Modeling of Gamma Index for Prediction of Pretreatment Quality Assurance in Stereotactic Body Radiation Therapy of the Liver.

Purpose: The purpose of this study was to develop a predictive model to evaluate pretreatment patient-specific quality assurance (QA) based on treatment planning parameters for stereotactic body radiation therapy (SBRT) for liver carcinoma. Materials and Methods: We retrospectively selected 180 cases of liver SBRT treated using the volumetric modulated arc therapy technique. Numerous parameters defining the plan complexity were calculated from the DICOM-RP (Radiotherapy Plan) file using an in-house program developed in MATLAB. Patient-specific QA was performed with global gamma evaluation criteria of 2%/2 mm and 3%/3 mm in a relative mode using the Octavius two-dimensional detector array. Various statistical tests and multivariate predictive models were evaluated. Results: The leaf speed (MILS) and planning target volume size showed the highest correlation with the gamma criteria of 2%/2 mm and 3%/3 mm (P < 0.05). Degree of modulation (DoM), MCSSPORT, leaf speed (MILS), and gantry speed (MIGS) were predictors of global gamma pass rate (GPR) for 2%/2 mm (G22), whereas DoM, MCSSPORT, leaf speed (MILS) and robust decision making were predictors of the global GPR criterion of 3%/3 mm (G33). The variance inflation factor values of all predictors were <2, indicating that the data were not associated with each other. For the G22 prediction, the sensitivity and specificity of the model were 75.0% and 75.0%, respectively, whereas, for G33 prediction, the sensitivity and specificity of the model were 74.9% and 85.7%%, respectively. Conclusions: The model was potentially beneficial as an easy alternative to pretreatment QA in predicting the uncertainty in plan deliverability at the planning stage and could help reduce resources in busy clinics.

Application of the "Plan-Do-Check-Action" plan in improving the pass rate of the "National Medical Licensing Examination".

BACKGROUND: The National Medical Licensing Examination (NMLE) is the only objective, standardized metric to evaluate whether a medical student possessing the professional knowledge and skills necessary to work as a physician. However, the overall pass rate of NMLE in our hospital in 2021 was much lower than that of Peking Union Medical College Hospital, which was required to be further improved. METHODS: To find the reasons for the unsatisfactory performance in 2021, the quality improvement team (QIT) organized regular face-to-face meetings for in-depth discussion and questionnaire, and analyzed the data by "Plato analysis" and "Brainstorming method". After finding out the reasons, the "Plan-Do-Check-Action" (PDCA) cycle was continued to identify and solve problems, which included the formulation and implementation of specific training plans by creating the "Gantt charts", the check of effects, and continuous improvements from 2021 to 2022. Detailed information about the performance of students in 2021 and 2022, and the attendance, assessment, evaluation and suggestions from our hospital were provided by the relevant departments, and the pass rate-associated data was collected online. RESULTS: After the PDCA plan, the pass rate of NMLE in our hospital increased by 10.89% from 80.15% in 2021 to 91.04% in 2022 (P = 0.0109), with the pass rate of skill examination from 95.59% in 2021 to 99.25% in 2022 (P = 0.0581) and theoretical examination from 84.5% in 2021 to 93.13% in 2022 (P = 0.027). Additionally, the mean scores of all examinees increased with the theoretical examination score increasing from 377.0 ± 98.76 in 2021 to 407.6 ± 71.94 in 2022 (P = 0.004). CONCLUSIONS: Our results showed a success application of the PDCA plan in our hospital which improved the pass rate of the NMLE in 2022, and the PDCA plan may provide a practical framework for future medical education and further improve the pass rate of NMLE in the next year.

Sparing the Hippocampus in Prophylactic Cranial Irradiation Using Three Different Linear Accelerators: A Comparative Study.

Hippocampus protection, as an organ at risk in brain radiotherapy, might protect patients' quality of life. Prophylactic cranial irradiation (PCI) has been used traditionally in small cell lung cancer (SCLC) patients as it increases survival. This study aimed to discover the contributing parameters for a successful PCI with simultaneous protection of the hippocampus by using three different treatment machines. For this purpose, treatment plans were generated for 45 SCLC patients using three half-arcs in three linear accelerators (LINACs; Elekta Infinity, Synergy, and Axesse; Elekta Ltd, Stockholm, Sweden) with different radiation field sizes and multileaf collimator (MLC) leaf thickness characteristics. The prescribed dose was 25 Gy in 10 fractions. Thresholds for the hippocampus were calculated based on the Radiation Therapy Oncology Group 0933 dose constraints. The planning and treatment system templates were common to all three LINACs. Plan evaluation was based on the dosimetric target coverage by the 95% isodose, the maximum dose of the plan, the conformity index (CI), the degree of plan modulation (MOD), and the patient-specific quality assurance (QA) pass rate. The mean target coverage was highest for Infinity (97.3%), followed by Axesse (96.6%) and Synergy (95.5%). The mean maximum dose was higher for Synergy (27.5 Gy), followed by Infinity (27.0 Gy) and Axesse (26.9 Gy). Axesse plans had the highest CI (0.93), followed by Infinity (0.91) and Synergy (0.88). Plan MOD was lower for Synergy (2.88) compared with Infinity (3.07) and Axesse (3.69). Finally, patient-specific QA was successful in all Infinity plans, in all but one Synergy plan, and in 17/45 Axesse plans, as was expected from the field size in that treatment unit. Based on overall performance, the most favorable combination of target coverage, hippocampus sparing, and plan deliverability was obtained with the LINAC, which has the largest field opening and thinnest MLC leaves.

Dosimetric effects of small field size, dose grid size, and variable split-arc methods on gamma pass rates in radiation therapy.

This study investigates the influence of calculation accuracy in peripheral low-dose regions on the gamma pass rate (GPR), utilizing the Acuros XB (AXB) algorithm and ArcCHECK™ measurement. The effects of varying small field sizes, dose grid sizes, and split-arc techniques on GPR were analyzed. Various small field sizes were employed. Thirty-two single-arc plans with dose grid sizes of 2 mm and 1 mm and prescribed doses of 2, 5, 10, and 20 Gy were calculated using the AXB algorithm. In total, 128 GPR plans were examined. These plans were categorized into three sub-fields (3SF), four sub-fields (4SF), and six sub-fields (6SF). The GPR results deteriorated with smaller target sizes and a 2 mm dose grid size in a single arc. A similar degradation in GPR was observed with smaller target sizes and a 1 mm dose grid size. However, the 1 mm dose grid size generally resulted in better GPR compared with the 2 mm dose grid size for the same target sizes. The GPR improved with finer split angles and a 2 mm dose grid size in the split-arc method. However, no statistically significant improvement was observed with finer split angles and a 1 mm dose grid size. This study demonstrates that coarser dose grid sizes result in lower GPRs in peripheral low-dose regions as calculated by AXB with ArcCHECK™ measurement. To enhance GPR, employing split-arc methods and finer dose grid sizes could be beneficial.

Empowering underprivileged students beyond financial aid: Insights from a scholarship program's monitoring and evaluation.

PURPOSE: This paper examines the impact of a scholarship program on underprivileged students, drawing on data from a two-year monitoring and evaluation (M&E) process. The report identifies both enablers and barriers to academic success among scholarship beneficiaries. METHODS: Data on program impact was collected through interviews with parents, teachers, and school records over two academic years. RESULTS: Financial aid emerged as a crucial enabler, with scholarships allowing students to focus on their studies by alleviating pressure around basic necessities. However, the research also revealed the importance of a holistic support system. Beyond tuition, the high cost of essential learning materials, including stationery, and subject-specific resources, can create a significant barrier. The study also highlighted the importance of student well-being. Health concerns, limited access to nutritious food, and even unaddressed mental health issues can all negatively impact attendance and focus. Furthermore, a gender gap emerged, with girls facing additional challenges related to social pressures to prioritize chores and the cost of menstrual hygiene products. CONCLUSION: This study highlights the importance of holistic scholarship programs that extend beyond tuition coverage. To maximize impact, policymakers and funders should prioritize initiatives that address the multifaceted needs of underprivileged students.

The North American Pharmacist Licensure Examination (NAPLEX) Pass Rate Conundrum.

As first-time pass rates on the North American Pharmacy Licensure Examination (NAPLEX) continue to decrease, pharmacy educators are left questioning the dynamics causing the decline and how to respond. Institutional and student factors both influence first-time NAPLEX pass rates. Pharmacy schools established before 2000, those housed within an academic medical center, and public rather than private schools have been associated with tendencies toward higher first-time NAPLEX pass rates. However, these factors alone do not sufficiently explain the issues surrounding first-time pass rates. Changes to the NAPLEX blueprint may also have influenced first-time pass rates. The number of existing pharmacy schools combined with decreasing numbers of applicants and influences from the COVID-19 pandemic should also be considered as potential causes of decreased first-time pass rates. In this commentary, factors associated with first-time NAPLEX pass rates are discussed along with some possible responses for the Academy to consider.

Factors associated with applicant performance on the Saudi Pharmacist Licensure Examination (SPLE).

Background: Limited data are available on factors that are associated with passing rates for the Saudi Pharmacist Licensure Examination (SPLE). The aim of this study is to investigate student characteristics and academic performance characteristics that may predict their success on SPLE. Methods: This was a single-institution retrospective cohort study, which included pharmacy graduates from 2019 to 2021. Demographic, academic, and SPLE data were collected for each graduate. Binary logistic regression was used to explore the association between potential predictors and first-time SPLE pass status. A stepwise regression was then performed to develop multiple logistic models. Results: A total of 494 graduates were included in the study. Females, PharmD graduates, and on-time graduation had higher odds of passing SPLE (P = 0.0065, P = 0.0003, and P < 0.0001, respectively). For each 0.5 increase in GPA, the odds of passing SPLE increase by 3.5 times (OR 3.53; 95 % CI, 2.83-4.42; P < 0.0001). Of the tests taken prior to university admission, the overall high school score, general aptitude test (GAT) score, and qualifying score were significantly associated with higher SPLE first-time pass rates. When multiple logistic regression analysis was performed, GPA and GAT scores were the only significant predictors for higher SPLE first-time pass rates (P < 0.0001 and P = 0.0002, respectively). Conclusion: The current research has shown that there is an association between higher SPLE first-time pass rates and several factors, most importantly the GPA and GAT score. Further research is needed, as it has the potential to inform the decision when reviewing pharmacy admission criteria.

Falling NAPLEX pass rates are cause for concern.

INTRODUCTION: First-time pass rates of the North American Pharmacist Licensure Examination (NAPLEX) have declined 7% from 2019 to 2022 with more than a third of schools experiencing a decline of ≥10%. COMMENTARY: The cause of the decline is likely multifactorial and extends beyond the impact of the COVID-19 pandemic. Changes to the NAPLEX blueprint in 2021, curricular revisions in response to the implementation of Accreditation Council for Pharmacy Education Standards 2016, and changes to prerequisite course requirements in response to declining enrollment must also be evaluated as potential causes. IMPLICATIONS: The academy must respond to this decline by scrutinizing admissions, curriculum, and assessment processes. We urge the National Association of Boards of Pharmacy to provide access to student-level data on NAPLEX performance and increase transparency in passing standard practices to inform this process.

A comparison of carcass characteristics, carcass cutting yields, and meat quality of barrows and gilts.

Objectives of this research were to compare carcass characteristics, carcass cutting yields, and meat quality for market barrows and market gilts. Commercially-sourced carcasses from 168 market barrows and 175 market gilts weighing an average of 107.44 ± 7.37 kg were selected from 17 different slaughter groups representing approximately 3,950 carcasses. Each group was sorted into percentiles based on hot carcass weight with an equal number of barrows and gilts selected from each quartile so that weight minimally confounded parameters of interest. Carcass lean yield was determined for carcasses following fabrication (i.e. dissection of lean, fat, and bone tissue components) and meat quality measurements were evaluated at the time of fabrication (24 to 72 h postmortem) and following 14-d of postmortem storage. Data were analyzed as a randomized complete block design with carcass serving as the experimental unit, sex (barrow or gilt), the three hot carcass weight quantiles (light [<104 kg]; average [104 to 110 kg]; heavy [>110 kg]), and the interaction between sex and hot carcass weight quantile serving as fixed effects, and producer nested within slaughter event serving as a random effect. Results from the study demonstrated that gilt carcasses were leaner (3 mm less backfat thickness; 3.5 cm2 greater loin muscle area, 1.52% greater merchandized-cut yield, and 2.92% greater dissected carcass lean yield; P < 0.01) than barrow carcasses, while loins from barrows were higher quality (0.43% more intramuscular fat and slightly less shear force; P < 0.01) than loins from gilts. While this study confirms the well-known biological principle that barrow carcasses have greater levels of fat deposition and lower levels of carcass leanness when compared with gilt carcasses, this study provides a much-needed quantification of these differences for the commercial industry that will undoubtedly be useful as new technologies emerge in upcoming years.

Plan quality and treatment efficiency assurance of two VMAT optimization for cervical cancer radiotherapy.

To investigate the difference of the fluence map optimization (FMO) and Stochastic platform optimization (SPO) algorithm in a newly-introduced treatment planning system (TPS). METHODS: 34 cervical cancer patients with definitive radiation were retrospectively analyzed. Each patient has four plans: FMO with fixed jaw plans (FMO-FJ) and no fixed jaw plans (FMO-NFJ); SPO with fixed jaw plans (SPO-FJ) and no fixed jaw plans (SPO-NFJ). Dosimetric parameters, Modulation Complexity Score (MCS), Gamma Pass Rate (GPR) and delivery time were analyzed among the four plans. RESULTS: For target coverage, SPO-FJ plans are the best ones (P ≤ 0.00). FMO plans are better than SPO-NFJ plans (P ≤ 0.00). For OARs sparing, SPO-FJ plans are better than FMO plans for mostly OARs (P ≤ 0.04). Additionally, SPO-FJ plans are better than SPO-NFJ plans (P ≤ 0.02), except for rectum V45Gy. Compared to SPO-NFJ plans, the FMO plans delivered less dose to bladder, rectum, colon V40Gy and pelvic bone V40Gy (P ≤ 0.04). Meanwhile, the SPO-NFJ plans showed superiority in MU, delivery time, MCS and GPR in all plans. In terms of delivery time and MCS, the SPO-FJ plans are better than FMO plans. FMO-FJ plans are better than FMO-NFJ plans in delivery efficiency. MCSs are strongly correlated with PCTV length, which are negatively with PCTV length (P ≤ 0.03). The delivery time and MUs of the four plans are strongly correlated (P ≤ 0.02). Comparing plans with fixed or no fixed jaw in two algorithms, no difference was found in FMO plans in target coverage and minor difference in Kidney_L Dmean, Mu and delivery time between PCTV width≤15.5 cm group and >15.5 cm group. For SPO plans, SPO-FJ plans showed more superiority in target coverage and OARs sparing than the SPO-NFJ plans in the two groups. CONCLUSIONS: SPO-FJ plans showed superiority in target coverage and OARs sparing, as well as higher delivery efficiency in the four plans.

[Study on the sensitivity of a volumetric modulated arc therapy plan verification equipment on multi-leaf collimator opening and closing errors and its gamma pass rate limit].

To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.

Association Between Accreditation Era, North American Pharmacist Licensure Examination Testing Changes, and First-Time Pass Rates.

Objective. To estimate whether first-time pass rates on the North American Pharmacist Licensure Examination (NAPLEX) have been influenced by the number of pharmacy programs founded since 2000, the programs' accreditation era, and the changes to the blueprint as well as changes to the testing conditions and passing standards implemented by the National Association of Boards of Pharmacy (NABP) beginning in 2015.Methods. This was a retrospective, observational cohort study using publicly published data. The number of programs and pass rates were collected from 2008 to 2020. Programs reporting pass rates from 2016 to 2020 were eligible. Accreditation era was defined as programs accredited before or after 2000. Pass rates were categorized into NAPLEX tests administered before or after 2015. Statistical analyses were conducted for comparisons.Results. Pass rates were initially found to decline as the number of programs rose. First-time pass rates of programs accredited before 2000 were higher than pass rates of programs accredited after 2000 every year after 2011. Only 40% of the programs accredited after 2000 exceeded the national average between 2016-2020. Blueprint changes implemented in 2015 and the changes to testing conditions plus passing standards implemented in 2016 had a greater effect on pass rates than the number of programs or applicants.Conclusion. Programs accredited after 2000 generally had lower first-time NAPLEX pass rates. Even so, blueprint changes and changes to the testing conditions plus passing standards instituted by the NABP were more important predictors of the decline of first-time NAPLEX pass rates. Stakeholders should collaborate and embrace best practices for assessing practice-ready competency for licensure.

Prediction of radiomics-based machine learning in dose verification of intensity-modulated pelvic radiotherapy / 中华放射医学与防护杂志

Objective:Based on radiomics characteristics, different machine learning classification models are constructed to predict the gamma pass rate of dose verification in intensity-modulated radiotherapy for pelvic tumors, and to explore the best prediction model.Methods:The results of three-dimensional dose verification based on phantom measurement were retrospectively analyzed in 196 patients with pelvic tumor intensity-modulated radiotherapy plans. The gamma pass rate standard was 3%/2 mm and 10% dose threshold. Prediction models were constructed by extracting radiomic features based on dose documentation. Four machine learning algorithms, random forest, support vector machine, adaptive boosting, and gradient boosting decision tree were used to calculate the AUC value, sensitivity, and specificity respectively. The classification performance of the four prediction models was evaluated.Results:The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision tree models were 0.93, 0.85, 0.93, 0.96, 0.38, 0.69, 0.46, and 0.46, respectively. The AUC values were 0.81 and 0.82 for the random forest and adaptive boosting models, respectively, and 0.87 for the support vector machine and gradient boosting decision tree models.Conclusions:Machine learning method based on radiomics can be used to construct a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity-modulated radiotherapy. The classification performance of the support vector machine model and gradient boosting decision tree model is better than that of the random forest model and adaptive boosting model.

Radiomics-based prediction of gamma pass rates for different intensity-modulated radiation therapy techniques for pelvic tumors / 中华放射医学与防护杂志

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

Applying an ArcCHECK detector to the dose verification for ultra-long target volumes of cervical cancer / 中华放射医学与防护杂志

Objective:To explore the feasibility of applying an ArcCHECK detector to the dose verification for ultra-long target volumes of cervical cancer.Methods:This study retrospectively selected patients suffering from cervical cancer with ultra-long target volumes (lengths: ≥ 26 cm; 50 cases; the ultra-long target volume group) and conventional target volumes (lengths: < 26 cm; 50 cases; the conventional target volume group). Subsequently, this study designed treatment plans using the Volumetric Modulated Arc Therapy (VMAT) technique and then collected and verified doses using an ArcCHECK detector. The dose detection for the conventional target volume group was performed at the central point of the detector (marked by iso and Short-0 cm). Then, the detector was moved for 5 cm along the bed exit direction (marked by iso 1), followed by the dose verification of the ultra-long target volume group (marked by Long-5 cm) and conventional target volume group (marked by Short-5 cm). The geometric parameters (the length and volume of a target volume), mechanical parameters (machine hop count and the duration of irradiation), and gamma pass rates (GPRs) under different detection conditions of each group were analyzed.Results:The target lengths, target volumes, machine hop counts, and irradiation durations of the ultra-long target group were higher than those of the conventional target group ( t = 2.61-18.56, P < 0.05). For the conventional target group, the GPRs at iso 1 were significantly lower than those at iso ( t = 2.14-8.17, P < 0.05). Meanwhile, the GPRs at iso 1 of the ultra-long target volume group were significantly lower than those of the conventional target volume group ( t = -4.70 to -2.73, P < 0.01). The GPRs of each group met clinical requirements for criteria of both 3%/3 mm and 3%/2 mm. Conclusions:The deviation of the positioning center and the length of the target volume serve as primary factors affecting the dose verification result of cervical cancer. For ultra-long target volumes, dose verification can be performed by moving the positioning center, thus ensuring treatment accuracy for cervical cancer patients.

Gamma pass rate classification prediction and interpretation based on SHAP value feature selection / 中华放射肿瘤学杂志

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.

Effect of carbon fiber postural fixation plate on radiotherapy dose of cervical cancer / 重庆医学

Objective To study the effect of carbon fiber postural fixation plate on radiotherapy dose of cervical cancer.Methods The carbon fiber postural fixation plate model was created in the RayStation plan-ning system,and the difference of attenuation coefficient between the model plate in the planning system and the real plate in the actual measurement was compared to verify the accuracy of the position fixing plate mod-el.A total of 10 patients with cervical cancer were selected,and the plate-free plan was designed on the CT im-age without the fixed plate model,and the dose was calculated.After the plate-free plan was completed,the plan was transplanted to the CT image with the fixed plate model to obtain the plate plan,and the dose was calculated.The dosimetric differences of target volume(PTV)and organ at risk(OAR)between the plate-free plan and the plate plan were compared.Two ArcCHECK verification phantoms were established in the RayStation planning system,which were the ArcCHECK verification phantom with the postural fixation plate model and the ArcCHECK verification phantom without the postural fixation plate model.The 10 cervical cancer plans were transplanted into two verification phantoms for dose calculation.Under the Xinhua accelera-tor,ArcCHECK was placed on the postural fixation plate to perform the validation plan,and the effect of the postural fixation plate model in the planning system on the gamma passing rate of the verification plan was compared.Results For the accuracy of the position fixation plate model was created in the planning system:the deviation(d)of the attenuation coefficient obtained in the planning system and the actual measurement is less than 0.3％.For the cervical cancer plan:compared with the plate without plan,the dose of PTV and OAR in the plate with plan was significantly lower.The average dose of PTV was about 1％lower,and the degree of OAR was different,ranging within 3％.For cervical cancer plan verification:the gamma pass rate of the plate model verification plan was significantly higher than that of the platefree plan model verification plan,and the pass rates of 3 mm/3％and 2 mm/2％were increased by 0.69％and 1.50％,respectively.Conclusion The carbon fiber postural fixation plate has a certain effect on the radiotherapy dose of cervical cancer patients.In order to ensure the accuracy of the target dose,it is recommended to add the postural fixation plate model in the plan design.

Study on the sensitivity of a volumetric modulated arc therapy plan verification equipment on multi-leaf collimator opening and closing errors and its gamma pass rate limit / 生物医学工程学杂志

To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.

Comparison of Saudi Pharmacist Licensure Examination (SPLE) Pass Rates by Institution and Applicant Characteristics.

In 2019, the Saudi Pharmacist Licensure Examination (SPLE) was first administered to all pharmacy graduates and served as one of the prerequisites for obtaining a pharmacist license. The objective of this study was to evaluate whether institution and applicant characteristics are associated with first-time SPLE success. Passing status for 2284 SPLE first-time applicants was obtained from online public data for the years 2019 and 2020. The data included applicant sex, institution type (public vs. private), and college establishment year (2006 or earlier vs. after 2006). Overall, the SPLE first-time pass rate in 2020 was significantly higher than in 2019 (98.0 vs. 95.9%; p = 0.0062). Applicants from pharmacy colleges established in or before 2006 had a higher SPLE first-time pass rate, compared to those from pharmacy colleges established after 2006 (98.2 vs. 95.2%; p < 0.0001). The pass rate for male applicants was lower compared to female applicants (95.8 vs. 97.5%; p = 0.0221). The results of logistic regression showed that exam year (2020 vs. 2019), applicant sex (female vs. male), and pharmacy college establishment year (≤2006 vs. >2006) were statistically significant predictors. Further studies are needed in the upcoming years when more cumulative data are available.

DR-only Carbon-ion radiotherapy treatment planning via deep learning.

PURPOSE: To evaluate the feasibility of patient-specific digital radiography (DR)-only treatment planning for carbon ion radiotherapy in anthropomorphic thorax-and-abdomen phantom and head-and-neck patients. METHODS: The study was conducted on the anthropomorphic phantom and head-and-neck patients. We collected computed tomography (CT) and DR images of the phantom and cone beam CT (CBCT) and DR images of the patients, respectively. Two different deep neural networks were established to correlate the relationships between DR and digitally reconstructed radiograph (DRR) images, as well as DRR and CT images. The similarity between CT and predicted CT images was evaluated by computing the mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. Dose calculations on the predicted CT images were compared against the true CT-based dose distributions for carbon-ion radiotherapy treatment planning with intensity-modulated pencil-beam spot scanning. Relative dose differences in the target volumes and organ-at-risks were computed and three-dimensional gamma analyses (3 mm, 3%) were performed. RESULTS: The average MAE, RMSE, PSNR and SSIM of the framework were 0.007, 0.144, 37.496 and 0.973, respectively. The average relative dose differences between the predicted CT- and CT-based dose distributions at the same carbon-ion irradiation settings for the phantom and the patients were <2% and ≤4%, respectively. The average gamma pass-rates were >98% for the predicted CT-based versus CT-based carbon ion plans of the phantom and the patients. CONCLUSION: We have demonstrated the feasibility of a patient-specific DR-only treatment planning workflow for heavy ion radiotherapy by using deep learning approach.