Technological advancements and future perspectives in breast cancer radiation therapy

Fozza, Alessandra; Rose, F. De; Santis, Maria Carmen De; Meattini, Icro; Meduri, Bruno; D’Angelo, Elisa; Dei, Damiano; Figlia, Vanessa; Rocca, Eliana La; Fregatti, Piero; Satragno, Camilla; Belgioia, Liliana; Giaj-Levra, Niccolò

doi:10.1080/14737140.2023.2195167

Cited by 4 publications

(1 citation statement)

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“…Auto-segmentation is particularly important for breast cancer patients compared to other sites because of the complex anatomy and variability in breast shape and size [12][13][14]. Precise segmentation of volumes helps ensure consistent treatment planning across different patients, leading to more effective and personalized breast cancer radiotherapy.…”

Section: Introductionmentioning

confidence: 99%

Auto-segmentation of thoracic organs in CT scans of breast cancer patients using a 3D U-net cascaded into 2D patchGANs

Colbert,

Ramachandran

2023

Biomed. Phys. Eng. Express

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The quality of organ volume delineation significantly influences the efficacy of radiotherapy treatment for breast cancer patients. This study developed and tested a novel method for auto-segmentation of the breasts, lungs, and heart. The proposed pipeline leveraged a multi-class 3D U-Net with a pre-trained ResNet(2+1)D-18 encoder branch, cascaded with a 2D PatchGAN mask correction model for each class. This approach required a single 3D model, providing a relatively efficient solution. The models were trained and evaluated on 70 thoracic DICOM datasets belonging to breast cancer patients. The evaluation demonstrated state-of-the-art segmentation performance, with mean Dice similarity coefficient values ranging between 0.89 and 0.98; Hausdorff distance values ranging between 2.25 and 8.68 mm; and mean surface distance values ranging between 0.62 and 2.79 mm. These results underscore the pipeline's potential to enhance breast cancer diagnosis and treatment strategies, with possible applications in other medical sectors utilizing auto-segmentation.

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Section: Introductionmentioning

confidence: 99%