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Cutaneous T-cell lymphomas are a relatively rare group of mature T-cell lymphomas mainly manifesting in the skin, and its common subtype is mycosis fungoides. Total skin electron irradiation is one of the important conventional treatment methods, but there are many disadvantages, such as uneven dose distribution, poor position repetition, and long treatment time, which affect the clinical efficacy and patient prognosis. With the emergence and gradual popularization of helical tomotherapy in recent years, more and more medical institutions are gradually expanding their applications in total skin irradiation due to their ability to treat ultra-long targets and achieve dose-sculpted distribution, aiming to further explore its good or bad, and confirm whether it can replace the traditional total skin electron irradiation. In this article, research progress on total skin irradiation using helical tomotherapy was reviewed, the development of treatment technology, clinical efficacy and current concerns and controversies were illustrated.
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Objective:To investigate the feasibility of applying the ArcherQA three-dimensional (3D) dosimetric verification system in intensity-modulated radiotherapy (IMRT) plans for nasopharyngeal carcinoma (NPC).Methods:A retrospective analysis was conducted for 105 NPC patients′ IMRT plans developed using the Eclipse treatment planning system (TPS). Dose verification was conducted using the ArcherQA system and through portal dosimetry (PD). Moreover, this study compared γ passing rates (criteria: 3 mm/3%, TH = 10%) between ArcherQA and PD and the doses delivered to the target volume ( Dmean, D90%) and organs at risk (OARs) ( Dmean) between ArcherQA and TPS, and analyzed the 3D γ passing rates of each organ at risk calculated by ArcherQA. Results:The average 3D γ passing rate calculated by ArcherQA was (99.04±1.01)%, and the average 2D γ passing rate measured by PD was (99.49±0.78)%, with statistically significant differences ( t=-3.35, P< 0.05). The dosimetric differences to the target volume between ArcherQA and TPS were as follows: the average difference in Dmean to the gross tumor volume (GTV) was (0.57±0.48)%, and the average difference in D90% was (0.65±0.56)%. For the target volume, the average γ passing rate was (97.67±3.43)% for GTV, (97.80±4.35)% for GTVnd-L, (97.82±4.07)% for GTVnd-R, (97.88±2.44)% for CTV1, and (96.64±4.32)% for CTV2. The mean dose difference of each target volume was CTV1 (0.57±0.46)%, GTVnd-L (0.85±0.55)%, GTVnd-R (0.73±0.55)%, and CTV2 (0.88±0.52)%. For OARs, the mean γ passing rate was (99.93±0.22)% for the brainstem, (99.17±2.82)% for the optic chiasm, (100±0)% for the lens, (99.56±1.05)% for the spinal cord, (99.00±2.06)% for the thyroid, and (87.86±10.42)% for the trachea. Statistically significant differences in the average doses to OARs were observed ( t=-14.62 to 4.82, P<0.05), except for those to the left optic nerve, the right hippocampus, and the right parotid gland. Conclusions:Based on the high-performance GPU platform and the Monte Carlo dose algorithm, ArcherQA can provide accurate 3D dose distribution and 3D γ passing rates inside patients according to CT images and provide the dose volume histogram (DVH) of various regions of interest (ROIs). Therefore, the ArcherQA three-dimensional dose verification system can be applied to IMRT plans for NPC. Moreover, it is inducive to improve the treatment efficiency since it does not occupy the accelerator operation time.
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Objective:To develop a dose prediction-based quantitative evaluation method of the quality of radiotherapy plans, and to verify the clinical feasibility and clinical value of the method .Methods:The 3D U-Netwas trained using the radiotherapy plans of 45 rectal cancer cases that were formulated by physicists with more than five years of radiotherapy experience. After obtaining 3D dose distribution using 3D U-Net prediction, this study established the plan quality metrics of intensity modulated radiotherapy(IMRT) rectal cancer radiotherapy plans using dose-volume histogram(DVH) indexes of dose prediction. Then, the initial scores of rectal cancer radiotherapy plans were determined.Taking the predicted dose as the optimization goal, the radiotherapy plans were optimized and scored again. The clinical significance of this scoring method was verified by comparing the scores and dosimetric parameters of the 15 rectal cancer cases before and after optimization.Results:The radiotherapy plans before and after optimization all met the clinical dose requirements. The total scores were(77.21±9.74) before optimization, and (88.78±4.92) after optimization. Therefore, the optimized radiotherapy planswon increased scores with a statistically significant difference( t=-4.105, P<0.05). Compared to the plans before optimization, the optimized plans show decreased Dmax of all organs at risk to different extents. Moreover, the Dmax, V107%, and HI of PTV and the Dmax of the bladder decreased in the optimized plans, with statistically significant differences ( t=2.346-5.771, P<0.05). There was no statistically significant difference in other indexes before and after optimization ( P>0.05).The quality of the optimized plans were improved to a certain extent. Conclusions:This study proposed a dose prediction-based quantitative evaluation method of the quality of radiotherapy plans. It can be used for the effective personalized elevation of the quality of radiotherapy plans, which is beneficial to effectively compare and review the quality of clinical plans determined by different physicists and provide personalized dose indicators. Moreover, it can provide great guidance for the formulation of clinical therapy plans.
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Objective:To develope a self-adjustable automatic planning method of intensity modulated radiotherapy based on predicted dose, in order to enhance the robustness of automatic planning.Methods:After the patients′ dose by 3D U-Res-Net_B network was predicted, the current dose was calculated based on the last iteration result, then the predicted dose was combined to calculate the target dose and optimized. With all iterations completed or exit conditions satisfied, final treatment plannings would be acquired. A total of 30 cases of rectal cancer were tested to verify the effectiveness of the algorithm.Results:The mean value of planning target volumes′ V100% was (95.03±0.91)% for clinical plans, close to (94.67±1.96)% for automatical plans( P>0.05), and better than (92.90±2.13)% for predicted dose with the statisically significant difference ( t=29.0, P<0.05). Automatic planning′s indexes such as V35 of small intestines, V40 of bladders and V20 - V40 of femoral heads were lower than predicted and clinical ones, with the statisically significant difference( t=4.5-118.0, P<0.05). Discrepancy in other indexes of organs at risk was not statistically significantly different( P>0.05). Conclusions:This method made automatic planning processes more robust and more adaptive to difficult clinical situations.
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Objective:To assess proton biological dose with using two kinds of relative biological effect models and to compare them with traditional clinical proton biological dose ( Dose1.1). Methods:Based on Particle simulation tools(TOPAS), physical dose, LET d and LET t were calculated in water phantom and two anthropomorphic phantoms (brain and prostate tumors) respectively. Then DoseLET d and DoseLET t were calculated according to different relative biological effect models, an RBE was 1.1 in traditional clinical proton biological dose calculation. Three kinds of biological doses were compared in the water phantom. To quantify the differences between three method in anthropomorphic phantoms, three points ( D1, D2, D3) were selected according to the physical dose to compare the biological dose. Results:DoseLET d and DoseLET t in water phantom showed the same trend with water depth and both of them were higher than Dose1.1 at the end of proton beam range. The maximal difference between DoseLET d and DoseLET t in the anthropomorphic phantoms was 10.08 cGy, where the relative difference was less than 5%. When DoseLET d and DoseLET t were compared with Dose1.1, the maximal differences in brain tumor target were 71.97 cGy and 61.91 cGy respectively, where the relative differences were less than 25%. The maximal differences in prostate tumor target were 25.95 and 19.96 cGy respectively, where the relative differences were less than 12%. However, the differences outside the target were very small, where the maximal differences in brain and prostate tumors were 5.99 cGy and 9.92 cGy respectively, and the relative differences were less than 5%. Conclusions:Biological doses calculated by two method are of little difference in both water and anthropomorphic phantoms, however, large differences were observed when they were compared with the traditional clinical proton biological dose especially in the high dose area.
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Objective:To develop a deep learning model for predicting three-dimensional (3D) voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT).Methods:A total of 110 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 90 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net was constructed to predict 3D dose distributions. Three types of 3D matrices from CT images, structure sets and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of IMRT dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: the average dose prediction bias and mean absolute errors (MAEs)of all voxels within the body, the dice similarity coefficients (DSCs), Hausdorff distance(HD 95) and mean surface distance (MSD) of different isodose surfaces were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; the dosimetric index (DI) including homogeneity index, conformity index, V50, V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired-samples t test. Results:For the 20 testing cases, the average prediction bias ranged from -2.12% to 2.88%, and the MAEs varied from 2.55% to 5.75%. The DSCs value was above 0.9 for all isodose surfaces, the average MSD ranged from 0.21 cm to 0.45 cm, and the average HD 95 varied from 0.61 cm to 1.54 cm. There was no statistically significant difference for all DIs, except for bladder Dmean. Conclusions:This study developed a deep learning model based on 3D U-Res-Net by considering beam configurations input and achieved an accurate 3D voxel-wise dose prediction for rectal cancer treated by IMRT.
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Objective Because of high precision and mild side effects,intensity-modulated proton therapy (IMPT) has become a hot spot in the radiotherapy field.Nevertheless,the precision of IMPT is extremely sensitive to the range uncertainties.In this paper,a novel robust optimization method was proposed to reduce the effect of range uncertainty upon IMPT.Methods Firstly,the robust optimization model was established which contained three types of range including the increased range,the normal range and the shortened range.The objective function was expressed in quadratic function.The organ dose contribution matrix of each range was calculated by proton pencil beam algorithm.The range deviation was discretized and the probability of each range was obtained based on the Gauss distribution function.Finally,the conjugate gradient method was adopted to find the optimal solution to make the actual dose coverage of the target area and the organs at risk distributed within the expected dose as possible.Results The 3 sets of simulation tests provided by the AAPM TG-119 Report were utilized to evaluate the effectiveness of this method:nasopharyngeal carcinoma,prostate and "C"-type cases.Compared with conventional IMPT optimization approach,this novel method was less sensitive to the range uncertainty.When the range deviation occurred,the dose coverage of the target area and organs at risk of the nasopharyngeal carcinoma and prostate cases almost reached the expected dose,and the high dose coverage of the target area and organs at risk protection were improved in the"C"-type cases.Conclusions To compensate for the range uncertainty,this novel method can enhance the dose coverage of the target area and reduce the dose coverage of the organs at risk.
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Objective To evaluate the feasibility of utilizing dose-volume histogram (DVH) prediction models of organs at risk (OARs) to deliver automatic treatment planning of prostate cancer.Methods The training set included 30 cases randomly selected from a database of 42 cases of prostate cancer receiving treatment planning.The bladder and rectum were divided into sub-volumes (Ai) of 3 mm in layer thickness according to the spatial distance from the boundary of planning target volume (PTV).A skewed normal Gaussian function was adopted to fit the differential DVH of Ai,and a precise mathematical model was built after optimization.Using the embedded C++ subroutine of Pinnacle scripa,ahe volume of each Ai of the remaining validation set for 12 patients was obtained to predict the DVH parameters of these OARa,ahich were used as the objective functions to create personalized Pinnacle script.Finalla,automatic plans were generated using the script.The dosimetric differences among the original clinical plannina,aredicted value and the automatic treatment planning were statistically compared with paired t-test.Results DVH residual analysis demonstrated that predictive volume fraction of the bladder and rectum above 6 000 cGy were lower than those of the original clinical planning.The automatic treatment planning significantly reduced the V70,V60,V50 of the bladder and the V70 and V60 of the rectum than the original clinical planning (all P＜0.05),the coverage and conformal index (CI) of PTV remained unchangea,and the homogeneity index (HI) was slightly decreased with no statistical significance (P＞ 0.05).Conclusion The automatic treatment planning of the prostate cancer based on the DVH prediction models can reduce the irradiation dose of OARs and improve the treatment planning efficiency.
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BACKGROUND: The surface electromyography can evaluate the effect of exercise by recording the parameters of muscle activities, and vibration exercise is the best supplement to traditional weight training. More research focuses on the effect of simple vibration training on the surface electromyography of lower limbs, but the influence of vibration combined with weight-bearing training is poorly understood.OBJECTIVE: To study the effect of vibration combined with load on the surface electromyography of lower limbs at the micro level of muscle working principle.METHODS: Eight healthy college students were recruited, and subjected to four different stimulations: vibration (45 Hz) combined with load (45% one repetition maximum (IRM)); vibration (45 Hz) combined with load (60% 1RM); vibration (50 Hz) combined with load (45% 1RM); vibration (50 Hz) combined with load (60% 1RM), followed by semi-squat exercise with the heel lifting, 10 times/minute, for 3 courses with more than 2 hours in between.RESULTS AND CONCLUSION: Different vibrations combined with loads made significant difference on the root mean square amplitude of the surface electromyography (P < 0.01), and there was a significantly increased root mean square amplitude in the vibration (50 Hz) combined with load (45% 1RM), especially at the medial gastrocnemius. The four kinds of stimulations made significant different effects on the surface electromyography of rectus femoris, tibialis anterior, and medial gastrocnemius, except semitendinosus (P ≤ 0.05). Moreover, the effect showed significant difference among different stimulations except vibration (50 Hz) combined with load (45% 1RM) (P ≤ 0.05). Compared with the other three stimulations, vibration (50 Hz) combined with load (45% 1RM) exerted better effect on the muscular activation. To conclude, different vibrations combined with loads exert different effects on the motor unit of same neuromuscular activity, and a suitable stimulation may produce better effect. Besides, the same stimulus for the motor unit of different neuromuscular activities produces different effects, which may match to the muscle nature.
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Objective To build a Monte Carlo dose verification tool for IMRT Plan by implanting an irradiation source model into DPM code and to extend the ability of DPM to calculate any incident angles and irregular-inhomogeneous fields.Methods The virtual source and the energy spectrum unfolded from the accelerator measurement data were used,in combination with optimized intensity maps,to calculate the dose distribution of the irradiation irregular-inhomogeneous field.The irradiation source model of accelerator was substituted by a grid-based surface source.The contour and the intensity distribution of the surface source were optimized by IMRT.The dose calculation was realized by combining the position of the emitter with the fluence map from the IMRT plan.The weight of the emitter was decided by the grid intensity.The direction of the emitter was decided by the combination of the virtual source and the emitting position.The weighted fraction of the emitter was also combined with the flux grid intensity based on the particle transport model of DPM code.Results The accuracy of calculation was verified by comparing with the measured data.It was illustrated that the differences were acceptable (＜ 2％ inside the field,2-3 mm in the penumbra).The dose calculation of irregular field by DPM simulation was also compared with that of FSPB (Finite Size Pencil Beam).The passing rate of gamma analysis was 95.1％ for peripheral lung cancer.The regular field and the irregular rotational field were all within permissible range of error.The calculation time of regular fields were less than 2 h,and that of the test of peripheral lung cancer was 160 min.Conclusions The adapted DPM code with its simple irradiation source model is faster than that with classical Monte Carlo procedure.Its computational accuracy and speed satisfy the clinical requiremcnt,and it can be useful as a Monte Carlo dose verification tool for IMRT Plan.
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The objective of this study is to compare the normalized prediction distribution errors (NPDE) and the visual predictive check (VPC) on model evaluation under different study designs. In this study, simulation method was utilized to investigate the capability of NPDE and VPC to evaluate the models. Data from the false models were generated by biased parameter typical value or inaccurate parameter inter-individual variability after single or multiple doses with the same sampling time or multiple doses with varied sampling time, respectively. The results showed that there was no clear statistic test for VPC and it was difficult to make sense of VPC under the multiple doses with varied sampling time. However, there were corresponding statistic tests for NPDE and the factor of study design did not affect NPDE significantly. It suggested that the clinical data and model which VPC was not fit for could be evaluated by NPDE.
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Animals , Humans , Computer Simulation , Models, Biological , Models, Statistical , Nonlinear Dynamics , Pharmaceutical Preparations , Metabolism , Pharmacokinetics , Predictive Value of Tests , SoftwareABSTRACT
Objective To explore the risk factors for intracranial hemorrhage caused by late vitamin K deficiency bleeding(VKDB),in order to prevent and reduce the incidence of intracranial hemorrhage caused by late VKDB.Methods A retrospective analysis of the risk factors of late VKDB and intracranial hemorrhage was applied to 2 groups of patients in PICU and department of neurology of Beijing children's Hospital from Jan.2002 to Dec.2007.In group Ⅰ,there were 90 patients suffering from intracranial hemorrhage caused by late VKDB;while in group Ⅱ,there were 23 patients of late VKDB without intracranial hemorrhage.Within 12 hours of hospitalization,the following 9 items were checked:the cranial CT,blood calcium concentration,liver function,serum sodium,blood glucose,prothrombin time,partial thromboplastin time,fibrinogen concentration,and platelet.Ten possible relevant risk factors of gender,age,birth situation,feeding patterns,recent diarrhea,cytomegalovirus(CMV)infection,hypocalcemia,dysglycemia,hyponatremia,and abnormal liver function were analyzed by the method of non-conditional Logistic regression analysis.Results Statistically significant difference had been found in the 3 factors of hypocalcaemia,recent diarrhea,abnormal liver function(Pa