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A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.

Valentina Brancato1, Nadia Brancati2, Giusy Esposito3,4

  • 1IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel radiomics approach to predict breast cancer (BC) heterogeneity using MRI. The method accurately identifies key biomarkers (ER, PR, HER2, Ki67), improving BC characterization and prognosis prediction.

Keywords:
Breast Cancerfeature selectionmachine learningradiomics

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Area of Science:

  • Oncology
  • Medical Imaging
  • Radiomics

Background:

  • Breast cancer (BC) exhibits heterogeneity, impacting prognosis.
  • Key biomarkers (Estrogen Receptor, Progesterone Receptor, HER2, Ki67) define BC subtypes.
  • Radiomics offers non-invasive prediction of BC heterogeneity from MRI.

Purpose of the Study:

  • To develop a novel radiomics approach for predicting BC molecular heterogeneity.
  • To build predictive models for Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67.
  • To address limitations in BC radiomics datasets and methodology.

Main Methods:

  • Utilized a two-step feature selection process on multiparametric MRI data.
  • Extracted thousands of radiomic features from DCE-MRI, ADC maps, and T2-weighted images.
  • Developed prediction models for ER, PR, HER2, and Ki67 using selected features.

Main Results:

  • Achieved high F1-scores: 84% (ER), 63% (PR), 90% (HER2), 72% (Ki67).
  • Validated models on the TCGA/TCIA Breast Cancer dataset, showing an 88% F1-score for ER+/ER- classification.
  • Demonstrated efficient characterization of BC heterogeneity based on molecular biomarkers.

Conclusions:

  • The developed radiomics approach effectively predicts key BC molecular biomarkers.
  • This method holds potential for improving BC characterization and clinical decision-making.
  • Further validation and integration into clinical practice are warranted.