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Purpose To evaluate the dosimetric characteristics of ZAP-X stereotactic radiosurgery (SRS) for single brain metastasis by comparing with two mature SRS platforms. Methods Thirteen patients with single brain metastasis treated with CyberKnife (CK) G4 were selected retrospectively. The prescription dose for the planning target volume (PTV) was 18–24 Gy for 1–3 fractions. The PTV volume ranged from 0.44 to 11.52 cc.Treatment plans of thirteen patients were replanned using the ZAP-X plan system and the Gamma Knife (GK) ICON plan system with the same prescription dose and organs at risk (OARs) constraints. The prescription dose of PTV was normalized to 70% for both ZAP-X and CK, while it was 50% for GK. The dosimetric parameters of three groups included the plan characteristics (CI, GI, GSI, beams, MUs, treatment time), PTV (D 2 , D 95 , D 98 , D min , D mean , Coverage), brain tissue (volume of 100%-10% prescription dose irradiation V 100% -V 10% , D mean ) and other OARs (D max , D mean ),all of these were compared and evaluated. All data were read and analyzed with MIM Maestro. One-way ANOVA or a multisample Friedman rank sum test was performed, where p  < 0.05 indicated significant differences. Results The CI of GK was significantly lower than that of ZAP-X and CK. Regarding the mean value, ZAP-X had a lower GI and higher GSI, but there was no significant difference among the three groups. The MUs of ZAP-X were significantly lower than those of CK, and the mean value of the treatment time of ZAP-X was significantly shorter than that of CK. For PTV, the D 95 , D 98 , and target coverage of CK were higher, while the mean of D min of GK was significantly lower than that of CK and ZAP-X. For brain tissue, ZAP-X showed a smaller volume from V 100% to V 20% ; the statistical results of V 60% and V 50% showed a difference between ZAP-X and GK, while the V 40% and V 30% showed a significant difference between ZAP-X and the other two groups; V 10% and D mean indicated that GK was better. Excluding the D max of the brainstem, right optic nerve and optic chiasm, the mean value of all other OARs was less than 1 Gy. For the brainstem, GK and ZAP-X had better protection, especially at the maximum dose. Conclusion For the SRS treating single brain metastasis, all three treatment devices, ZAP-X system, CyberKnife G4 system, and GammaKnife system, could meet clinical treatment requirements. The newly platform ZAP-X could provide a high-quality plan equivalent to or even better than CyberKnife and Gamma Knife, with ZAP-X presenting a certain dose advantage, especially with a more conformal dose distribution and better protection for brain tissue. As the ZAP-X systems get continuous improvements and upgrades, they may become a new SRS platform for the treatment of brain metastasis.

Background The purpose of this phase II study was to evaluate the feasibility of hypofractionated radiotherapy (HFRT) with a dose of 36.5 Gy in 10 fractions in postmastectomy patients. Methods From March 2014 to December 2015, 85 patients with locally advanced breast cancer were eligible to participate in this study with a schedule of 36.5 Gy in 10 fractions. Intensity-modulated radiation therapy (IMRT) was delivered to the chest wall with or without the supraclavicular region. The primary endpoint was radiation-related toxicities. The secondary endpoints were locoregional failure-free survival (LRFFS), disease-free survival (DFS) and overall survival (OS). And the outcomes were compared with our retrospective study of 72 patients with 42.5 Gy in 16 fractions. Results The median follow-up was 69.0 (range 66.5-71.5) months in the 36.5 Gy group and 93.0 (range 91.9-94.1) months in the 42.5 Gy group, respectively. Radiation-related toxicities were mainly grade 1, although a few patients had grade 2 plexopathy (1.2%) and acute skin toxicity (1.2%) in the 36.5 Gy group, and grade 2 acute skin toxicity (5.6%) and lymphedema (4.2%) in the 42.5 Gy group. There were no significant differences between the groups in acute and late toxicities. For all the patients, the 5-year LRFFS, DFS and OS were 97.7 and 100.0%, 93.1 and 90.3%, 98.8 and 97.2%, respectively, without significant differences between the groups. Conclusion Postmastectomy HFRT with a schedule of 36.5 Gy in 10 fractions was feasible, with mild toxicities and excellent 5-year clinical outcome. Trial registration Trial registration number: ChiCTR-ONRC-14004391 . Date of registration: 9/3/2014.

Background It is very important to accurately delineate the CTV on the patient’s three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convolution Network (Dense V-Net) in predicting Clinical Target Volume (CTV) pre-delineation in cervical cancer patients for radiotherapy. Methods In this study, we used Dense V-Net, a dense and fully connected convolutional network with suitable feature learning in small samples to automatically pre-delineate the CTV of cervical cancer patients based on computed tomography (CT) images and then we assessed the outcome. The CT data of 133 patients with stage IB and IIA postoperative cervical cancer with a comparable delineation scope was enrolled in this study. One hundred and thirteen patients were randomly designated as the training set to adjust the model parameters. Twenty cases were used as the test set to assess the network performance. The 8 most representative parameters were also used to assess the pre-sketching accuracy from 3 aspects: sketching similarity, sketching offset, and sketching volume difference. Results The results presented that the DSC, DC/mm, HD/cm, MAD/mm, ∆V, SI, IncI and JD of CTV were 0.82 ± 0.03, 4.28 ± 2.35, 1.86 ± 0.48, 2.52 ± 0.40, 0.09 ± 0.05, 0.84 ± 0.04, 0.80 ± 0.05, and 0.30 ± 0.04, respectively, and the results were greater than those with a single network. Conclusions Dense V-Net can correctly predict CTV pre-delineation of cervical cancer patients and can be applied in clinical practice after completing simple modifications.

This study evaluated the clinical benefit of the application of the ZAP-X stereotactic radiosurgery (SRS) system in the treatment of glomus jugulare tumors. Two patients with recurrent or progressive glomus jugulare tumors underwent treatment with the ZAP-X SRS system. Analysis of clinical treatment processes and follow-up data over a period of up to three years revealed that after treatment with the ZAP-X system, the tumor volumes significantly reduced, and no severe radiation therapy-related complications were observed. These findings highlight the potential clinical benefits of the ZAP-X system in the treatment of complex skull base tumors.

This article reports two cases of meningioma patients treated with the novel radiosurgical device ZAP-X stereotactic radiosurgery. During the long-term follow-up period of up to four years after treatment, neither patient developed new neurological deficits, and no adverse reactions of Common Terminology Criteria for Adverse Events grade 2 or above occurred. By retrospectively analyzing the clinical diagnosis, treatment processes, and follow-up results of these two patients, this study aims to provide practical references for the large-scale clinical application of this technology.

This case describes an 81-year-old male patient with poorly differentiated lung adenocarcinoma, complicated by severe chronic obstructive pulmonary disease (COPD) and a history of coronary stenting. The patient underwent CyberKnife (Accuray Inc., Madison, WI, United States) stereotactic body radiotherapy (SBRT) with synchrony respiratory tracking and staged radiotherapy. The patient received staged radiotherapy delivered in two sequential phases: phase I (30 Gy in three fractions) was administered from November 18 to 24, 2022, followed by phase II (30 Gy in three fractions) initiated on February 20, 2023, after a 12-week intermission for therapeutic response assessment and normal tissue recovery. The cumulative prescription totaled 60 Gy in six fractions (equivalent dose in 2-Gy fractions (EQD2) = 120 Gy, α/β = 10), with strict adherence to organ-at-risk (OAR) constraints. Through fiducial marker implantation and dynamic dose optimization, the ipsilateral lung V20 was reduced from 3.35% to 1.63%. No severe treatment-related toxicities (≥ grade 2) were observed during or after the treatment period. At a three-month follow-up, the tumor volume decreased by 79% (117.04 cm³ → 24.26 cm³), demonstrating significant local control. This case provides a practical reference for personalized radiotherapy in elderly patients with cardiopulmonary comorbidities.

This article presents a case of a patient with advanced head and neck cancer, characterized by a large and protruding tumor. The patient was treated with an innovative on-demand online adaptive radiotherapy (ART) technology, guided by cone beam computed tomography (CBCT), on the Ethos adaptive radiotherapy platform (version 1.0, Varian Medical Systems, Palo Alto, CA). A solution was provided for this special case to address the issue where part of the target volume could not participate in the optimization due to exceeding the external contour boundary during online adaptive radiotherapy. The treatment outcome was satisfactory in terms of tumor regression, while only grade 1 radiodermatitis and grade 2 oral mucositis at the end of radiotherapy. This article discusses the clinical diagnosis, treatment process, and follow-up of this case, aiming to provide clinical references for a broader application of this technology.

Purpose: To investigate and compare 2 cone-beam computed tomography (CBCT) correction methods for CBCT-based dose calculation. Materials and Methods: Routine CBCT image sets of 12 head and neck cancer patients who received volumetric modulated arc therapy (VMAT) treatment were retrospectively analyzed. The CBCT images obtained using an on-board imager (OBI) at the first treatment fraction were firstly deformable registered and padded with the kVCT images to provide enough anatomical information about the tissues for dose calculation. Then, 2 CBCT correction methods were developed and applied to correct CBCT Hounsfield unit (HU) values. One method (HD method) is based on protocol-specific CBCT HU to physical density (HD) curve, and the other method (HM method) is based on histogram matching (HM) of HU value. The corrected CBCT images (CBCTHD and CBCTHM for HD and HM methods) were imported into the original planning system for dose calculation based on the HD curve of kVCT (the planning CT). The dose computation result was analyzed and discussed to compare these 2 CBCT-correction methods. Results: Dosimetric parameters, such as the Dmean, Dmax and D5% of the target volume in CBCT plan doses, were higher than those in the kVCT plan doses; however, the deviations were less than 2%. The D2%, in parallel organs such as the parotid glands, the deviations from the CBCTHM plan dose were less than those of the CBCTHD plan dose. The differences were statistically significant (P < .05). Meanwhile, the V30 value based on the HM method was better than that based on the HD method in the oral cavity region (P = .016). In addition, we also compared the γ passing rates of kVCT plan doses with the 2 CBCT plan doses, and negligible differences were found. Conclusion: The HM method was more suitable for head and neck cancer patients than the HD one. Furthermore, with the CBCTHM-based method, the dose calculation result better matches the kVCT-based dose calculation.

Parkinson’s disease (PD) is one of the cognitive degenerative disorders of the central nervous system that affects the motor system. Gait dysfunction represents the pathology of motor symptom while gait analysis provides clinicians with subclinical information reflecting subtle differences between PD patients and healthy controls (HCs). Currently neurologists usually assess several clinical manifestations of the PD patients and rate the severity level according to some established criteria. This is highly dependent on clinician’s expertise which is subjective and ineffective. In the present study we address these issues by proposing a hybrid signal processing and machine learning based gait classification system for gait anomaly detection and severity rating of PD patients. Time series of vertical ground reaction force (VGRF) data are utilized to represent discriminant gait information. First, phase space of the VGRF is reconstructed, in which the properties associated with the nonlinear gait system dynamics are preserved. Then Shannon energy is used to extract the characteristic envelope of the phase space signal. Third, Shannon energy envelope is decomposed into high and low resonance components using dual Q -factor signal decomposition derived from tunable Q -factor wavelet transform. Note that the high Q -factor component consists largely of sustained oscillatory behavior, while the low Q -factor component consists largely of transients and oscillations that are not sustained. Fourth, variational mode decomposition is employed to decompose high and low resonance components into different intrinsic modes and provide representative features. Finally features are fed to five different types of machine learning based classifiers for the anomaly detection and severity rating of PD patients based on Hohen and Yahr (HY) scale. The effectiveness of this strategy is verified using a Physionet gait database consisting of 93 idiopathic PD patients and 73 age-matched asymptomatic HCs. When evaluated with 10-fold cross-validation method for early PD detection and severity rating, the highest classification accuracy is reported to be 98.20 % and 96.69 % , respectively, by using the support vector machine classifier. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.

This dissertation consists of three independent essays in applied microeconomics. I focus on applying newly developed causal inference models to study US workers during the COVID-19 pandemic. I also study the productivity of US public banks using utilizing a novel developed stochastic frontier model. In Chapter 1, I use data from the Current Population Survey (CPS) to investigate the impact of working from home (WFH) due to COVID-19 on individual work absence and hours worked. Based on the potential outcome framework, I compare the estimated treatment effects of WFH from a traditional linear regression model and from a modern causal machine learning model which controls for the confounders non-linearly and thereby estimates heterogeneous effects. In both models, I find a negative effect of WFH on work absence due to sickness and individual hours worked. However, the linear regression model might underestimate the effect magnitude due to strong functional form assumptions. Also, the causal machine learning model discovers rich heterogeneous effects of WFH, which suggests that only looking at an average summary may not be informative compared to looking into how the effects change with individual characteristics. In Chapter 2, I collaborate with Shi Zuo to examine the effect of holding a government-issued professional certification or license on the probability of remaining employed during the COVID-19 pandemic for US workers. We utilize an exogenous negative labor market shock from the COVID-19 pandemic at the end of March 2020, and adopt the Difference-in-Difference (DID) framework. We estimate the DID model using both the traditional two-way fixed effects (TWFE) model and a newly developed doubly-robust DID model which requires less assumptions than the TWFE. We find that holding a government-issued professional certification or license significantly increases the probability of remaining employed during the pandemic, especially in the first few months. We also find that the effect is stronger among married individuals. However, workers with disabilities might not experience higher probability of staying employed even when they hold licenses. In Chapter 3, I explore persistent and transient technical inefficiency (TE), returns to scale (RTS) and technical change (TC) of US public banks for the period from 2010 to 2019. I model bank operation using a newly developed four component stochastic frontier model, construct RTS and TC estimates using estimated model parameters, and calculate bank TE by decomposing residuals. I find that persistent technical inefficiency is not negligible among US public banks. All banks experienced increasing returns to scale, and almost all banks experienced positive technical changes. Also, I investigate the correlation between technical inefficiency and bank financial risk, whether or nor a bank participates in foreign market, and whether or not a bank is incorporated in Delaware. The results indicate that financially healthier banks have lower persistent inefficiency. Banks that only participate in the US domestic market have higher persistent inefficiency. And banks incorporated in Delaware have higher persistent inefficiency.