2021
DOI: 10.3389/fonc.2021.725507
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Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study

Abstract: PurposeWe developed a deep learning model to achieve automatic multitarget delineation on planning CT (pCT) and synthetic CT (sCT) images generated from cone-beam CT (CBCT) images. The geometric and dosimetric impact of the model was evaluated for breast cancer adaptive radiation therapy.MethodsWe retrospectively analyzed 1,127 patients treated with radiotherapy after breast-conserving surgery from two medical institutions. The CBCT images for patient setup acquired utilizing breath-hold guided by optical surf… Show more

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Cited by 16 publications
(20 citation statements)
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“…; however, the more permissible 3%/3‐mm criteria passed in their investigation. Conversely, Dai et al 51 . reported failing plans at 3%/3 mm.…”
Section: Resultsmentioning
confidence: 97%
See 4 more Smart Citations
“…; however, the more permissible 3%/3‐mm criteria passed in their investigation. Conversely, Dai et al 51 . reported failing plans at 3%/3 mm.…”
Section: Resultsmentioning
confidence: 97%
“…When the same sCT auto‐segmentation was compared to a manually segmented ground truth dataset, the Dice score improved to 0.96 ± 0.04, suggesting that only small modifications are necessary. Dai et al 51 . trained a separate segmentation network on sCT and CT images with respective manually generated ground truth labels for the thoracic region.…”
Section: Resultsmentioning
confidence: 99%
See 3 more Smart Citations