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 2 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%

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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Comparison of plan quality and robustness using VMAT and IMRT for breast cancer

Yin,

Deng,

Mei

et al. 2024

Open Physics

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To evaluate the plan quality and robustness of volumetric modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) for breast cancer, 50 patients, including 25 patients who received radiotherapy after breast-conserving surgery (BCR) and 25 patients who received postmastectomy radiotherapy (PRT), were selected for this study. Nominal VMAT and IMRT plans were generated for each patient on Eclipse treatment planning system (version 15.6). The dosimetric metrics, dose distribution, gamma passing rate, and delivery time were compared. In addition, 12 uncertainty plans with plan isocenter uncertainty and CT density uncertainty were recalculated based on the nominal plans for each patient. The dose volume histogram (DVH) band width (DVHBW) was adopted to quantify the plan robustness of the nominal plans for the perturbed scenarios in this study. For BCR, the dosimetric metrics except planning target volume (PTV) conformal index (CI) and ipsilateral lung V 5 were not statistically different for IMRT and VMAT plans. PTV CI of VMAT plans was better than that of IMRT plans (VMAT: 0.923 ± 0.024, IMRT: 0.855 ± 0.032, p = 0.003). The ipsilateral lung V 5 of VMAT plan was higher than that of IMRT plan (VMAT: 42.4% ± 2.8%, IMRT: 40.5% ± 4.0%, p = 0.045). The VMAT plans save more than 1.20 min compared to the IMRT plans (VMAT: 0.87 min, IMRT: 2.08 min, p < 0.001). The gamma passing rates of VMAT plans were better than those of IMRT plans (3 mm/3%, VMAT: 99.7% ± 0.2%, IMRT: 99.4% ± 0.4%, p < 0.001; 2 mm/2%, VMAT: 97.2% ± 1.0%, IMRT: 96.9% ± 0.6%, p = 0.108). For PRT, the dosimetric metrics of VMAT plans, including PTV D mean, homogeneity index (HI), CI, and D max of spinal cord, were significantly better than those of IMRT plans. The VMAT plans save more than 45% time compared with IMRT plans (VMAT: 1.54 min, IMRT: 2.81 min, p < 0.001). The difference in gamma passing rates between VMAT plans and IMRT plans was not statistically significant. For the plan robustness, the DVHBW of VMAT plans and IMRT plans for BCR were 2.09% ± 0.23% and 2.98% ± 0.40%, respectively (p < 0.05). For PRT, the DVHBW of VMAT plans was significantly better than those of IMRT plans (VMAT: 3.05% ± 0.26%, IMRT: 3.57% ± 0.27%, p < 0.05). The results show that the dosimetric metrics of VMAT plans were comparable to those of IMRT plans. More importantly, the VMAT plans had excited dose distribution and fast execution efficiency. The plan robustness of VMAT plans were superior.

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Technological advancements and future perspectives in breast cancer radiation therapy

Cited by 2 publications

References 100 publications

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

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

Comparison of plan quality and robustness using VMAT and IMRT for breast cancer

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