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Advances in Precision Oncology for Thyroid Carcinoma: Integrating Artificial Intelligence,...
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Advances in Precision Oncology for Thyroid Carcinoma: Integrating Artificial Intelligence, Genomics and Molecular Diagnostics

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 1904

Editor

Dr. Kyoung Sik Park

E-Mail Website
Guest Editor
Department of Surgery, Konkuk University Medical Centre, Konkuk University School of Medicine, 120 Neungdong-ro, Gwangjin-gu, Seoul 143-729, Republic of Korea
Interests: minimal invasive and robotic surgery in breast and endocrine disease; precision medicine and genomic research in surgical oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thyroid carcinoma, particularly differentiated thyroid carcinoma (DTC), has become increasingly prevalent, with diverse clinical behaviors ranging from indolent nodules to aggressive, treatment-resistant variants. As traditional risk stratification approaches face limitations in capturing the underlying tumor biology, there is a growing need for precision medicine frameworks incorporating genomic, radiologic, and molecular data.

This Special Issue will explore recent advances in artificial intelligence (AI), next-generation sequencing (NGS), and multi-omics diagnostics which are transforming the landscape of thyroid cancer diagnosis, prognostication, and management.

Additionally, contributions to this issue will highlight the role of genomic profiling—such as for BRAF V600E, TERT promoter mutations, and RET/PTC rearrangements—in guiding targeted therapy and patient-specific management. Innovative approaches using single-cell transcriptomics, digital pathology, and machine learning-based integrative models will also be featured, emphasizing the importance of a multi-modal approach in endocrine oncology.

By bringing together interdisciplinary insights from surgery, oncology, pathology, bioinformatics, and molecular diagnostics, this Special Issue will offer a comprehensive view of the future of personalized care in thyroid cancer and establish a platform for collaborative innovation in surgical precision oncology.

Dr. Kyoung Sik Park
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • thyroid carcinoma
  • artificial intelligence
  • genomics
  • precision oncology
  • molecular diagnostics

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Published Papers (1 paper)

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Research

13 pages, 1629 KB  
Article
Sex-Stratified Prediction Models for 5-Year Nonalcoholic Fatty Liver Disease Risk in Thyroid Cancer Patients: A Nationwide Cohort Study
by Young Bin Cho and Kyoung Sik Park
Biomedicines 2025, 13(9), 2250; https://doi.org/10.3390/biomedicines13092250 - 12 Sep 2025
Viewed by 1452
Abstract
Background/Objectives: Nonalcoholic fatty liver disease (NAFLD) is a significant complication among survivors of thyroid cancer; however, existing prediction models for NAFLD remain inadequate. Our objective was to develop survival prediction models for 5-year risk of NAFLD in patients diagnosed with thyroid cancer. [...] Read more.
Background/Objectives: Nonalcoholic fatty liver disease (NAFLD) is a significant complication among survivors of thyroid cancer; however, existing prediction models for NAFLD remain inadequate. Our objective was to develop survival prediction models for 5-year risk of NAFLD in patients diagnosed with thyroid cancer. Methods: Utilizing the Korean National Health Insurance Service claims database, we selected 3644 post-thyroidectomy patients with thyroid cancer between 2004 and 2014. Following a 7:3 stratified division into training and test datasets, we developed sex-stratified survival models using random survival forest (RSF) and Cox proportional hazards regression (Cox). The evaluation of prediction models was performed using Harrell’s concordance index (C-index), time-dependent area under the curve (AUC), and risk stratification analysis. Results: In the female cohort, the Cox model exhibited a superior C-index of 0.67 (95% CI 0.61–0.72), surpassing the RSF model, which had a C-index of 0.62 (95% CI 0.57–0.68). Notably, age-stratified Cox models for females demonstrated enhanced performance compared to the unstratified female Cox model. Conversely, male-specific models did not show significant performance in NAFLD. Risk stratification analysis revealed that the female-specific models effectively categorized patients into low- and high-risk groups, with statistical significance (p < 0.001). Conclusions: This study constructed well-performing time-to-event prediction models for NAFLD of female patients with thyroid cancer, which is significant in risk stratification. Full article
(This article belongs to the Special Issue Advances in Precision Oncology for Thyroid Carcinoma: Integrating Artificial Intelligence, Genomics and Molecular Diagnostics)
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