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Related Experiment Video

Updated: May 11, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

Predicting outcome by images?

Dirk De Ruysscher1

  • 1Department of Radiation Oncology, University Hospitals Leuven/KU Leuven, Leuven, Belgium. dirk.deruysscher@uzleuven.be

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research
|May 30, 2013
PubMed
Summary
This summary is machine-generated.

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Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Imaging features from CT, MRI, and PET scans can predict survival in non-small cell lung carcinoma patients. This individualized, whole-body image analysis offers a comprehensive view of tumors and organs, paving the way for clinical validation.

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Non-small cell lung carcinoma (NSCLC) patient survival is influenced by various factors.
  • Current imaging techniques provide valuable diagnostic information but may lack comprehensive prognostic capabilities.

Purpose of the Study:

  • To explore the relationship between imaging features from CT, MRI, and PET scans and patient survival in NSCLC.
  • To highlight the potential of individualized image-based tissue characterization for a holistic assessment of disease burden.

Main Methods:

  • Analysis of imaging features derived from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans.
  • Correlation of identified image features with patient survival data in a cohort of NSCLC patients.

Related Experiment Videos

Last Updated: May 11, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

Main Results:

  • Specific imaging features extracted from CT, MRI, and PET scans demonstrate a significant association with survival outcomes in NSCLC.
  • Individualized image-based tissue characterization provides a comprehensive whole-body perspective, encompassing all tumor deposits and organs at risk.

Conclusions:

  • Image-based tissue characterization holds significant promise for personalized medicine in NSCLC.
  • The findings support the need for large-scale international studies to validate and integrate this advanced imaging technology into routine clinical practice.