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Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics

Jan C Peeken1, Lucas Etzel2, Tim Tomov3

  • 1Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany; German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), German Research Center for Environmental Health GmbH, Neuherberg, Germany; Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology
|May 23, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning algorithm automates tumor segmentation for radiomics analysis in soft tissue sarcomas (STS), offering reproducible predictions. While effective for feature extraction, its direct clinical applicability for radiotherapy planning requires further investigation.

Keywords:
Deep LearningMRIRadiologyRadiomicsRadiotherapySoft tissue sarcomaTumor Volume

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Volume of interest (VOI) segmentation is critical for radiomics and radiotherapy (RT).
  • Manual segmentation is time-consuming and prone to inter-observer variability.
  • Deep learning offers a potential solution for automated, reproducible segmentation.

Purpose of the Study:

  • To develop and validate a deep learning-based automatic segmentation (DLBAS) algorithm.
  • To predict the primary gross tumor volume of interest (VOI) for radiomics analysis in extremity soft tissue sarcomas (STS).
  • To assess the reproducibility and clinical applicability of DLBAS predictions.

Main Methods:

  • A DLBAS algorithm was trained on 157 patients and tested on 87 patients using contrast-enhanced MRI.
  • Manual tumor delineations by radiation oncologists served as ground truths (GTs).
  • A benchmark study compared DLBAS predictions against manual segmentations by residents and radiation oncologists, with clinical suitability ratings.

Main Results:

  • DLBAS achieved a median Dice Similarity Coefficient (DSC) of 0.88 against GTs and high stability for radiomics features (median ICC of 0.97).
  • Comparisons with expert delineations showed median DSCs of 0.89 (residents) and 0.82 (radiation oncologists).
  • Radiation oncologists found DLBAS predictions clinically suitable in 35% and 20% of cases.

Conclusions:

  • The DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction in extremity STS.
  • While effective for radiomics, the direct clinical applicability of DLBAS predictions for radiotherapy planning requires further evaluation and improvement.