Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Preoperative Biomarker Panel With Fibrinogen, FVIII, and CA 19-9 as a Tool for Prognostic Evaluation in Neoadjuvant-Treated Pancreatic Ductal Adenocarcinoma.

Cancer medicine·2026

Same author

Lacrimal sac squamous cell carcinoma: diagnostic challenges, surgical management and clinical outcomes.

Orbit (Amsterdam, Netherlands)·2026

Same author

Fiber type-specific expression of LACTB leverages a function in oxidative metabolism.

Histochemistry and cell biology·2026

Same author

Actinomyces lesions and acute inflammation predominate in osteonecrosis of the jaw associated with osteoclast-suppressing therapy in contrast to non-medication-related osteonecrosis.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology·2026

Same author

Demographic and Clinical Characteristics of Early-Onset Colorectal Cancer in Sweden and Finland: A Multicentre Retrospective Cohort Study Over Three Decades.

Journal of surgical oncology·2026

Same author

CXCL12 and eotaxin are independent prognostic serum biomarkers in gastric cancer.

Scientific reports·2026

Same journal

Modeling and analysis of forward and inverse kinematics for a flexible Stewart platform.

PloS one·2026

Same journal

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review.

PloS one·2026

Same journal

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

PloS one·2026

Same journal

Key targets and mechanisms by which gut microbiota-derived metabolites regulate Alzheimer's disease through the immune - inflammatory pathway: Based on network pharmacology and molecular docking.

PloS one·2026

Same journal

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications.

PloS one·2026

Same journal

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 2, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

A deep learning-based algorithm for tall cell detection in papillary thyroid carcinoma.

Sebastian Stenman^1,2,3, Nina Linder^1,4, Mikael Lundin¹

¹Institute for Molecular Medicine Finland - FIMM, University of Helsinki, Helsinki, Finland.

|August 9, 2022

Summary

This summary is machine-generated.

A new deep learning algorithm accurately detects tall cells (TCs) in papillary thyroid carcinoma (PTC). Higher TC scores identified by the algorithm predict poorer relapse-free survival in PTC patients.

More Related Videos

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

Related Experiment Videos

Last Updated: Sep 2, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

Area of Science:

Oncology
Pathology
Artificial Intelligence in Medicine

Background:

The tall cell variant (TCV) of papillary thyroid carcinoma (PTC) is an aggressive subtype, but its definition leads to diagnostic challenges and interobserver variability.
Accurate identification and quantification of tall cells (TCs) are crucial for assessing PTC aggressiveness and patient prognosis.
Current methods for TC assessment are subjective, highlighting the need for objective and reproducible diagnostic tools.

Purpose of the Study:

To develop and validate a deep learning algorithm for the automated detection and quantification of tall cells (TCs) in papillary thyroid carcinoma (PTC).
To evaluate the algorithm's performance in comparison to human assessment.
To determine the association between algorithm-derived TC scores and patient disease outcomes, specifically relapse-free survival.

Main Methods:

A deep learning algorithm was trained using supervised learning on PTC samples.
The algorithm was tested on an independent dataset and validated on 90 PTC samples from Helsinki.
Algorithm-based TC percentages were compared to visual scoring by a human investigator and correlated with disease outcomes and tumor relapse samples.

Main Results:

The deep learning algorithm achieved high sensitivity (93.7%) and specificity (94.5%) in detecting TCs.
Algorithm-derived TC scores significantly correlated with diminished relapse-free survival at various cutoff points (10%, 20%, 30%).
Visually assessed TC scores did not significantly predict survival, unlike the algorithm's findings.

Conclusions:

A novel deep learning algorithm effectively detects tall cells in PTC, offering an objective measure.
A high deep learning-based TC score is a statistically significant predictor of less favorable relapse-free survival in PTC.
This AI-driven approach has the potential to improve the accuracy and consistency of PTC subtyping and prognostic assessment.