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

Request and reporting models for computed tomography in the multidisciplinary management of cancer patients: consensus between the Italian Society of Medical and Interventional Radiology (SIRM) and the Italian Society of Medical Oncology (AIOM).

La Radiologia medica·2026

Same author

Diagnostic performance of CT-based node-RADS for detecting metastatic lymph nodes in melanoma and comparison with short-axis and roundness index.

Clinical imaging·2026

Same author

Splenic FDG PET uptake and CT volume as prognostic biomarkers in diffuse large B cell lymphoma.

La Radiologia medica·2026

Same author

Interventional radiology management of disc herniation: a narrative review.

La Radiologia medica·2026

Same author

An alternative to inside-out access for central venous catheter placement in thoracic central vein obstruction.

The journal of vascular access·2026

Same author

Impact of neoadjuvant chemotherapy on breast tissue density and its correlation with pathologic response: a retrospective AI-Enhanced radiological study.

The British journal of radiology·2026

Search research articles

Related Experiment Video

Updated: Jun 30, 2025

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

Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy.

Michaela Cellina¹, Giuseppe De Padova², Nazarena Caldarelli²

¹Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy.

Critical Reviews in Oncogenesis

|March 20, 2024

Summary

Artificial intelligence (AI) offers powerful tools to improve lung cancer management. AI assists in precise diagnosis, treatment response prediction, and personalized patient care, enhancing overall outcomes.

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Related Experiment Videos

Last Updated: Jun 30, 2025

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

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Lung cancer presents a significant global health burden, necessitating advancements in diagnosis, prognosis, and treatment.
Current management strategies require enhanced precision, particularly in diagnosis and predicting patient outcomes.

Approach:

This review explores the multifaceted applications of artificial intelligence (AI) in lung cancer management.
AI-driven approaches, including deep learning models (e.g., U-Net, BCDU-Net), are evaluated for automating segmentation and feature extraction.
The integration of radiomic features with clinical data for treatment response prediction is examined.

Key Points:

AI automates lung nodule and cancer segmentation, reducing inter-observer variability and enabling objective quantification.
AI models extract radiomic features for tissue characterization and predict responses to immunotherapy and targeted therapies.
AI-based prognostic models aid in identifying high-risk patients and personalizing treatment strategies.

Conclusions:

Artificial intelligence is revolutionizing lung cancer care, from segmentation and virtual biopsy to outcome prediction.
AI applications enhance the precision and effectiveness of lung cancer diagnosis and treatment.
The continued evolution of AI holds significant potential to impact clinical practice and improve patient outcomes.