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

Targeted Cancer Therapies

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...

You might also read

Related Articles

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

Sort by

Same author

Cross-Dataset Generalization of Deep Learning-Based Detectors for Intracranial Hemorrhage Subtype Localization on Noncontrast Head CT: A Comparative Study.

Diagnostics (Basel, Switzerland)·2026

Same author

A multi-task deep learning pipeline for classification, detection, and weakly supervised 3D segmentation of intraparenchymal hematoma on brain CT.

Scientific reports·2026

Same author

MYADM Activates RhoA-Mediated Ameboid Migration to Drive Metastasis.

Cancer research·2025

Same author

Interpretable multi-scale deep learning to detect malignancy in cell blocks and cytological smears of pleural effusion and identify aggressive endometrial cancer.

Medical image analysis·2025

Same author

Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2025

Same author

Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides.

NPJ precision oncology·2024

Search research articles

Related Experiment Video

Updated: Jul 19, 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

Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy.

Ching-Wei Wang¹, Kai-Lin Chu¹, Hikam Muzakky¹

¹Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

|August 12, 2023

Summary

A novel deep learning method enhances breast cancer diagnosis and HER2 gene amplification detection. This AI approach offers high accuracy and efficiency, making it suitable for clinical use.

Keywords:

breast cancer metastases breast cancer target therapy dilated soft label deep learning histopathology

More Related Videos

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

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

Related Experiment Videos

Last Updated: Jul 19, 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

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

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

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Breast cancer is a leading cause of death in women globally.
Accurate detection of human epidermal growth factor receptor-2 (HER2) gene amplification is crucial for targeted therapy.
Current manual microscopic analysis for HER2 detection is subjective and lacks reproducibility.

Purpose of the Study:

To introduce an efficient deep learning framework for breast cancer diagnosis and HER2 amplification detection.
To address the limitations of manual slide analysis in pathology.

Main Methods:

Development of a low-computing-cost deep learning method.
Application of the framework to breast cancer diagnosis and HER2 amplification detection on Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) slides.

Main Results:

The proposed deep learning framework achieved high precision and recall in clinical applications.
The method demonstrated superior performance compared to benchmark methods in Intersection over Union (IoU).
Significant reductions in AI training time (16.93%), AI inference time (17.25%), and memory usage (18.52%) were observed.

Conclusions:

The deep learning method provides accurate and reproducible results for breast cancer diagnosis and HER2 amplification detection.
The framework's efficiency and reduced resource requirements make it clinically feasible.