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Related Concept Videos

Targeted Cancer Therapies02:57

Targeted Cancer Therapies

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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.
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Related Experiment Video

Updated: Jan 10, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive

Bo Yang1,2, Silin Chen1,2, Yunze Wang1,2

  • 1Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital, and Centre for Infection Immunity and Cancer (IIC) of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.

Journal of Translational Medicine
|November 25, 2025
PubMed
Summary

Radiology-based artificial intelligence (AI) models can predict patient response to targeted cancer therapy non-invasively. This review of 112 studies shows AI

Keywords:
AIPan-cancerPrognosis and response predictionRadiologyTargeted therapy

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Precision oncology relies on targeted therapy, but predicting patient response is difficult.
  • Conventional molecular testing has limited predictive value for treatment efficacy.
  • Artificial intelligence (AI) and medical imaging offer new non-invasive methods for assessing treatment response.

Purpose of the Study:

  • To review and synthesize the current state of radiology-based AI models for predicting targeted therapy response.
  • To classify AI approaches into direct and indirect prediction strategies.
  • To identify common cancer types, imaging modalities, and AI frameworks used in this field.

Main Methods:

  • A comprehensive literature review of 112 studies was conducted.
  • Studies developing AI models for predicting targeted therapy response were analyzed.
  • Models were categorized into direct (end-to-end imaging) and indirect (biomarker inference) prediction methods.

Main Results:

  • Computed tomography (CT) was the most common imaging modality, followed by MRI, PET, and US.
  • Lung and breast cancers were most frequently studied, with expansion into other cancer types.
  • Both machine learning (ML) and deep learning (DL) were used, with ML dominant but DL growing.

Conclusions:

  • Radiology-based AI provides a non-invasive tool to guide targeted therapy selection and monitoring.
  • The review highlights the progress, strengths, and limitations of different AI prediction strategies.
  • Future directions and resources are discussed to support accessibility and further research.