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Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and

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Deep learning segmentation and radiomics analysis of lung tumors after thermal ablation can predict patient survival and recurrence. Post-ablation imaging data improved prediction accuracy for these outcomes.

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Image-guided thermal ablation (IGTA) is a minimally invasive treatment for lung tumors.
  • Accurate prediction of survival and recurrence post-ablation is crucial for patient management.
  • Deep learning offers potential for automated tumor segmentation and feature extraction.

Purpose of the Study:

  • To predict survival and tumor recurrence after IGTA for lung tumors.
  • To evaluate a deep learning approach for tumor segmentation and radiomic feature extraction.
  • To assess the performance of a radiomics-based model for outcome prediction.

Main Methods:

  • Retrospective analysis of 113 patients undergoing IGTA for lung tumors.
  • U-Net and UNETR (U-shaped encoder-decoder transformer) models for automated lung and tumor segmentation on CT scans.
  • Radiomic features extracted from segmented tumors.
  • Support vector machine (SVM) model trained for survival and recurrence prediction.
  • Model performance evaluated using c-index and time-dependent AUC.

Main Results:

  • UNETR achieved a Dice score of 0.75 for tumor segmentation.
  • The radiomics model using post-ablation scans predicted survival with a c-index of 0.71 and AUC of 0.75.
  • Pre-ablation scans yielded lower prediction metrics (c-index 0.56, AUC 0.56) for survival.
  • For recurrence prediction, post-ablation scans achieved a c-index of 0.65 and AUC of 0.72.
  • Pre-ablation scans showed reduced performance for recurrence prediction (c-index 0.54, AUC 0.54).

Conclusions:

  • Automatic segmentation using transformer-based U-NET enables radiomic feature analysis.
  • Radiomic features from post-ablation CT scans can predict survival and recurrence following IGTA.
  • This AI-driven approach shows promise for improving prognostic accuracy in lung cancer treatment.