Submitted:
03 September 2024
Posted:
03 September 2024
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Abstract
Keywords:
1. Introduction
2. Management of Medullary Thyroid Carcinoma
2.1. Extent of Thyroidectomy for Medullary Thyroid Carcinoma
2.2. Extent of Cervical Lymphadenectomy
2.3. Management of Locally Advanced or Metastatic Medullary Thyroid Carcinoma
2.4. Post-Operative Follow-Up
2.5. Management of Persistent or Recurrent Disease Medullary Thyroid Carcinoma
3. Challenges Associated with Management of Medullary Thyroid Carcinoma
3.1. Serum Calctonin
3.2. Hereditary MTC
3.3. Sporadic MTC
3.4. Targeted Therapy
4. Artificial Intelligence in the Healthcare Industry
5. Application of Artificial Intelligence during the Investigation of Medullary Thyroid Carcinoma
5.1. Radiomics
5.2. Pathomics

5.3. Epigenomics
5.4. Other Omics for Investigation and Management of Cancer
6. Potential Application of Artificial Intelligence in MTC

6.1. Metastatic Work-Up of Medullary Thyroid Carcinoma
6.2. Risk Stratification of Medullary Thyroid Carcinoma
6.3. Treatment of Locally Advanced and Metastatic Medullary Thyroid Carcinoma
| Target | Omics option | References |
|---|---|---|
| Diagnosis | Fluidomics Genomics Glycomics Metabolomics Pathomics Proteomics Radiomics Trascriptomics |
[4] [165] [162] [160] [12,145] [177] [104,111] [5] |
| Staging | Fluidomics Metabolomics Radiomics Pathomics Transcriptomics |
[4,178] [155] [111,156,173] [146,147] [5] |
| Risk stratification | Epigenomics Fluidomics Genomics Glycomics Immunomics Pathomics Radiomics Transcriptomics |
[55] [4,99,173,176] [94] [177] [47,174] [146,147,156] [173] [5,12] |
| Selection of treatment | Fluidomics Genomics Immunomics Pathomics Radiomics Transcriptomics |
[176] [99] [47] [147] [141] [5,8,12] |
| Follow-up | Delta radiomics Fluidomics Genomics Metabolomics |
[143] [4] [176] [155] |
7. Conclusion and Perspectives
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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| Algorithm | Application | Function | Example |
|---|---|---|---|
| Convolutional neural network [125,126] | Image analysis (ultrasound, CT, MRI) | Automatically identify features indicative of MTC | Distinguish between benign and malignant thyroid nodules with high accuracy |
| Support vector machine (SVM) [127] | Classification of thyroid lesions | Find the optimal hyperplane that separates different classes of data points | Classify MTC patients based on genetic mutations and protein expression profiles |
|
Random forest (RF) [128] |
Classification and regression | Build multiple decision trees and merge them to improve predictive accuracy | Predict patient outcomes and treatment responses based on clinical, genetic, and proteomic data |
| K-nearest Neighbours (KNN) regression [129] | Classification and regression | Classify a data point based on the classification of its neighbors | Classify thyroid nodules based on ultrasound features to determine likelihood of malignancy |
| ANN [130] | Complex pattern recognition | Process data in layers to learn intricate patterns | Integrate multi-omics data to predict disease progression and patient survival |
| Gradient bosting model (GBM) [131] | Classification and regression | Build models sequentially, each correcting errors of the previous ones | Predict recurrence risk in MTC patients by analyzing clinical and molecular data |
| Recurrent neural network (RNN) [130] | Time-series prediction and sequential data analysis | Maintain a memory of previous inputs to predict future outcomes | Analyze longitudinal patient data to predict future disease progression and treatment outcomes |
| Autoencoders [132] | Data dimensionality reduction and feature extraction | Compress data into a lower-dimensional representation and reconstruct it back | Identify key features in genetic and proteomic data that are most indicative of MTC |
|
Bayesian Networks [130] |
Probabilistic inference and decision-making | Represent variables and their conditional dependencies through directed acyclic graphs | Model relationships between genetic mutations, environmental factors, and MTC development |
| Natural language processing (NLP) [133] | Process and analyze unstructured clinical texts | Extract relevant information from EHRs, pathology reports, and scientific literature | Extract patient data and clinical outcomes related to MTC, integrating with omics data for comprehensive analysis |
| Geolocation [134] | Epidemiology and public health planning | Mapping the geographical distribution of MTC cases to identify environmental and genetic risk factors; planning targeted screening programs and resource allocation | Identifying regions with higher incidence rates of MTC to implement targeted screening programs and allocate resources effectively; correlating regional dietary habits and environmental exposures with MTC incidence |
| Survival Analysis [135] | Prognostic predictions and patient stratification | Estimating time until events (disease progression, recurrence, death) and identifying prognostic factors | Developing risk stratification models based on clinical, genetic, and demographic variables to predict patient outcomes and tailor follow-up and monitoring strategies |
|
Lean Six Sigma [136] |
Process optimization and efficiency in clinical workflows | Streamlining clinical processes, reducing diagnostic errors, and improving treatment workflows by eliminating inefficiencies | Standardizing procedures for sample collection and data integration to reduce variability and improve the reliability of holomic analyses; ensuring consistent follow-through on diagnostic and treatment protocols |
| Name of miRNA | Expression | Consequences | References |
|---|---|---|---|
| miR-375 | Overexpressed | Diagnosis, Lateral lymph nodes predicted, worse prognosis. Distinguishing hereditary from sporadic MTC. |
[30,149,150] |
| miR-127 | Underexpressed | Aggressive sporadic disease. | [149,151] |
| miR-429 | Overexpressed | Not yet specified | [149] |
| miR-592 | Overexpressed | Poor prognosis. | [106] |
| miR-224 | Underexpressed | Poor prognosis | [30] |
| miR-199-5p | Underexpressed | Not yet specified | [149] |
| miR-199a-3p | Underexpressed | Not yet specified | [149] |
| miR-34a | Underexpressed | Biomarker of MTC | [152] |
| miR-9 | Underexpressed | Distinguishing hereditary versus sporadic | [153] |
| miR-21 | Overexpressed | Prediction of lymph node and distant metastasis | [30] |
| miR-144 | Overexpressed | Biomarker of MTC | [152] |
| miR—183 | Overexpressed | Prediction of lateral lymph nodes involvement, distant metastasis and high mortality, and distinguishing hereditary from sporadic MTC. | [30,153] |
| Application | Description | Examples/Impact |
|---|---|---|
| Enhanced diagnostic accuracy [162] | AI improves diagnostic accuracy for MTC by analyzing imaging data, integrating multiple data sources including imaging, genetic and clinical, and identifying subtle features indicative of MTC. Traditional methods like ultrasound and fine-needle aspiration can be inconclusive. | AI-powered image recognition systems distinguish between benign and malignant thyroid nodules more accurately than human radiologists, leading to early and accurate diagnosis essential for effective treatment of MTC. |
| Personalized treatment plans [163] | AI personalizes treatment plans by analysing genetic and molecular data to identify specific mutations and biomarkers associated with MTC. It predicts patient responses to targeted therapies, optimizing treatment efficacy and minimizing side effects. AI updates treatment recommendations as new data become available. | AI guides the selection of targeted therapies such as tyrosine kinase inhibitors, ensuring that patients with MTC receive the most current and effective treatments based on their unique genetic profile. |
| Prognostic predictions [164] | AI develops predictive models to estimate disease progression and patient outcomes by integrating diverse data points like stage of cancer, genetic mutations and patient’s characteristics. Machine learning algorithms analyze historical patient data to identify patterns and risk factors associated with recurrence or metastasis. | AI helps clinicians stratify patients into different risk categories and tailor follow-up and monitoring strategies, providing more accurate prognostic information and improving long-term management of MTC. |
| Holomic integration [165] | Holomics integrates various omics data to provide a holistic view of MTC at the molecular level. AI analyze and interprets complex datasets to identify gene expression patterns, detect protein biomarkers, and analyze metabolic profiles, offering a more complete understanding of the disease. | AI-enabled holomics uncovers novel insights into MTC pathogenesis and identifies new therapeutic targets, leading to better diagnostic and therapeutic strategies. |
| Comparative insights in HICs versus LMICs [166] | AI application varies between high-income countries (HICs) and low- and middle-income countries (LMICs). HICs benefit from advanced healthcare infrastructure and cutting-edge technologies, while LMICs face challenges like limited resources and insufficient training. AI can bridge these gaps by deploying diagnostic tools via mobile health platforms and optimizing resource use. | AI-driven diagnostic tools enable remote diagnosis and expert consultations in resource-limited settings, making high-quality cancer care more accessible and efficient. This reduces disparities between HICs and LMICs in MTC management. |
| Future prospects [167] | The future of AI in MTC investigation is promising with continued advancements in AI algorithms and the growing availability of comprehensive holomic datasets. Collaborative efforts between researchers, clinicians, and AI experts will develop and validate tailored AI tools. The development of interpretable AI models will be crucial for clinical acceptance. | Expanding AI applications to other areas of thyroid cancer research, such as risk stratification and the discovery of novel therapeutic targets, holds great potential for improving patient outcomes. AI will play an increasingly integral role in the investigation and management of MTC, transforming the landscape of thyroid cancer care. |
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