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Application of Mid-Pancreatectomy with End-to-End Anastomosis in Pancreatic Benign Tumors
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Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning.

Wei Zhao1, Liyue Shen1, Bin Han1

  • 1Department of Radiation Oncology, Stanford University, Stanford, California.

International Journal of Radiation Oncology, Biology, Physics
|June 16, 2019
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Summary
This summary is machine-generated.

Deep learning accurately localizes pancreatic tumors on X-ray images for image-guided radiation therapy. This markerless approach enhances treatment precision for challenging pancreatic cancer cases.

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Area of Science:

  • Medical Imaging
  • Radiation Oncology
  • Artificial Intelligence

Background:

  • Pancreatic tumors present unique challenges for radiation delivery due to their anatomical location and characteristics.
  • Accurate on-board target verification is crucial for effective image-guided radiation therapy (IGRT).

Purpose of the Study:

  • To develop and evaluate a deep learning model for markerless localization of pancreatic tumors on kV X-ray images.
  • To assess the accuracy of deep neural networks in identifying the planning target volume (PTV) for IGRT.

Main Methods:

  • A deep neural network was trained using digitally reconstructed radiographs and monoscopic X-ray projection images.
  • Simulated inter- and intrafractional variations were introduced to planning CT images for robust training.
  • Model accuracy was evaluated using mean absolute differences (MADs) and Lin's concordance correlation coefficient on patient data.

Main Results:

  • The deep learning model achieved mean absolute differences (MADs) of less than 2.60 mm for target localization in multiple directions.
  • Comparison studies with and without fiducials showed MADs below 2.49 mm.
  • Lin's concordance correlation coefficients exceeded 93%, indicating high accuracy in predicted target positions.

Conclusions:

  • Deep learning enables accurate, markerless localization of pancreatic tumors for IGRT.
  • The proposed approach demonstrates the feasibility and effectiveness of AI in improving precision for pancreatic cancer radiation therapy.