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Targeted Cancer Therapies02:57

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Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction.

Nuno M Rodrigues1,2, José Guilherme de Almeida2, Ana Rodrigues2,3

  • 1LASIGE, Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.

JCO Clinical Cancer Informatics
|September 18, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence enhances prostate cancer aggressiveness prediction by integrating deep learning features with radiomic signatures. Careful assessment of deep features is crucial for improving model performance and clinical decision-making.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Artificial intelligence (AI) shows promise in improving prostate cancer detection and treatment.
  • Radiomics, traditionally viewed as a sequential process, can be integrated with AI for enhanced analysis.
  • Prostate segmentation and reconstruction models offer valuable feature information beyond traditional radiomics.

Purpose of the Study:

  • To investigate the impact of integrating deep learning features with radiomic signatures for prostate cancer aggressiveness classification.
  • To evaluate the effectiveness of different modeling decisions, including feature aggregation and dimensionality reduction, on predictive performance.
  • To determine if combining deep features from segmentation/reconstruction models with radiomic features improves classification accuracy.

Main Methods:

  • Conducted 2,244 experiments using deep learning features from 13 different models.
  • Extracted deep features from models trained on various prostate anatomic zones.
  • Employed feature aggregation and dimensionality reduction techniques, including principal component analysis (PCA).

Main Results:

  • Integrating deep features from autoencoder models trained on the full prostate gland with radiomic features significantly improved disease aggressiveness classification.
  • While some deep features enhanced prediction, others were detrimental, highlighting the need for careful selection.
  • Principal Component Analysis (PCA) and PCA + relief were identified as effective feature selection methods.

Conclusions:

  • Combining deep features from prostate reconstruction models with radiomic features can significantly boost the performance of models predicting disease aggressiveness.
  • The strategic selection of features is critical for optimizing predictive model performance.
  • While deep features offer potential benefits, their inclusion requires thorough evaluation to avoid negatively impacting predictive accuracy.