ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2021
Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.The Journal of clinical endocrinology and metabolism · 2024Pooled it
- The Application of Artificial Intelligence in Thyroid Nodules: A Systematic Review Based on Bibliometric Analysis.Endocrine, metabolic & immune disorders drug targets · 2024Pooled it
- Artificial Intelligence for Thyroid Nodule Diagnosis: From Multimodal Integration to Clinical Utility.Diagnostics (Basel, Switzerland) · 2026Review
- From bench to bedside: the evolving role of iodine in thyroid cancer management.Nature reviews. Endocrinology · 2026Review
- Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images.Journal of imaging informatics in medicine · 2026Article
- Occupational dust exposure and thyroid nodule screening: A predictive modeling approach for early detection in coal mining communities.The Journal of international medical research · 2026Article
- PathoTiroid dataset: Indonesian collection (PTIC)-a histopathology image dataset for papillary thyroid carcinoma.BMC research notes · 2026Article
- From Tissue Architecture To Genetic Signature: Artificial intelligence-based Analysis of Reticulin Framework and Clinical Variables Predicts Molecular Cluster in Paragangliomas.Endocrine pathology · 2026Article
- Article
- A Novel Three-Stage AI-Assisted Approach for Accurate Differential Diagnosis and Classification of NIFTP and Thyroid Neoplasms.Endocrine pathology · 2025Article
- External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study.Journal of pathology informatics · 2024Article
- Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.Tissue engineering. Part A · 2024Review
- Pathology diagnosis of intraoperative frozen thyroid lesions assisted by deep learning.BMC cancer · 2024Article
- Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma.NPJ precision oncology · 2024Article
- Slideflow: deep learning for digital histopathology with real-time whole-slide visualization.BMC bioinformatics · 2024Article
- Multiple augmented reduced rank regression for pan-cancer analysis.Biometrics · 2024Article
- Breaking Barriers: AI's Influence on Pathology and Oncology in Resource-Scarce Medical Systems.Cancers · 2023Review
- Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images.Scientific reports · 2023Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Deep learning generates synthetic cancer histology for explainability and education.NPJ precision oncology · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) are follicular-patterned thyroid neoplasms defined by nuclear atypia and indolent behavior. They harbor RAS mutations, rather than BRAF
Indexed as
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.