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

Objective:To address large-scale nonlinear programming challenges in optimizing prognosis-guided intensity-modulated radiation therapy (IMRT) plans, to propose gradient-enhanced random contrastive interaction particle swarm optimization (GradRCIPSO). This gradient-enhanced swarm intelligence algorithm aims to enable global optimization of prognostic treatment plans in clinically efficient scenarios.Methods:The core concept of GradRCIPSO lied in achieving rapid global convergence by allowing particles to learn both swarm interaction and gradient information. Specifically, the interaction information was obtained from elite individuals in the swarm, enabling the particles to efficiently search the entire solution space, whereas the gradient information represents the direction of the steepest descent, enabling the particles to quickly explore the current neighborhood. To assess the effectiveness of the methodology, the IMRT plans for 10 cases of non-small cell lung cancer (NSCLC) were selected in this study. They were compared with the GradRCIPSO-generated prognosis-guided IMRT plans. Moreover, the interior-point method, sequential quadratic programming, active set, gradient descent method, and random contrastive interaction particle swarm optimization (RCIPSO) were employed as optimization engines and compared with GradRCIPSO in terms of optimization efficiency and accuracy.Results:GradRCIPSO successfully generated clinically viable prognosis-guided IMRT plans with comparable dosimetric statistics to original plans, while significantly reducing predicted total radiotherapy risk from 1.22(0.84, 1.51) to 0.93(0.80, 1.29) ( z=2.81, P<0.01). It demonstrated superior accuracy over the above four gradient-based method ( z=2.80-2.81, P<0.01) and achieved threefold acceleration versus RCIPSO while maintaining equivalent solution quality( P>0.05). Conclusions:The proposed GradRCIPSO demonstrates high feasibility and performance in optimizing prognosis-guided IMRT plans, laying the technical foundation for the broad clinical application of prognosis-guided IMRT plans for lung cancer.

Objective To compare the performance of two different deep learning models,VTcGAN and Pix2pix,in generating synthetic computed tomography(sCT)from magnetic resonance imaging(MRI)of nasopharyngeal carcinoma(NPC),and to evaluate their accuracies in treatment planning dose calculations.Methods MRI and CT images as well as treatment planning data of 115 NPC patients were retrospectively selected,and paired dataset was obtained through rigid image registration,with 105 cases as the training set,and the remaining 10 cases as the test set.Two kinds of models,namely Pix2pix model based on conventional convolutional neural network and the improved VTcGAN model based on Transformer network,were constructed with the consistent structure except for the bottleneck network in the generator.The generated sCT images(sCTPix2pix and sCTVTcGAN)were assessed in terms of image quality,intensity value and dosimetric differences.Results For the cases in test set,the mean error,mean absolute error,peak signal-to-noise ratio,and structural similarity index between the ground truth CT images and the sCTPix2pix were(-0.86±12.42)HU,(40.77±3.06)HU,(33.45±0.62)dB,and 0.928±0.013,respectively;and those between the ground truth CT images and the sCTVTcGAN were(-1.10±8.56)HU,(37.40±2.08)HU,(34.33±0.45)dB,and 0.936±0.009,respectively.For the criterion of 1 mm/1%,the averaged gamma passing rates of sCTPix2pix and sCTVTcGAN were(96.62±1.08)%and(96.88±0.99)%at a dose threshold of 10%,(94.31±1.03)%and(94.72±0.91)%at a dose threshold of 50%,(84.62±1.74)%and(86.06±1.41)%at a dose threshold of 80%,respectively.Conclusion The proposed VTcGAN model is superior to the traditional Pix2pix model in terms of accuracy in generating sCT from MRI of NPC,and it can fulfill the requirements for dose calculation in the MRI-Only workflow.

Objective:To address large-scale nonlinear programming challenges in optimizing prognosis-guided intensity-modulated radiation therapy (IMRT) plans, to propose gradient-enhanced random contrastive interaction particle swarm optimization (GradRCIPSO). This gradient-enhanced swarm intelligence algorithm aims to enable global optimization of prognostic treatment plans in clinically efficient scenarios.Methods:The core concept of GradRCIPSO lied in achieving rapid global convergence by allowing particles to learn both swarm interaction and gradient information. Specifically, the interaction information was obtained from elite individuals in the swarm, enabling the particles to efficiently search the entire solution space, whereas the gradient information represents the direction of the steepest descent, enabling the particles to quickly explore the current neighborhood. To assess the effectiveness of the methodology, the IMRT plans for 10 cases of non-small cell lung cancer (NSCLC) were selected in this study. They were compared with the GradRCIPSO-generated prognosis-guided IMRT plans. Moreover, the interior-point method, sequential quadratic programming, active set, gradient descent method, and random contrastive interaction particle swarm optimization (RCIPSO) were employed as optimization engines and compared with GradRCIPSO in terms of optimization efficiency and accuracy.Results:GradRCIPSO successfully generated clinically viable prognosis-guided IMRT plans with comparable dosimetric statistics to original plans, while significantly reducing predicted total radiotherapy risk from 1.22(0.84, 1.51) to 0.93(0.80, 1.29) ( z=2.81, P<0.01). It demonstrated superior accuracy over the above four gradient-based method ( z=2.80-2.81, P<0.01) and achieved threefold acceleration versus RCIPSO while maintaining equivalent solution quality( P>0.05). Conclusions:The proposed GradRCIPSO demonstrates high feasibility and performance in optimizing prognosis-guided IMRT plans, laying the technical foundation for the broad clinical application of prognosis-guided IMRT plans for lung cancer.

Objective To compare the performance of two different deep learning models,VTcGAN and Pix2pix,in generating synthetic computed tomography(sCT)from magnetic resonance imaging(MRI)of nasopharyngeal carcinoma(NPC),and to evaluate their accuracies in treatment planning dose calculations.Methods MRI and CT images as well as treatment planning data of 115 NPC patients were retrospectively selected,and paired dataset was obtained through rigid image registration,with 105 cases as the training set,and the remaining 10 cases as the test set.Two kinds of models,namely Pix2pix model based on conventional convolutional neural network and the improved VTcGAN model based on Transformer network,were constructed with the consistent structure except for the bottleneck network in the generator.The generated sCT images(sCTPix2pix and sCTVTcGAN)were assessed in terms of image quality,intensity value and dosimetric differences.Results For the cases in test set,the mean error,mean absolute error,peak signal-to-noise ratio,and structural similarity index between the ground truth CT images and the sCTPix2pix were(-0.86±12.42)HU,(40.77±3.06)HU,(33.45±0.62)dB,and 0.928±0.013,respectively;and those between the ground truth CT images and the sCTVTcGAN were(-1.10±8.56)HU,(37.40±2.08)HU,(34.33±0.45)dB,and 0.936±0.009,respectively.For the criterion of 1 mm/1%,the averaged gamma passing rates of sCTPix2pix and sCTVTcGAN were(96.62±1.08)%and(96.88±0.99)%at a dose threshold of 10%,(94.31±1.03)%and(94.72±0.91)%at a dose threshold of 50%,(84.62±1.74)%and(86.06±1.41)%at a dose threshold of 80%,respectively.Conclusion The proposed VTcGAN model is superior to the traditional Pix2pix model in terms of accuracy in generating sCT from MRI of NPC,and it can fulfill the requirements for dose calculation in the MRI-Only workflow.

Objective:To explore the feasibility of applying ArcherQA to independent dose verification of MR-guided online adaptive radiotherapy (ART) plans performed on Elekta Unity 1.5 Tesla (T) magnetic resonance-linear accelerator (MR-Linac).Methods:The dose calculation accuracy of ArcherQA under a specific magnetic field was validated using a homogeneous water phantom. A total of 32 patients who received MR-guided online ART on Elekta Unity were randomly selected by lottery, with 32 offline plans and 177 online plans for five treatment sites (brain, mediastinum, liver, kidney, and vertebral body) enrolled. Finally, the γ pass rates (threshold: 10%; criteria: 3 mm/3% and 2 mm/2%) were compared among the result upon independent dose verification of ArcherQA, measurements of ArcCheck, and calculations using the Monaco treatment planning system (TPS) to quantitatively evaluate the accuracy and efficiency of ArcherQA in independent dose verification of online plans on Elekta Unity.Results:ArcherQA was proven accurate in calculating the dose distribution of therapeutic photon beams under the specific magnetic field. With the 3 mm/3% criterion, the γ pass rates of verification result exceeded 99% in all square fields of a water phantom. Under the stricter 2 mm/2% criterion, the γ pass rates also surpassed 95% in all square fields except 20 cm × 20 cm field. Regarding the verification of treatment plans, the ArcherQA result were found to be highly consistent with those measured or calculated using ArcCheck and Monaco TPS, with the average γ pass rates exceeding 99% under the 3 mm/3% criterion and above 97% under the 2 mm/2% criterion. ArcherQA was acceptably efficient for independent dose verification of online plans, with 50 to 150 s, (108 s on average) required to complete the independent dose verification of 177 online plans.Conclusions:ArcherQA allows for accurately and efficiently calculating the dose distribution of therapeutic photon beams under a specific magnetic field, establishing it as an effective supplementary tool for independent dose calculation of MR-guided offline and online ART plans, thereby ensuring the safety of patient treatment plans.

Objective:To develop a prediction model for cell survival under radiation of mixed carbon ion beam based on DNA radiation damage simulation, and to assess the impacts of secondary particles on the cell survival prediction for regions beyond the Bragg peak.Methods:First, the Monte Carlo Damage Simulation (MCDS) code was employed to construct a database of DNA double-strand break (DSB) damage induced by carbon ions and their primary secondary particles for Chinese hamster ovary (CHO) cells. Subsequently, models for cell survival under irradiation of single type of particles were established through fitting and were validated based on the DSB damage database and the Particle Irradiation Data Ensemble (PIDE) experimental database of radiation biology for cells in vitro. Then, the TOPAS Monte Carlo code was used to simulate the depth-dose and energy spectrum distributions of 290 MeV/u clinical carbon ion beam. A dose-weighting method based on a precomputed DSB damage database for monoenergetic particles was proposed, and the impacts of secondary particles on cell survival prediction beyond the Bragg peak were assessed. Results:The model established in this study accurately predicted the survival rates of CHO cells under different irradiation conditions. Concurrently, the dose-weighting method employed accurately characterized the radiation damage properties of mixed beams of carbon ions and their secondary particles. The root mean square errors (RMSE) of parameter α between the experimental values and model-derived predictions after irradiation using the H +, He 2+, C 6+, and Ne 10+ beams were 0.139 2, 0.203 9, 0.192 0, and 0.516 9 Gy -1, respectively, while the RMSEs of parameter β were 0.020 5, 0.059 8, 0.040 5, and 0.060 5 Gy -2, respectively. The discrepancies between model-derived predictions and experimentally measured values of the survival rates of CHO cells at and beyond the Bragg peak after irradiation using 290 MeV/u carbon ion beam were 0.3%±0.24% and 2.3%±0.24%, respectively. Conclusions:A prediction model for cell survival under irradiation of carbon ion beam based on DNA radiation damage simulation is developed in this study. By further considering the dose distributions of various secondary particles, the model can more accurately predict cell survival rates beyond the Bragg peak. This study is expected to provide a reference for accurately assessing the equivalent biological dose beyond the Bragg peak in carbon ion clinical radiotherapy.

Objective To evaluate the efficacy of staged carotid artery stenting and coronary artery bypass grafting in the treatment of coronary heart disease complicated with carotid stenosis. Methods The clinical data of patients with coronary heart disease and carotid stenosis treated in Fuwai Hospital from November 2019 to September 2021 were retrospectively analyzed. All patients underwent staged carotid artery stenting and coronary artery bypass grafting. The incidence and risk factors of severe complications such as myocardial infarction, cerebral infarction and death during the perioperative period and follow-up were analyzed. Results A total of 58 patients were enrolled, including 47 males and 11 females with an average age of 52-77 (64.2±5.6) years. No complications occurred before coronary artery bypass grafting. There was 1 myocardial infarction, 1 cerebral infarction and 1 death after the coronary artery bypass grafting. The early complication rate was 5.2%. During the follow-up of 18.3 months, 1 cerebral infarction and 2 deaths occurred, and the overall complication rate was 10.3%. According to Kaplan-Meier survival curve analysis, patients with symptomatic carotid stenosis (log-rank, P=0.037) and placement of close-cell (log-rank, P=0.030) had a higher risk of postoperative ischemic cerebrovascular event, and patients with previous cerebral infarction had a higher risk of postoperative severe complications (log-rank, P=0.044). Conclusion Staged carotid artery stenting and coronary artery bypass grafting is safe and feasible for the treatment of coronary heart disease complicated with carotid stenosis.

Objective:To develop a prediction model for cell survival under radiation of mixed carbon ion beam based on DNA radiation damage simulation, and to assess the impacts of secondary particles on the cell survival prediction for regions beyond the Bragg peak.Methods:First, the Monte Carlo Damage Simulation (MCDS) code was employed to construct a database of DNA double-strand break (DSB) damage induced by carbon ions and their primary secondary particles for Chinese hamster ovary (CHO) cells. Subsequently, models for cell survival under irradiation of single type of particles were established through fitting and were validated based on the DSB damage database and the Particle Irradiation Data Ensemble (PIDE) experimental database of radiation biology for cells in vitro. Then, the TOPAS Monte Carlo code was used to simulate the depth-dose and energy spectrum distributions of 290 MeV/u clinical carbon ion beam. A dose-weighting method based on a precomputed DSB damage database for monoenergetic particles was proposed, and the impacts of secondary particles on cell survival prediction beyond the Bragg peak were assessed. Results:The model established in this study accurately predicted the survival rates of CHO cells under different irradiation conditions. Concurrently, the dose-weighting method employed accurately characterized the radiation damage properties of mixed beams of carbon ions and their secondary particles. The root mean square errors (RMSE) of parameter α between the experimental values and model-derived predictions after irradiation using the H +, He 2+, C 6+, and Ne 10+ beams were 0.139 2, 0.203 9, 0.192 0, and 0.516 9 Gy -1, respectively, while the RMSEs of parameter β were 0.020 5, 0.059 8, 0.040 5, and 0.060 5 Gy -2, respectively. The discrepancies between model-derived predictions and experimentally measured values of the survival rates of CHO cells at and beyond the Bragg peak after irradiation using 290 MeV/u carbon ion beam were 0.3%±0.24% and 2.3%±0.24%, respectively. Conclusions:A prediction model for cell survival under irradiation of carbon ion beam based on DNA radiation damage simulation is developed in this study. By further considering the dose distributions of various secondary particles, the model can more accurately predict cell survival rates beyond the Bragg peak. This study is expected to provide a reference for accurately assessing the equivalent biological dose beyond the Bragg peak in carbon ion clinical radiotherapy.

Objective:To investigate the role of three-dimensional dose distribution-based deep learning model in predicting distant metastasis of head and neck cancer.Methods:Radiotherapy and clinical follow-up data of 237 patients with head and neck cancer undergoing intensity-modulated radiotherapy (IMRT) from 4 different institutions were collected. Among them, 131 patients from HGJ and CHUS institutions were used as the training set, 65 patients from CHUM institution as the validation set, and 41 patients from HMR institution as the test set. Three-dimensional dose distribution and GTV contours of 131 patients in the training set were input into the DM-DOSE model for training and then validated with validation set data. Finally, the independent test set data were used for evaluation. The evaluation content included the area under receiver operating characteristic curve (AUC), balanced accuracy, sensitivity, specificity, concordance index and Kaplan-Meier survival curve analysis.Results:In terms of prognostic prediction of distant metastasis of head and neck cancer, the DM-DOSE model based on three-dimensional dose distribution and GTV contours achieved the optimal prognostic prediction performance, with an AUC of 0.924, and could significantly distinguish patients with high and low risk of distant metastasis (log-rank test, P<0.001). Conclusion:Three-dimensional dose distribution has good predictive value for distant metastasis in head and neck cancer patients treated with IMRT, and the constructed prediction model can effectively predict distant metastasis.