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Adjuvant breast radiotherapy could reduce the risk of local recurrence. However, the radiation dose received by the heart also increases the risk of cardiotoxicity and causes consequential heart diseases. This prospective study aimed to evaluate more precisely cardiac subvolume doses and corresponding myocardial perfusion defects according to the American Heart Association (AHA)'s 20-segment model for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) interpretation for breast cancer after radiotherapy. The 61 female patients who underwent adjuvant radiotherapy following breast cancer surgery for left breast cancer were enrolled. SPECT MPI were performed before radiotherapy for baseline study, and 12 months after for follow-up. Enrolled patients were divided into two groups, new perfusion defect (NPD) and non new perfusion defect found (non-NPD) according to myocardial perfusion scale score. CT simulation data, radiation treatment planning, and SPECT MPI images were fused and registered. The left ventricle was divided into four rings, three territories, and 20 segments according to the AHA's 20-segment model of the LV. The doses between NPD and non-NPD groups were compared by the Mann-Whitney test. The patients were divided into two groups: NPD group (n = 28) and non-NPD group (n = 33). The mean heart dose was 3.14 Gy in the NPD group and 3.08 Gy in the non-NPD group. Mean LV doses were 4.84 Gy and 4.71 Gy, respectively. The radiation dose of the NPD group was higher than the non-NPD group in the 20 segments of LV. There was significant difference in segment 3 (p = 0.03). The study indicated that the radiation doses to 20 segments of LV in NPD were higher than those in non-NPD significantly at segment 3, and higher in other segments in general. In the bull's eye plot combining radiation dose and NPD area, we found that the new cardiac perfusion decline may exist even in the low radiation dose region.Trial registration: FEMH-IRB-101085-F. Registered 01/01/2013, https://clinicaltrials.gov/ct2/show/NCT01758419?cond=NCT01758419&draw=2&rank=1 ., (© 2023. The Author(s).)

Radiotherapy treatment planning (RTP) is time-consuming and labor-intensive since medical physicists must devise treatment plans carefully to reduce damage to tissues and organs for patients. Previously, we proposed the volume-based algorithm (VBA) method, providing optimal partial arcs (OPA) angle to achieve the low-dose volume of lungs in dynamic arc radiotherapy. This study aimed to implement the VBA for esophageal cancer (EC) patients and compare the lung dose and delivery time between full arcs (FA) without using VBA and OPA angle using VBA in volumetric modulated arc therapy (VMAT) plans. We retrospectively included 30 patients diagnosed with EC. RTP of each patient was replanned to 4 VMAT plans, including FA plans without (FA-C) and with (FA + C) dose constraints of OARs and OPA plans without (OPA-C) and with (OPA + C) dose constraints of OARs. The prescribed dose was 45 Gy. The OARs included the lungs, heart, and spinal cord. The dose distribution, dose-volume histogram, monitor units (MUs), delivery time, and gamma passing rates were analyzed. The results showed that the lung V 5 and V 10 in OPA + C plans were significantly lower than in FA + C plans (p < 0.05). No significant differences were noted in planning target volume (PTV) coverage, lung V 15 , lung V 20 , mean lung dose, heart V 30 , heart V 40 , mean heart dose, and maximal spinal cord dose between FA + C and OPA + C plans. The delivery time was significantly longer in FA + C plans than in OPA + C plans (237 vs. 192 s, p < 0.05). There were no significant differences between FA + C and OPA + C plans in gamma passing rates. We successfully applied the OPA angle based on the VBA to clinical EC patients and simplified the arc angle selection in RTP. The VBA could provide a personalized OPA angle for each patient and effectively reduce lung V 5 , V 10, and delivery time in VMAT., (© 2022. The Author(s).)

This study aims to develop a volume-based algorithm (VBA) that can rapidly optimize rotating gantry arc angles and predict the lung V 5 preceding the treatment planning. This phantom study was performed in the dynamic arc therapy planning systems for an esophageal cancer model. The angle of rotation of the gantry around the isocenter as defined as arc angle (θ A ), ranging from 360° to 80° with an interval of 20°, resulting in 15 different θ A of treatment plans. The corresponding predicted lung V 5 was calculated by the VBA, the mean lung dose, lung V 5 , lung V 20 , mean heart dose, heart V 30 , the spinal cord maximum dose and conformity index were assessed from dose-volume histogram in the treatment plan. Correlations between the predicted lung V 5 and the dosimetric indices were evaluated using Pearson's correlation coefficient. The results showed that the predicted lung V 5 and the lung V 5 in the treatment plan were positively correlated (r = 0.996, p < 0.001). As the θ A decreased, lung V 5 , lung V 20 , and the mean lung dose decreased while the mean heart dose, V 30 and the spinal cord maximum dose increased. The V 20 and the mean lung dose also showed high correlations with the predicted lung V 5 (r = 0.974, 0.999, p < 0.001). This study successfully developed an efficient VBA to rapidly calculate the θ A to predict the lung V 5 and reduce the lung dose, with potentials to improve the current clinical practice of dynamic arc radiotherapy.

Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct due to X-ray scattering, noise, and artefacts. We proposed a deep learning model, "Cycle-Deblur GAN", combined with CycleGAN and Deblur-GAN models to improve the image quality of chest CBCT images. The 8706 CBCT and FBCT image pairs were used for training, and 1150 image pairs were used for testing in deep learning. The generated CBCT images from the Cycle-Deblur GAN model demonstrated closer CT values to FBCT in the lung, breast, mediastinum, and sternum compared to the CycleGAN and RED-CNN models. The quantitative evaluations of MAE, PSNR, and SSIM for CBCT generated from the Cycle-Deblur GAN model demonstrated better results than the CycleGAN and RED-CNN models. The Cycle-Deblur GAN model improved image quality and CT-value accuracy and preserved structural details for chest CBCT images.