction model for cervical lymph node metastasis (CLNM) by applying clinical and ultrasonographic data of 74 patients with MTC. Their model had a high discrimination with an AUC of 0.919.…”

Section: Discussionmentioning

confidence: 76%

“…In contrast, Luo et al. 's 16 predictive model relies on eight thyroid ultrasound image features, which may pose challenges for dissemination and adoption across diverse medical institutions.…”

Section: Discussionmentioning

confidence: 99%

“…When considering the application of the model in different hospitals, the advantage of TI-RADS lies in its widespread clinical adoption, obviating the need for additional training for radiologists. In contrast, Luo et al's 16 predictive model relies on eight thyroid ultrasound image features, which may pose challenges for dissemination and adoption across diverse medical institutions.…”

Section: Discussionmentioning

confidence: 99%

“…There have been some studies trying to build models to predict LLNM of MTC, based on a limited sample size. 14 , 15 , 16 Zhou et al. 15 developed a nomogram based on the five clinical characteristics of 35 patients from single center, and their prediction model had discrimination with a C‐index of 0.825.…”

Section: Discussionmentioning

confidence: 99%

“…There have been some studies trying to build models to predict LLNM of MTC, based on a limited sample size 14–16 . Zhou et al 15 .…”

Section: Discussionmentioning

confidence: 99%

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The machine learning‐based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis

Zhang,

Zhao,

Jin

et al. 2024

Cancer Medicine

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BackgroundFor medullary thyroid carcinoma (MTC) with no positive findings in the lateral neck before surgery, whether prophylactic lateral neck dissection (LND) is needed remains controversial. A better way to predict occult metastasis in the lateral neck is needed.MethodsFrom January 2010 to January 2022, patients who were diagnosed with MTC and underwent primary surgery at our hospital were retrospectively reviewed. We collected the patients' baseline characteristics, surgical procedure, and rescored the ultrasound images of the primary lesions using American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI‐RADS). Regularized logistic regression, 5‐fold cross‐validation and decision curve analysis was applied for lateral lymph node metastasis (LLNM) model's development and validation. Then, we tested the predictive ability of the LLNM model for occult LLNM in cN0−1a patients.ResultsA total of 218 patients were enrolled. Five baseline characteristics and two TI‐RADS features were identified as high‐risk factors for LLNM: gender, baseline calcitonin (Ctn), tumor size, multifocality, and central lymph node (CLN) status, as well as TI‐RADS margin and level. A LLNM model was developed and showed a good discrimination with 5‐fold cross‐validation mean area under curve (AUC) = 0.92 ± 0.03 in the test dataset. Among cN0−1a patients, our LLNM model achieved an AUC of 0.91 (95% CI, 0.88–0.94) for predicting occult LLNM, which was significantly higher than the AUCs of baseline Ctn (0.83) and CLN status (0.64).ConclusionsWe developed a LLNM prediction model for MTC using machine learning based on clinical baseline characteristics and TI‐RADS. Our model can predict occult LLNM for cN0−1a patients more accurately, then benefit the decision of prophylactic LND.

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Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…There have been some studies trying to build models to predict LLNM of MTC, based on a limited sample size 14–16 . Zhou et al 15 .…”

Section: Discussionmentioning

confidence: 99%

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The machine learning‐based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis

Zhang,

Zhao,

Jin

et al. 2024

Cancer Medicine

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A practical nomogram for preoperatively predicting lateral cervical lymph node metastasis in medullary thyroid carcinoma: a dual-center retrospective study

Zhu,

Guo,

Guo

et al. 2024

Front. Endocrinol.

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PurposeLateral lymph node metastasis (LLNM) is very common in medullary thyroid carcinoma (MTC), but there is still controversy about how to manage cervical lateral lymph nodes, especially for clinically negative MTC. The aim of this study is to develop and validate a nomogram for predicting LLNM risk in MTC.Materials and methodsA total of 234 patients from two hospitals were retrospectively enrolled in this study and divided into LLNM positive group and LLNM negative group based on the pathology. The correlation between LLNM and preoperative clinical and ultrasound variables were evaluated by univariable and multivariable logistic regression analysis. A nomogram was generated to predict the risk of the LLNM of MTC patients, validated by external dataset, and evaluated in terms of discrimination, calibration, and clinical usefulness.ResultsThe training, internal, and external validation datasets included 152, 51, and 31 MTC patients, respectively. According to the multivariable logistic regression analysis, gender (male), relationship to thyroid capsule and serum calcitonin were independently associated with LLNM in the training dataset. The predictive nomogram model developed with the aforementioned variables showed favorable performance in estimating risk of LLNM, with the area under the ROC curve (AUC) of 0.826 in the training dataset, 0.816 in the internal validation dataset, and 0.846 in the external validation dataset.ConclusionWe developed and validated a model named MTC nomogram, utilizing available preoperative variables to predict the probability of LLNM in patients with MTC. This nomogram will be of great value for guiding the clinical diagnosis and treatment process of MTC patients.

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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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Nomogram for preoperative estimation risk of cervical lymph node metastasis in medullary thyroid carcinoma

Cited by 3 publications

References 40 publications

The machine learning‐based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis

The machine learning‐based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis

A practical nomogram for preoperatively predicting lateral cervical lymph node metastasis in medullary thyroid carcinoma: a dual-center retrospective study

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

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