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Related Concept Videos

Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
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Related Experiment Video

Updated: Jun 23, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Radiomics-based machine learning models for predicting genomic alterations in metastatic prostate cancer using PSMA

Anna Scavuzzo1, Giovanni Pasini2,3,4, Osvaldo G Perez5

  • 1Department of Urology, Instituto Nacional de Cancerologia, Mexico City, Mexico. annaurologia80@comunidad.unam.mx.

EJNMMI Reports
|December 30, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models combining radiomics and clinical data from PSMA PET imaging can predict key genomic mutations in metastatic prostate cancer (mPCa). This non-invasive approach aids in guiding precision oncology and treatment stratification for mPCa patients.

Keywords:
Genomic predictionMachine learningPSMA PET/cTPrecision oncologyProstate cancerRadiogenomics

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Area of Science:

  • Oncology
  • Radiology
  • Genomics
  • Machine Learning

Background:

  • Genomic characterization is crucial for precision oncology in metastatic prostate cancer (mPCa).
  • Non-invasive methods to predict genomic alterations are needed for mPCa patients.

Purpose of the Study:

  • To assess the feasibility of using machine learning (ML) with radiomics and clinical data from PSMA PET imaging.
  • To non-invasively predict key genomic mutations in mPCa patients.

Main Methods:

  • Retrospective analysis of 14 mPCa patients with [18F]PSMA-1007 PET/CT imaging.
  • Extraction of radiomics features from segmented prostate and metastatic lesions.
  • Application of six ML algorithms with feature selection and cross-validation to predict genomic alterations (TP53, TMPRSS2, PTEN, OTHER).

Main Results:

  • Clinical-radiomics ML models demonstrated high predictive accuracy for common mutations.
  • Achieved AUCs of 91.11% for TP53, 84.44% for TMPRSS2, 80.00% for PTEN, and 77.78% for OTHER mutations.
  • Selected features showed consistent stability across model runs.

Conclusions:

  • ML models integrating radiomics and clinical data from PSMA PET imaging show promise for predicting actionable genomic alterations in mPCa.
  • This radiogenomics approach can serve as a complementary non-invasive tool for molecular profiling and treatment decisions.
  • Further research is warranted to validate and implement these models in clinical practice.