Definitive treatment of lung cancer with radiotherapy is challenging, as respiratory motion and anatomical changes can increase the risk of severe off-target effects during radiotherapy. Online adaptive radiotherapy (ART) is an evolving approach that enables timely modification of a treatment plan during the interfraction of radiotherapy, in response to physiologic or anatomic variations, aiming to improve the dose distribution for precise targeting and delivery in lung cancer patients. The effectiveness of online ART depends on the seamless integration of multiple components: sufficient quality of linear accelerator-integrated imaging guidance, deformable image registration, automatic recontouring, and efficient quality assurance and workflow. This review summarizes the present status of online ART for lung cancer, including key technologies, as well as the challenges and areas of active research in this field.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods*

This paper aims to propose a noninvasive radiotherapy patient positioning system based on structured light surface imaging, and evaluate its clinical feasibility. First, structured light sensors were used to obtain the panoramic point clouds during radiotherapy positioning in real time. The fusion of different point clouds and coordinate transformation were realized based on optical calibration and pose estimation, and the body surface was segmented referring to the preset region of interest (ROI). Then, the global-local registration of cross-source point cloud was achieved based on algorithms such as random sample consensus (RANSAC) and iterative closest point (ICP), to calculate 6 degrees of freedom (DoF) positioning deviation and provide guidance for the correction of couch shifts. The evaluation of the system was carried out based on a rigid adult phantom and volunteers' body, which included positioning error, correlation analysis, and receiver operating characteristic (ROC) analysis. Using Cone Beam CT (CBCT) as the gold standard, the maximum translation and rotation errors of this system were (1.5 ± 0.9) mm along Vrt direction (chest) and (0.7 ± 0.3) ° along Pitch direction (head and neck). The Pearson correlation coefficient between results of system outputs and CBCT verification distributed in an interval of [0.80, 0.84]. Results of ROC analysis showed that the translational and rotational AUC values were 0.82 and 0.85, respectively. In the 4D freedom accuracy test on the human body of volunteers, the maximum translation and rotation errors were (2.6 ± 1.1) mm (Vrt direction, chest and abdomen) and (0.8 ± 0.4)° (Rtn direction, chest and abdomen) respectively. In summary, the positioning system based on structured light body surface imaging proposed in this article can ensure positioning accuracy without surface markers and additional doses, and is feasible for clinical application.
Humans ; Patient Positioning/methods* ; Phantoms, Imaging ; Cone-Beam Computed Tomography ; Algorithms ; Radiotherapy, Image-Guided/methods* ; Radiotherapy Planning, Computer-Assisted/methods*

To address the issues of difficulty in preserving anatomical structures, low realism of generated images, and loss of high-frequency image information in medical image cross-modal translation, this paper proposes a medical image cross-modal translation method based on diffusion generative adversarial networks. First, an unsupervised translation module is used to convert magnetic resonance imaging (MRI) into pseudo-computed tomography (CT) images. Subsequently, a nonlinear frequency decomposition module is used to extract high-frequency CT images. Finally, the pseudo-CT image is input into the forward process, while the high-frequency CT image as a conditional input is used to guide the reverse process to generate the final CT image. The proposed model is evaluated on the SynthRAD2023 dataset, which is used for CT image generation for radiotherapy planning. The generated brain CT images achieve a Fréchet Inception Distance (FID) score of 33.159 7, a structure similarity index measure (SSIM) of 89.84%, a peak signal-to-noise ratio (PSNR) of 35.596 5 dB, and a mean squared error (MSE) of 17.873 9. The generated pelvic CT images yield an FID score of 33.951 6, a structural similarity index of 91.30%, a PSNR of 34.870 7 dB, and an MSE of 17.465 8. Experimental results show that the proposed model generates highly realistic CT images while preserving anatomical accuracy as much as possible. The transformed CT images can be effectively used in radiotherapy planning, further enhancing diagnostic efficiency.
Humans ; Tomography, X-Ray Computed/methods* ; Magnetic Resonance Imaging/methods* ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Brain/diagnostic imaging* ; Algorithms ; Radiotherapy Planning, Computer-Assisted ; Generative Adversarial Networks

OBJECTIVE: Automatic planning is a commonly used alternative to manual planning. This study evaluated the clinical performance of automatic plans available in commercial treatment planning systems for nasopharyngeal carcinoma (NPC) treatment by comparing automatic planning with manual planning. METHODS: A total of 14 patients with nasopharyngeal carcinoma were enrolled in the study. For each patient, three different sets of clinical goals were used to generate three hybrid automatic plans based on 3D dose distribution prediction and three automatic plans based on script, respectively, which were compared with the manual plans used in clinic. RESULTS: The dose coverage performance of the automatic planning based on 3D dose distribution prediction on the planning target volume (PTV) was comparable to that of the manual planning. Automatic planning based on 3D dose prediction achieved the level of manual planning in most organs at risk. However, automatic planning based on scripts did not perform well in the prediction of some organs at risk, especially the parotid gland. CONCLUSION The hybrid automatic plan based on 3D dose distribution prediction can reach the level of manual planning and have good robustness with the change of clinical objective.
Humans ; Nasopharyngeal Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Nasopharyngeal Carcinoma ; Male ; Female ; Middle Aged ; Adult ; Carcinoma ; Radiotherapy Dosage

OBJECTIVE: To analyze the time characteristics of the Ethos online adaptive radiotherapy (OART) process in clinical practice and provide guidance for the comprehensive optimization of each stage of adaptive radiotherapy. METHODS: The study involved 61 patients with cervical, rectal, gastric, lung, esophageal, and breast cancers who underwent Ethos OART. The mean ± standard deviation of segmental time, total time, and target volume for these patients were tracked. The time characteristics for different cancer types were evaluated, and the average time for target and organ at risk (OAR) modifications was compared with the average target volume for each cancer type. RESULTS: Cervical cancer born the longest total treatment time, while breast cancer had the shortest. For all cancer types except breast cancer, the modification time for target and OAR was the most time-consuming segment. The average time for target and OAR modifications aligned with the trend of the average target volume. CONCLUSION The total treatment time for various cancers ranges from 15 to 35 minutes, indicating room for improvement.
Humans ; Radiotherapy Planning, Computer-Assisted/methods* ; Neoplasms/radiotherapy* ; Female

OBJECTIVE: To study effective methods for reducing lung V₅, V₁₀, and mean lung dose (MLD) in the design of volumetric modulated arc therapy for central lung cancer by using different arc configurations and dose-limiting blocks designs. METHODS: Five groups of plans were designed for the enrolled patients. Group A used a full-arc field. Group B used a partial-arc field. Groups C, D, and E used full-arc fields with vertical-length, semi-ring, and triangular dose-limiting blocks added respectively. The dosimetric similarities of target areas and the dosimetric differences in lung V₅, V₁₀, V₂₀, and MLD among the groups were compared. RESULTS: Compared with group A, groups B, C, D, and E had decreased homogeneity and conformity of the target area, but significantly lower V₅ and V₁₀ of the whole lung. The MLD of groups C, D, and E was lower than that of group A. CONCLUSION Using a full-arc field combined with dose-limiting blocks can effectively reduce lung V₅, V₁₀, MLD, and monitor units (MU).
Lung Neoplasms/radiotherapy* ; Humans ; Radiotherapy, Intensity-Modulated/methods* ; Radiotherapy Planning, Computer-Assisted/methods* ; Radiotherapy Dosage ; Lung/radiation effects*

OBJECTIVE: To evaluate the feasibility and accuracy of cone-beam CT (CBCT) images for radiotherapy dose calculation in pelvic tumors. METHODS: An improved volumetric density coverage method was used to establish CT value-relative electron density (RED) curves for CBCT images. The planning CT plans were transferred to the CBCT images, and the constructed density curves were applied to calculate doses for CBCT plans while maintaining the optimization parameters unchanged. Dose calculation deviations between the two plans were analyzed. RESULTS: The mean differences in dosimetric parameters for the target volume and organs at risk (OAR) between the two plans were less than 1% and 1.5%, respectively. The target conformity index (CI), homogeneity index (HI), and gamma passing rates were highly consistent, with no statistically significant differences. CONCLUSION CBCT images corrected by this method can be used for dose calculation in pelvic tumor radiotherapy planning.
Cone-Beam Computed Tomography/methods* ; Humans ; Radiotherapy Planning, Computer-Assisted/methods* ; Radiotherapy Dosage ; Pelvic Neoplasms/diagnostic imaging*

OBJECTIVE: To investigate the impact of different iterative cone beam CT (iCBCT) scanning beam currents from a ring-mounted linac on synthetic CT image quality for lung adaptive radiotherapy under lung scanning protocol. METHODS: The CIRS lung motion phantom was configured to simulate conventional respiratory motion pattern, followed by 4D-CT simulation. After transferring the radiotherapy plan to the ring-mounted Halcyon 3.0 linac, three groups of typical iCBCT scans with different beam currents [ I _low (160 mA), I _middle (282 mA), and I _high (491 mA)] were performed and corresponding image reconstructions were completed. Synthetic CT (sCT) images were subsequently obtained based on the deformable registration algorithm. RESULTS: Compared to the corresponding CBCT images, the sCT images exhibited a significant reduction in artifacts. The fine structure of the planning CT (pCT) image was preserved for sCT images corresponding to different scanning beam currents, with Dice similarity coefficients exceeding 0.90 for all cases. CONCLUSION The image quality of sCT corresponding to different iCBCTs is comparable to that of pCT, and changes in iCBCT beam parameters have a negligible impact on sCT image quality. Taking into account both image quality and imaging dose factors associated with the beam currents, iCBCT with a lower beam current on the ring-mounted Halcyon linac offers greater clinical value in lung adaptive radiotherapy.
Cone-Beam Computed Tomography/methods* ; Phantoms, Imaging ; Humans ; Radiotherapy Planning, Computer-Assisted/methods* ; Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging*

OBJECTIVES: To explore the synthesis of high-quality CT (sCT) from cone-beam CT (CBCT) using PE-CycleGAN for adaptive radiotherapy (ART) for nasopharyngeal carcinoma. METHODS: A perception-enhanced CycleGAN model "PE-CycleGAN" was proposed, introducing dual-contrast discriminator loss, multi-perceptual generator loss, and improved U-Net structure. CBCT and CT data from 80 nasopharyngeal carcinoma patients were used as the training set, with 7 cases as the test set. By quantifying the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), as well as the dose gamma pass rate and the relative dose deviations of the target area and organs at risk (OAR) between sCT and reference CT, the image quality and dose calculation accuracy of sCT were evaluated. RESULTS: The MAE of sCT generated by PE-CycleGAN compared to the reference CT was (56.89±13.84) HU, approximately 30% lower than CBCT's (81.06±15.86) HU (P<0.001). PE-CycleGAN's PSNR and SSIM were 26.69±2.41dB and 0.92±0.02 respectively, significantly higher than CBCT's 21.54±2.37dB and 0.86±0.05 (P<0.001), indicating substantial improvements in image quality and structural similarity. In gamma analysis, under the 2 mm/2% criterion, PE-CycleGAN's sCT achieved a pass rate of (90.13±3.75)%, significantly higher than CBCT's (81.65±3.92)% (P<0.001) and CycleGAN's (87.69±3.50)% (P<0.05). Under the 3 mm/3% criterion, PE-CycleGAN's sCT pass rate of (90.13±3.75)% was also significantly superior to CBCT's (86.92±3.51)% (P<0.001) and CycleGAN's (94.58±2.23)% (P<0.01). The mean relative dose deviation of the target area and OAR between sCT and planned CT was within ±3% for all regions, except for the Lens Dmax (Gy), which had a deviation of 3.38% (P=0.09). The mean relative dose deviations for PTVnx HI, PTVnd HI, PTVnd CI, PTV1 HI, PRV_SC, PRV_BS, Parotid, Larynx, Oral, Mandible, and PRV_ON were all less than ±1% (P>0.05). CONCLUSIONS PE-CycleGAN demonstrates the ability to rapidly synthesize high-quality sCT from CBCT, offering a promising approach for CBCT-guided adaptive radiotherapy in nasopharyngeal carcinoma.
Humans ; Cone-Beam Computed Tomography/methods* ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Nasopharyngeal Carcinoma/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Radiotherapy Dosage ; Signal-To-Noise Ratio ; Radiotherapy, Intensity-Modulated

OBJECTIVES: To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. METHODS: A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. RESULTS: In terms of the dosemetric indicators, D_95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. CONCLUSIONS The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Prognosis ; Radiotherapy, Intensity-Modulated/methods* ; Carcinoma, Non-Small-Cell Lung/radiotherapy* ; Radiotherapy Dosage ; Female ; Male ; Middle Aged