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Updated: Dec 6, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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[Artificial intelligence and machine learning in oncologic imaging].

Jens Kleesiek1,2,3, Jacob M Murray4,5, Christian Strack4,5

  • 1AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland. jens.kleesiek@uk-essen.de.

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PubMed
Summary
This summary is machine-generated.

Machine learning (ML) is transforming medicine, with artificial neural networks (ANNs) improving tumor identification and prognoses. These AI applications promise faster, reproducible, and cost-effective oncologic diagnostics and treatment.

Keywords:
Computer-assisted image processingDeep learningDiagnostic imagingMachine learningNeural networks (computer)

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Machine learning (ML) is increasingly integrated into various societal sectors, notably medicine.
  • The application of ML in medicine, particularly oncology, has the potential to significantly alter medical practice.
  • Current research indicates ML algorithms are achieving human-level or superior performance in critical oncologic tasks.

Purpose of the Study:

  • To highlight the transformative impact of machine learning in oncologic patient care.
  • To discuss the capabilities of ML algorithms, such as artificial neural networks (ANNs), in medical applications.
  • To project the future role of artificial intelligence (AI) in advancing oncologic diagnostics and treatment.

Main Methods:

  • Review of recent publications on ML applications in oncology.
  • Analysis of ML algorithm performance in tumor identification, classification, prognosis estimation, and treatment evaluation.
  • Examination of the characteristics of ML algorithms, including ANNs, regarding reproducibility, speed, and cost-effectiveness.

Main Results:

  • Computers, using ML, are demonstrating superior performance compared to humans in tumor identification and classification.
  • ML algorithms can accurately estimate prognoses and evaluate treatment effectiveness.
  • ANNs, a key ML technology, provide reproducible, fast, and inexpensive solutions for complex medical tasks.

Conclusions:

  • Machine learning is poised to become an integral component of medical practice.
  • AI applications offer significant advantages for oncologic diagnostics.
  • The integration of ML will enhance treatment strategies and patient outcomes in oncology.