ArticleCell discovery2022
Artificial intelligence defines protein-based classification of thyroid nodules.
Article in Cell discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 71 citations in OpenAlex.
- MS-FGNet: An Attention-Guided Deep Learning Framework for Fine-Grained Pathological Subtype Prediction from DIA Proteomics Imaging.Journal of imaging informatics in medicine · 2026Article
- MSstatsQC-ML: A Supervised Machine Learning Approach to Monitor System Suitability and Quality Control in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Article
- Proteomic analysis reveals modulation of key proteins in follicular thyroid cancer progression.Chinese medical journal · 2026Article
- Proteogenomic characterization delineates clinically relevant subtypes of advanced differentiated thyroid cancer.Cell reports. Medicine · 2026Article
- A targeted proteomics assay for the preoperative diagnosis of thyroid nodules.Cell reports. Medicine · 2026Article
- ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and machine learning.Nature communications · 2026Article
- Multi-center multi-omics integration predicts individualized prognosis in medullary thyroid carcinoma.Nature communications · 2026Article
- The role of artificial intelligence in thyroid cytology of indeterminate nodules: from digital cytology to multimodal precision triage.Frontiers in endocrinology · 2026Review
- A deep learning model for multiclass lung cancer classification using multimodal data fusion.Discover oncology · 2025Article
- Predicting and Treating Pulmonary Fibrosis with Proteomic Biomarker Investigations.Biomedicines · 2025Review
- Proteomic Profiling of EUS-FNA Samples Differentiates Pancreatic Adenocarcinoma from Mass-Forming Chronic Pancreatitis.Biomedicines · 2025Article
- Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules.Endocrinology and metabolism (Seoul, Korea) · 2025Article
- Towards an Integrated Multi-Omic Approach to Improve the Diagnostic Accuracy of Fine-Needle Aspiration in Thyroid Nodules with Indeterminate Cytology.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 2025Article
- Advancements in proteogenomics for preclinical targeted cancer therapy research.Biophysics reports · 2025Article
- Review
- iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.Nature communications · 2025Article
- DiffBP: generative diffusion of 3D molecules for target protein binding.Chemical science · 2025Article
- Comparative urine proteomic study involving papillary thyroid carcinoma and benign thyroid nodules.Frontiers in oncology · 2025Article
- Towards routine proteome profiling of FFPE tissue: insights from a 1,220-case pan-cancer study.The EMBO journal · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
52 authors at 14 institutions in 3 countries.
Funding
Abstract
Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.