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 investigate whether the dosimetric effects of stereotactic body radiotherapy(SBRT)in hepatic carcinoma conform to the linear-quadratic(LQ)model.Methods Human hepatic carcinoma cell lines HepG2 and Hep3B cultured in vitro were selected and subjected to biological equivalent dose(BED)irradiation(6,8,10,12,14 Gy).The fractionation regimens included single-fraction irradiation(simulated SBRT)and 3-or 5-fraction irradiation(simulated IMRT).The surviving fraction after irradiation reflected the damage effects of different fractionation regimens;the effects of different fractionation regimens on cell proliferation were analyzed through survival curves;and cell scratch experiment after irradiation was used to observe the cell invasive and migration abilities after exposure to different fractionation regimens.Results Significant separation effect was observed in the in vitro validation of the LQ model in SBRT for hepatic carcinoma.For HepG2 cells and Hep3B cells,when BED<12 Gy(α/β=10 Gy),fractionation regimens did not show significant differences in terms of damage effects,proliferative ability,and invasive ability,indicating SBRT conformed to the LQ model;when BED≥12 Gy,single-fraction had more obvious damage effects as compared with multiple-fraction,indicating that the damage effect in SBRT was more significant than that in IMRT.Conclusion The LQ model applies to SBRT for hepatic carcinoma in a certain dose range,beyond which the damage effect is higher than the predicted results of the LQ model.

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.

Objective:To investigate the clinical characteristics and prognosis of cryptococcal meningitis patients with anti-granulocyte-macrophage colony-stimulating factor (GM-CSF) autoantibodies.Methods:A total of 216 non-acquired immunodeficiency syndrome (AIDS) related cryptococcal meningitis cases with positive cultures of Cryptococcus, hospitalized at Huashan Hospital, Fudan University during January 2014 and December 2021, were retrospectively included. The serum anti-GM-CSF autoantibodies were detected by enzyme linked immunosorbent assay, and the clinical characteristics and prognosis were compared between patients with and without anti-GM-CSF autoantibodies. Statistical comparisons were mainly performed using the chi-square test or Fisher′s exact test. Cox proportional-hazards model was used to analyze the risk factors associated with prognosis. Results:Among 216 enrolled patients, 23 patients were positive of anti-GM-CSF autoantibodies, with a positive rate of 10.6%. Among 23 patients, seven cases were infected with Cryptococcus gattii, and 16 cases were infected with Cryptococcus neoformans. In the group with positive anti-GM-CSF autoantibodies, 30.4%(7/23) of the patients were infected with Cryptococcus gattii, which was higher than that of 1.6%(3/193) in the group with negative anti-GM-CSF autoantibodies, and the difference was statistically significant ( χ2=38.82, P<0.001). In the group with positive anti-GM-CSF autoantibodies, 30.0% (6/20) had mass lesions with a diameter greater than three centimeters in the lungs, and the one-year all-cause mortality rate was 50.0% (10/20), which were both higher than those of 3.4%(5/145) and 16.1% (29/180) in the negative group, respectively. The differences were both statistically significant (both Fisher′s exact test, P<0.01). Age≥60 years (hazard ratio ( HR)=4.146, P=0.002), predisposing factors ( HR=3.160, P=0.021), epilepsy ( HR=6.129, P=0.002), positive anti-GM-CSF autoantibodies ( HR=2.675, P=0.034), white blood cell count of cerebrospinal fluid (CSF)<100 ×10 6/L ( HR=2.736, P=0.039), the titers of cryptococcal capsular polysaccharide antigen of CSF≥1∶1 280 ( HR=4.361, P=0.009) were independent risk factors for one-year all-cause mortality in patients with cryptococcal meningitis. Conclusions:In non-AIDS related cryptococcal meningitis patients, the positive rate of serum anti-GM-CSF autoantibodies is as high as 10.6%. Patients with anti-GM-CSF autoantibodies could be infected with both Cryptococcus neoformans and Cryptococcus gattii, and they have higher proportion of lung mass lesions than patients with negative anti-GM-CSF autoantibodies. The one-year survival rate decreases significantly in patients with anti-GM-CSF autoantibodies, which is an independent risk factor for the prognosis of cryptococcal meningitis.

In order to suppress the geometrical artifacts caused by random jitter in ray source scanning, and to achieve flexible ray source scanning trajectory and meet the requirements of task-driven scanning imaging, a method of free trajectory cone-beam computed tomography (CBCT) reconstruction is proposed in this paper. This method proposed a geometric calibration method of two-dimensional plane. Based on this method, the geometric calibration phantom and the imaging object could be simultaneously imaged. Then, the geometric parameters could be obtained by online calibration method, and then combined with the geometric parameters, the alternating direction multiplier method (ADMM) was used for image iterative reconstruction. Experimental results showed that this method obtained high quality reconstruction image with high contrast and clear feature edge. The root mean square errors (RMSE) of the simulation results were rather small, and the structural similarity (SSIM) values were all above 0.99. The experimental results showed that it had lower image information entropy (IE) and higher contrast noise ratio (CNR). This method provides some practical value for CBCT to realize trajectory freedom and obtain high quality reconstructed image.
Algorithms ; Calibration ; Cone-Beam Computed Tomography ; Image Processing, Computer-Assisted ; Phantoms, Imaging

Objective:To establish an automatic segmentation network based on different receptive fields for gross target volume (GTV) and organs at risk in patients with nasopharyngeal carcinoma.Methods:Radiotherapy data of 100 cases of nasopharyngeal carcinoma including CT images and GTV and organs at risk delineated by the physicians were collected. Ninety plans were randomly selected as the training dataset, and the other 10 plans as the validation dataset. Firstly, the images were subject to three data augmentation methods including center cropping, vertical flipping and rotation (-30°to 30°), and then input into MA_net networks proposed in this study for training. The model performance of networks was assessed by the number of network parameters (NP), floating-point number (FPN), the running memory (RM) and Dice index (DI), and eventually compared with DeeplabV3+ , PSP_net, UNet+ + and U_Net networks.Results:When the input image was in the size of 240×240, MA_net had a NP of 23.20%, 20.10%, 25.55% and 27.11% of these 4 networks, 50.02%, 19.86%, 6.37% and 13.44% for the FPN, 40.63%, 23.60%, 11.58% and 14.99% for the RM, respectively. For the DI of GTV, MA_net was 1.16%, 2.28%, 1.27% and 3.59% higher than these 4 networks. For the average DI of GTV and OAR, MA_net was 0.16%, 1.37%, 0.30% and 0.97% higher than these 4 networks.Conclusion:Compared with those four networks, the proposed MA_net network has slightly higher Dice index with fewer parameters, lower FPN and smaller RM.

Objective:To establish a correlation model between MRI and CT images to generate synthetic-CT (sCT) of head and neck cancer during MRI-guided radiotherapy by using generative adversarial networks (GAN).Methods:Images and IMRT plans of 45 patients with nasopharyngeal carcinoma were collected before treatment. Firstly, the MRI (T1) and CT images were preprocessed, including rigid registration, clipping, background removal and data enhancement, etc. Secondly, the cases were trained by GAN, of which 30 cases were randomly selected and put into the network as training set images for modeling and learning, and the other 15 cases were used for testing. The image quality of predicted sCT and real CT were statistically compared, and the dose distribution recalculated upon predicted sCT was statistically compared with that of real planned dose distribution.Results:The mean absolute error of the predicted sCT of the testing set was (79.15±11.37) HU, and the SSIM value was 0.83±0.03. The MAE values of dose distribution difference at different regional levels were less than 1% compared to the prescription dose. The gamma passing rate of the sCT dose distribution was higher than 92% and 98% under the 2mm/2% and 3mm/3% criteria.Conclusions:We have successfully proposed and realized the generation of sCT for head and neck cancer using GAN, which lays a foundation for the implementation of MRI-guided radiotherapy. The comparison of image quality and dosimetry shows the feasibility and accuracy of this method.

Objective:To compare the accuracy and generalized robustness of three predictive models of knowledge-based treatment strategies for radiotherapy for optimized model selection.Methods:The clinical radiotherapy plans of 45 prostate cancer (PC) cases and 25 nasopharyngeal cancer (NPC) cases were collected, and analyzed using three models (Z, L and S model), proposed by Zhu et al, Appenzoller et al and Shiraishi et al, respectively, to predict the dose-volume histogram (DVH) of bladder and rectum on PC cases and that of left and right parotid on NPC cases. The prediction error was measured by the difference of area under the predicted DVH and the clinical DVH curves (|V (pre_DVH)-V (clin_DVH)|), where a smaller prediction error implies a greater prediction accuracy. The accuracies of these three models were compared on the single organ at risk (OAR), and the generalized robustness of models was evaluated and compared by calculating the standard deviation of the prediction accuracy on different OAR. Results:For bladder and rectum, the prediction error of L model (0.114 and 0.163, respectively) was significantly higher than those values of Z and S models (≤0.071, P<0.05); for left parotid gland, the predicted error of S model (0.033) did not present significant difference from those values of Z and L models (≤0.025, P>0.05); for right parotid gland, S model (0.033) demonstrated significantly higher prediction error than those of Z and L models (≤0.028, P<0.05). Regarding different OAR, S model showed a lower standard deviation of prediction accuracy when comparing to Z and L models (0.016, 0.018 and 0.060, respectively). Conclusions:In the prediction of DVH in bladder and rectum of PC, Z and S models were more accurate than L model. In contrast, Z and L models demonstrated higher accuracy than S model in the prediction of left and right parotid glands of NPC. In respect to different OAR, the generalized robustness of S model was superior than the other two models.

Objective:To analyze the impacts of different registration ranges on the accuracy of multiple metastases treated with helical tomotherapy.Methods:According to the locations of target volumes, 28 patients with multiple metastases were divided into the head/chest group ( n= 15) and the chest/pelvis group ( n= 13). The CT and MVCT images acquired in first fraction were studied and compared in two groups, which were captured and matched with different registration ranges (all targets/the targets in proximity to the head/ the targets in proximity to the foot). The CTV MVCT volume coverage rate (CR) under the matched target volumes, the dice similarity coefficient (DSC) between the CTV CT and CTV MVCT, and the position deviation of the CTV geometric center were compared. Results:We observed similar results in the head/chest group and chest/pelvis group. Specifically, there was no significant difference in the CR, DSC and geometric center deviation between the two target regions when registered with all targets ( P>0.05). Regarding single target region registration, the DSC and geometric center deviation of this target were significantly superior to the other non-registered target ( P< 0.05). To a single target, the CR, DSC, and geometric center deviation obtained with registration presented the best performance, which was significantly greater than these parameters obtained with all targets registration, while the other side target area obtained the worst results ( P< 0.05). Conclusions:Registration of one target region may reduce the accuracy of other non-registered targets. We recommend that the image guidance ranges for multiple metastases treated with tomotherapy should include all target regions or independent registrations for different targets.

Objective To investigate the clinical efficacy and safety of high-dose caspofungin (70 mg/d)as initial or salvage treatment for invasive pulmonary aspergillosis.Methods Twenty-one patients with proven or probable invasive pulmonary aspergillosis from June 2014 to October 2017 in Huashan Hospital,Fudan University were retrospectively reviewed.According to the anti-fungal treatment before high-dose caspofungin application,patients were divided into initial treatment group and salvage treatment group.Patients' clinical data and laboratory data were collected.The characteristics,clinical efficacy,adverse reactions,one-year survival rate and the overall effective rate were evaluated.The prognosis of the two groups was compared by Kaplan-Meier analysis.Results Twenty of the 21 patients opportunistic acquired invasive pulmonary aspergillosis during the treatment of underlying diseases.Five patients were initially treated with high-dose caspofungin for 68 (62) days.At week 12,one patient achieved complete response,3 patients achieved partial response,and the overall effective rate was 80％ (4/5).Sixteen patients received caspofungin as salvage therapy for 66.50 (58) days,of which one patient got complete response at week 12,10 had partial response,and the overall effective rate was 68.75％ (11/16).One-year follow-up showed that no patient died in the initial treatment group,and the one-year survival rate was 100％ (5/5).In salvage treatment group,3 patients died of pulmonary bacterial infections and the one-year survival rate was 81.25％ (13/16).During treatment,one patient had elevated total bilirubin,which was possibly associated with high-dose caspofungin.Conclusions High-dose caspofungin regimen has good efficacy and safety,both for initial treatment and salvage therapy in patients with invasive pulmonary aspergillosis.