Development and interinstitutional validation of an automatic vertebral‐body misalignment error detector for cone‐beam CT‐guided radiotherapy

Autor: Dishane C, Luximon, Timothy, Ritter, Emma, Fields, John, Neylon, Rachel, Petragallo, Yasin, Abdulkadir, John, Charters, Daniel A, Low, James M, Lamb
Rok vydání: 2022
Předmět:
Zdroj: Medical Physics. 49:6410-6423
ISSN: 2473-4209
0094-2405
Popis: In cone-beam computed tomography (CBCT)-guided radiotherapy, off-by-one vertebral-body misalignments are rare but serious errors that lead to wrong-site treatments.An automatic error detection algorithm was developed that uses a three-branch convolutional neural network error detection model (EDM) to detect off-by-one vertebral-body misalignments using planning computed tomography (CT) images and setup CBCT images.Algorithm training and test data consisted of planning CTs and CBCTs from 480 patients undergoing radiotherapy treatment in the thoracic and abdominal regions at two radiotherapy clinics. The clinically applied registration was used to derive true-negative (no error) data. The setup and planning images were then misaligned by one vertebral-body in both the superior and inferior directions, simulating the most likely misalignment scenarios. For each of the aligned and misaligned 3D image pairs, 2D slice pairs were automatically extracted in each anatomical plane about a point within the vertebral column. The three slice pairs obtained were then inputted to the EDM that returned a probability of vertebral misalignment. One model (EDMWhen applied to the combined test set, EDMThe proposed algorithm demonstrated accuracy in identifying off-by-one vertebral-body misalignments in CBCT-guided radiotherapy that was sufficiently high to allow for practical implementation. It was found that fine-tuning the model on a multi-facility dataset can further enhance the generalizability of the algorithm.
Databáze: OpenAIRE
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