Background and purpose:In recent years,domestic radiotherapy equipment and related software have made great progress,and testing the functionality and stability of the equipment and software is an essential step.This paper focused on comparing the differences in intensity-modulated radiation therapy(IMRT)plans dosimetry and organ at risk(OAR)volume calculations for common cancers between uRT-treatment planning system(TPS)and Monaco-TPS,and to evaluate the feasibility of dose calculation for Infinity linac(linear accelerator,Elekta,Sweden)using uRT-TPS.Methods:Twenty cases of rectal cancer,lung cancer,breast cancer and nasopharyngeal carcinoma were selected.The IMRT plans were completed in uRT-TPS and Monaco-TPS.The dose uniformity and conformity,mean dose,maximum dose of planning target volume(PTV)and OAR between two plans under the same prescribed dose of PTV were compared.And the pass rates of two TPS plans validated at the same linear accelerator were compared.Meanwhile,monitor units(MU),source skin distance(SSD)and the volume of OAR in uRT-TPS and Monaco-TPS were compared.Results:Wonderful plans that met the clinical requirements were obtained in uRT-TPS and Monaco-TPS.Comparable uniformity and conformability was received in PTV,and the maximum dose of PTV was reduced by 1.1 Gy for uRT-TPS(P = 0.006).For breast cancer and lung cancer,the dose in lung was lower for Monaco-TPS(P＜0.05).For nasopharyngeal carcinoma,the dose indicators that oral cavity and throat in the uRT-TPS was reduced by 9.2%and 5.1%,respectively.The verification results of absolute point dose(＜3%)and three-dimensional surface dose(＞95%)for both plans met the clinical requirements.The region of interest in uRT-TPS was smaller compared with Monaco-TPS(P＜0.05).Conclusion:A comparable IMRT plan was obtained for common tumors in uRT-TPS and Monaco-TPS.It is feasible to calculate the dose of Infinity linac using uRT-TPS.

Objective:To develop and validate a structure for broadening the Bragg peak to improve the efficiency and conformality of particle radiotherapy.Methods:Techniques of random stacking and regular stacking were employed to fabricate the mesh-stacked porous structure (MPS). In each layer of the grid, the thickness, line width and spacing were set at 0.1 mm, 0.1 mm and 0.5 mm, respectively, resulting in a total size of 10 cm ×10 cm. Monte Carlo code FLUKA was performed to simulate the transportation of 196 MeV/u carbon ion beam and a 105 MeV proton beam through the MPS. Dose distribution, fluence homogeneity, and modulation stability of the modulated beams were evaluated. Moreover, the modulation effect of MPS in clinical radiotherapy plans for nasopharyngeal carcinoma (63 Gy in 21 fractions), lung cancer (77 Gy in 22 fractions) and prostate cancer (70.4 Gy in 16 fractions) was also evaluated, respectively.Results:The MPS was capable of broadening the Bragg peak width by 1.73 mm for proton beams and 2.95 mm for carbon ion beams. For different entrance positions, regular stacking of more than 10 layers could reduce the modulation power difference of MPS to within 5%. For MPS with 30 layers of regular stacking, the modulated fluence homogeneity could achieve a value of less than 3% by transporting 18 cm distance in air. When comparing to the clinically used ripple filters, MPS reduced the isocenter spot size of proton beams by 0.91 mm. In the comparison study of the treatment plan for nasopharyngeal carcinoma, the use of MPS could shorten the treatment time by 213 s (37%) and reduce the maximum dose to the brainstem by 3.28 Gy (7.5%).Conclusions:MPS effectively broadens the Bragg peak of particle beams and improves the efficiency of clinical radiotherapy. Regularly stacked MPS demonstrates robust modulation stability, and the modulated beam achieves relatively well fluence homogeneity, making it a promising clinical application for closer to the patients and reducing lateral scattering.

Background and purpose:Deep learning methods can be used for automatic segmentation and detection of lymphocytes on pathological images.This study aimed to assess the performance of using variational autoencoding pre-training method for lymphocyte infiltration detection on pathological images,as well as the impact of removing tumor necrosis regions on model performance.Methods:Using variational autoencoding(VAE)pre-training method,pre-training was performed on a large number of unlabeled pathological images from the Cancer Genome Atlas(TCGA)database(TCGA-COAD and TCGA-READ)to obtain an auto-encoding pre-training model,and then a classifier model of lymphocyte infiltration was trained on the public data samples.To avoid confusion with necrotic regions,a Unet segmentation model for tumor necrotic regions was trained to remove the influence of tumor necrotic regions on lymphocyte identification.Results:The lymphocyte infiltration detection model pre-trained with the VAE model had an area under curve(AUC)of 0.979(95%CI:0.978-0.980),an accuracy of 92.5%(95%CI:92.3%-92.6%),a kappa value of 0.849,sensitivity of 0.908,specificity of 0.941,precision of 0.939,recall of 0.908,and F1 of 0.923 under the receiver operating characteristic(ROC)curve on the training set.The AUC for the validation set was 0.968(95%CI:0.964-0.972),the accuracy was 91.3%(95%CI:90.6%-92.0%),kappa value was 0.826,sensitivity was 0.898,specificity was 0.928,precision was 0.925,recall was 0.898,and F1 was 0.912.The results of Resnet18 model on the labeled dataset were as follows:accuracy of the validation set was 83.2%(95%CI:82.2%-84.1%),kappa value was 0.664,sensitivity was 0.823,specificity was 0.840,precision was 0.838,recall was 0.823 and F1 was 0.830.The Unet model that segmented the necrotic regions of the tumors had a final DICE of 0.78 for the training set,and 0.76 for the validation.After removing the necrotic region,the predictive performance of the pre-trained lymphocyte infiltration detection model using the VAE proposed in this article was improved to some extent,with the AUC on the validation set increasing from 0.968(95%CI:0.964-0.972)to 0.971(95%CI:0.968-0.975).The accuracy was 92.4%(95%CI:91.7%-93.0%),kappa value was 0.849,sensitivity was 0.928,specificity was 0.921,precision was 0.921,recall was 0.928,and F1 was 0.925.Conclusion:Using the variational autoencoding model pre-training method to classify the pathological pictures of lymphocyte infiltration can obtain better model performance compared with direct training,and removing the influence of tumor necrosis areas through Unet can further improve the performance of the model.

Parvalbumin interneurons belong to the major types of GABAergic interneurons. Although the distribution and pathological alterations of parvalbumin interneuron somata have been widely studied, the distribution and vulnerability of the neurites and fibers extending from parvalbumin interneurons have not been detailly interrogated. Through the Cre recombinase-reporter system, we visualized parvalbumin-positive fibers and thoroughly investigated their spatial distribution in the mouse brain. We found that parvalbumin fibers are widely distributed in the brain with specific morphological characteristics in different regions, among which the cortex and thalamus exhibited the most intense parvalbumin signals. In regions such as the striatum and optic tract, even long-range thick parvalbumin projections were detected. Furthermore, in mouse models of temporal lobe epilepsy and Parkinson's disease, parvalbumin fibers suffered both massive and subtle morphological alterations. Our study provides an overview of parvalbumin fibers in the brain and emphasizes the potential pathological implications of parvalbumin fiber alterations.
Mice ; Animals ; Epilepsy, Temporal Lobe/pathology* ; Parvalbumins/metabolism* ; Parkinson Disease/pathology* ; Neurons/metabolism* ; Interneurons/physiology* ; Disease Models, Animal ; Brain/pathology*

Purpose: In non-metastatic prostate cancer (nmPCa) setting, it is important to early identify the patients at risk of biochemical recurrence (BCR) for immediate postoperative intervention. Our study aimed to evaluate the potential clinical utility of circulating tumor DNA (ctDNA) for predicting disease recurrence. Materials and Methods: This real-world observational study evaluated 161 cases of nmPCa undergoing next-generation sequencing at our institution. A total of 139 ctDNA samples and 31 biopsied tumor tissue underwent genomic profiling. The study endpoint was BCR after radical prostatectomy. Relationships between the ctDNA status and the biochemical progression-free survival (bPFS) were analyzed by log-rank test and multivariate Cox regression. Results: Of 161 enrolled patients, 19 (11.8%) harbored deleterious alterations in NCOR2, followed by BRCA2 (3.7%), ATR (2.5%), and CDK12 (2.5%). Of available pre-operative blood samples (n=139), ctDNA was detectable in 91 (65.5%). Until last follow-up, 56 of 68 patients (85.3%) with detectable ctDNA had achieved BCR, whereas only eight of 39 patients (20.5%) with undetectable ctDNA had achieved BCR. Patients who had undetectable ctDNA experienced significantly longer bPFS compared with those who had detectable ctDNA (not available vs. 8.2 months; hazard ratio, 0.14; p < 0.01). Pre-operative ctDNA status was a significant prognostic factor of disease recurrence. Conclusion Pre-operative ctDNA detection could identify patients at high risk of recurrence and has the potential to inform immediate postoperative interventions, but these approaches remain to be validated in prospective studies. ctDNA studies can provide insights into accurate monitoring and precise treatment rather than simply following routine clinical care.

Objective:To study the influence of environmental radiation of radiotherapy workplace on the stereotactic radiation therapy(SRT) plan absolute dose verification with plastic scintillator detector Exradin W1.Methods:The computed tomography (CT) image of the stereotactic dose verification phantom (SDVP) was scanned and imported into the treatment planning system. Three schemes, including 3 cm × 3 cm to 20 cm × 20 cm square gradient field irradiation, virtual planning target volume(PTV) non-coplanar arcs irradiation and 10 cases of volumetric modulated arc radiotherapy SRT (VMAT SRT) clinical plan verification, were measured with or without a home-made shield over the photodiode. Measurements were recorded to analyze the impact of environmental radiation on dose measurement under different conditions.Results:The noise effect of the photodiode increased with the the lager open field size, and decreased with the reduced distance between the photodiode and isocenter. The contribution of photodiode noise effect increase with the lager non-coplanar arc field size, with the largest up to 4.16%. As for the clinical SRT plan verification measurement, the relative difference between the SRT plan measurements and treatment planning system(TPS) before and after shielding were (1.39±1.05)% and (0.59±1.03)%, respectively ( t=-5.343, P < 0.05). and for W1 vs. A16 microchamber was (1.22±1.56)% and (0.42±1.42)%, respectively ( t=-5.414, P < 0.05). Conclusions:The measurements of Exradin W1 are in good agreement with the TPS result and the ionization chamber measurements, but its accuracy is easily affected by the environmental radiation of radiotherapy workplace. To measure non-coplanar radiation, the photodiode should be placed as far away as possible from the isocenter and be properly shielded, which can effectively improve the accuracy and stability of the measurement and provide a strong guarantee for clinical precision radiotherapy.

Objective:To investigate the clinical efficacy of neoadjuvant chemo-hormonal therapy(NCHT)followed by radical prostatectomy(RP) plus extended pelvic lymphadenectomy for very-high-risk locally advanced prostate cancer.Methods:The data of 327 cases of very-high-risk locally advanced prostate cancer treated in Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine, The Second Hospital of Tianjin Medical University, and The Third Affiliated Hospital of Sun Yat-sen University from December 2014 to July 2019 were retrospectively analyzed. Patients were divided into two groups according to treatment regimens: the RP group (direct RP + extended pelvic lymphadenectomy 4-6 weeks after the biopsy of prostate) and the NCHT group (4-6 cycles of NCHT prior to RP). There were 171 cases in RP group and 156 cases in NCHT group, respectively. In the RP group, the median age was 67 (ranging 44-83)years. The median PSA at diagnosis was 27.24 (ranging 4.55-207.00) ng/ml. Patients’numbers of clinical T 2, T 3a, T 3b, T 4 stage were 13, 85, 57, 16, respectively, and clinical N 1, N 0 stage were 33 and 138, respectively. Patients’numbers of ISUP grade groups of 1, 2, 3, 4, 5 were 5, 35, 41, 51, 39, respectively. In the NCHT group, The median age was 67 years, ranging 46-78 years. The median PSA at diagnosis was 72.09(ranging 4.08-722.95)ng/ml. Patients’ numbers of clinical T 2, T 3a, T 3b, T 4 stage were 11, 47, 58, 40, respectively, and clinical N 1, N 0stage were 76 and 80, respectively. Patients’numbers of ISUP grade groups of 1, 2, 3, 4, 5 were 1, 11, 33, 43, 68, respectively. At baseline, the NCHT group showed higher PSA, higher ISUP grade, and more advanced clinical stage at diagnosis( P<0.05). The PSA, pathological down-staging rate, and positive surgical margin rate as well as the biochemical recurrence free survival(bRFS)were compared between the two groups. Results:After radical prostatectomy, compared with the RP group, the NCHT group had a higher proportion of patients achieving PSA<0.2 ng/ml at 6-week postoperative follow-up ( P<0.001), a higher pathologic tumor stage down-staging rate ( P<0.001), a higher ISUP down-grading rate ( P<0.001), and a lower positive surgical margins rate ( P<0.001). In addition, 10.9% of the NCHT group achieved pT 0 or minimal residual disease in postoperative pathology exams. Eighty-three patients (48.5%) in the RP group and 125 patients (80.1%) in the NCHT group achieved undetectable PSA after surgery and entered further analysis for bRFS, which showed NCHT group had significantly longer bRFS (19.46 months vs. 6.35 months). NCHT significantly reduced the risk for biochemical recurrence in locally advanced prostate cancer patients( HR=0.278, 95% CI 0.198-0.390, P<0.001). Such a reduce in risk for biochemical recurrence was seen in all subgroups( P<0.001). Conclusions:NCHT might improve surgical outcomes as well as bRFS in very-high-risk locally advanced prostate cancer patients.

Objective:To realize automatic delineation of rectal cancer target volume and normal tissues and improve clinical work efficiency.Methods:The deep learning method based on convolutional neural network was adopted to construct neural network, learn and realize automatic delineation, and compare the differences between automatic delineation and manual delineation.Results:Two hundred and ten cases with rectal cancer were randomly assigned to a training set of 190 and a validation set of 20. The complete delineation of a single case took about 10s; the average Dice of CTV was 0.87±0.04; the average Dice of other normal tissues was bigger than 0.8; the Hausdorff distance (HD) index of CTV was 25.33±16.05; the mean distance to agreement (MDA) index was 3.07±1.49, and the Jaccard similarity coefficient (JSC) index was 0.77±0.07.Conclusion:The deep learning method based on full convolutional neural network can realize the automatic delineation of rectal cancer target volume and improve work efficiency.

Objective:To develop a deep learning-based approach for predicting the dose distribution of intensity-modulated radiotherapy (IMRT) for breast cancer patients, and evaluate the feasibility of applying the predicted dose distribution in the automatic treatment planning.Methods:A total of 240 patients with left breast cancer admitted to Fudan University Shanghai Cancer Center were enrolled in this study: 200 cases in the training dataset, 20 cases in the validation dataset and 20 cases in the testing dataset. A modified deep residual neural network was trained to establish the relationship between CT image, the contouring images of target area and organs at risk (OARs) and the dose distribution, aiming to predict the dose distribution. The predicted dose distribution was utilized as the optimization objective function to optimize and generate a high-quality plan.Results:Compared with the dose distribution of clinical treatment plan, the predicted dose distribution for target areas and OARs showed no statistical significance except for a simultaneous boost target PTV 48Gy. And the treatment plan generated based on the predicted dose distribution was basically consistent with the predicted outcomes. Conclusion:Our results demonstrate that the deep learning-based approach for predicting the dose distribution of IMRT for breast cancer contributes to further achieving the goal of automatic treatment planning.