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Related Experiment Video

Updated: Oct 12, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Prostate Cancer Aggressiveness Prediction Using CT Images.

Bruno Mendes1,2, Inês Domingues1,3, Augusto Silva4

  • 1Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal.

Life (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

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Radiomics analysis of Computed Tomography (CT) images shows promise for assessing prostate cancer (PCa) aggressiveness. This non-invasive approach could aid in evaluating the effectiveness of External Beam Radiotherapy Treatment (EBRT).

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Prostate cancer (PCa) diagnosis often relies on invasive Transrectal Ultrasound Guided Biopsy (TRUS), which has limitations like sampling errors and potential complications.
  • External Beam Radiotherapy Treatment (EBRT) is a key modality for localized, locally advanced, and metastatic prostate cancer, utilizing Computed Tomography (CT) for treatment planning.
  • There is a need for non-invasive methods to assess PCa aggressiveness and predict EBRT effectiveness, given the limitations of current diagnostic procedures.

Purpose of the Study:

  • To evaluate the utility of radiomics, a data-characterization technique using CT images, for assessing prostate cancer aggressiveness.
  • To explore the potential of radiomics as a tool to help evaluate the effectiveness of External Beam Radiotherapy Treatment (EBRT).

Main Methods:

Keywords:
classificationcomputed tomographyprostate cancerradiomic featuresrisk stratification

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  • Extraction of radiomic features from CT images using Pyradiomics and Local Image Features Extraction (LIFEx) software.
  • Application of Principal Component Analysis (PCA) to identify a radiomic signature indicative of PCa aggressiveness.

Main Results:

  • The study successfully extracted radiomic features from CT images relevant to prostate cancer.
  • Principal Component Analysis (PCA) applied to these features yielded promising results in assessing PCa aggressiveness.

Conclusions:

  • Radiomics analysis of CT images presents a promising, non-invasive approach for evaluating prostate cancer aggressiveness.
  • This methodology could potentially enhance the assessment of External Beam Radiotherapy Treatment (EBRT) effectiveness in prostate cancer management.