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

Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...

You might also read

Related Articles

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

Sort by

Same author

A gold-standard French-language annotated corpus of oncological entities with ICD-O normalisation.

Scientific data·2026

Same author

Precision Rehabilitation in Spinal Cord Injury: A Systematic Review of Omics Applications for Intervention Monitoring in Spinal Cord Injury.

Archives of rehabilitation research and clinical translation·2026

Same author

Clarifying the relationship between biomedical and health informatics and digital health: expert perspectives.

BMJ health & care informatics·2026

Same author

Higher disease reactivation risk in women after fingolimod withdrawal.

Acta neuropathologica communications·2026

Same author

QT prolongation alerts lead to monitoring but rarely to therapeutic changes: a prospective hospital study.

Frontiers in pharmacology·2026

Same author

Can LLMs Turn French PET/CT Narrative Reports into Structured Knowledge?

Studies in health technology and informatics·2026

Same journal

Artificial intelligence in pharmacy practice: pharmacists' perceptions and concerns toward implementation.

Frontiers in digital health·2026

Same journal

A European framework for the assessment of digital health technologies: conceptual advances, challenges, and future directions.

Frontiers in digital health·2026

Same journal

Understanding digital health literacy in the arab world: a study of arab adults with diabetes, hypertension, and rheumatoid arthritis residing in Qatar.

Frontiers in digital health·2026

Same journal

Digital epidemiology and public health surveillance: scientometric mapping of emerging technologies and challenges (2000-2025).

Frontiers in digital health·2026

Same journal

Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.

Frontiers in digital health·2026

Same journal

Sugar slay: a gamified decision support ecosystem for type 1 diabetes.

Frontiers in digital health·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 25, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Multilingual RECIST classification of radiology reports using supervised learning.

Luc Mottin^1,2, Jean-Philippe Goldman³, Christoph Jäggli⁴

¹HES-SO\HEG Genève, Information Sciences, Geneva, Switzerland.

Frontiers in Digital Health

|June 30, 2023

Summary

This summary is machine-generated.

This study explores AI and NLP for automatic RECIST classification from radiology reports. The best models achieve high accuracy, comparable to expert labeling, and generalize well to new data.

Keywords:

RECIST language models narrative text classification radiology reports supervised machine learning

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Jul 25, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Medical Informatics
Artificial Intelligence
Natural Language Processing

Background:

Accurate tumor response assessment is crucial for cancer treatment.
Manual RECIST classification from radiology reports can be time-consuming and subjective.
Automating RECIST classification can improve efficiency and consistency.

Purpose of the Study:

To explore AI and NLP techniques for automatic RECIST classification.
To evaluate the impact of language (French, German) and institutional factors on classification quality.
To assess the generalizability and accuracy of developed models.

Main Methods:

Evaluated 7 machine learning methods for baseline performance.
Developed and fine-tuned models for French and German languages.
Compared model performance against expert annotations using metrics like F1-score, Matthew's correlation coefficient, and Cohen's Kappa.

Main Results:

Achieved average F1-scores of 90% for 2-class (Progressive/Non-progressive) and 86% for 4-class RECIST classification.
Performance metrics were competitive with manual labeling (MCC 79%, Kappa 76%).
Demonstrated model generalizability on unseen data.

Conclusions:

AI and NLP techniques effectively support automatic RECIST classification.
Language and institutional specificities have a manageable impact on classification quality.
Pre-trained Language Models (PLMs) can enhance classifier accuracy.