Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study

Abstract

Objectives

To explore the efficacy of a novel teaching model based on the Monaco planning system in radiation oncology education.

Methods

Forty clinical undergraduate medical interns were randomized and equally divided into a control group and an experimental group. The experimental group underwent the Monaco planning system-based teaching model, while the control group received conventional clinical teaching activities. Upon completion of the internship, both groups were simultaneously assessed mini clinical evaluation exercise (Mini-CEX), direct observation of procedural skills (DOPS), and end-of-rotation theoretical and clinical skills assessment. A satisfaction survey was also conducted. The assessment scores were compared to evaluate the teaching effectiveness.

Results

The experimental group demonstrated significantly better scores than the control group according to the 7 core evaluation indicators of Mini-CEX, DOPS assessment results, final departmental exam performance, and satisfaction scores, with statistically significant differences (P < .05).

Conclusion

The Monaco planning system-based teaching model significantly enhances clinical competency and learner satisfaction in radiation oncology education compared to traditional methods.

Keywords

mini-clinical exercise evaluation direct observation of procedural skills Monaco planning system radiation oncology teaching model

Introduction

In the context of the swiftly advancing field of radiation therapy, the discipline of radiation oncology emerges as a critical component of undergraduate oncology internship education. Given its highly specialized nature, intricate knowledge framework, and extensive scope of expertise encompassing clinical oncology, radiophysics, radiobiology, medical imaging, as well as systemic and regional anatomy, the execution of clinical undergraduate practical teaching presents a considerable challenge.¹ The conventional approach of teaching, such as lecture-based learning (LBL) and case-based learning (CBL), places the initiative predominantly with the instructor. The instructor selects a topic and then passes on knowledge centered around this theme, primarily utilizing PowerPoint (PPT) presentations. This method focuses more on theoretical learning than practical application, often overlooking the critical importance of hands-on experience.^2,3 Given its highly practical nature, radiation oncology faces significant challenges in achieving effective clinical internship outcomes under this traditional teaching paradigm due to its monotony, lack of engagement, and absence of encouragement for creative thinking.⁴ Furthermore, China experiences a notable scarcity of systematic and efficient internship teaching methodologies in radiation oncology, highlighting the pressing need for the development of an accessible, engaging, and innovative educational model in this field.

As artificial intelligence (AI) continues to weave its way into various application scenarios, its judicious integration has the potential to substantially enhance medical education.⁵ The Monaco planning system stands at the forefront of this integration, serving as an advanced oncology radiotherapy planning tool that harnesses the precision of the Monte Carlo dose algorithm and the finesse of biological optimization. This system is good at supporting a comprehensive range of contemporary radiotherapy techniques, including 3-dimensional conformal radiation therapy (3D-CRT), intensity-modulated radiation therapy, volumetric modulated arc therapy, and stereotactic body radiation therapy plans. The simulation teaching method, utilizing the Monaco planning system, effectively recreates a realistic radiotherapy process, encompassing functions like automatic image fusion, automatic delineation of target volumes, and the identification of organs at risk. The teaching model based on the Monaco planning system offers the following advantages. Firstly, the system's capacity to store extensive radiation oncology case data enables interns to access diverse cancer case studies for repeated review, allowing students to engage in self-directed learning during their free time. Secondly, it integrates patient computed tomography (CT) or magnetic resonance imaging (MRI) images into 3D models, enabling trainees to observe anatomical structures from multiple planes and angles—such as coronal, sagittal, and transverse sections—providing a more comprehensive and accurate understanding of spatial anatomical relationships compared to traditional 2-dimensional (2D) imaging. Furthermore, its automated target delineation and radiation dose optimization functionalities enhance teaching efficiency and quality while improving students’ learning experience and practical skills.^6-8 By leveraging advanced features such as 3D dose clouds, real-time multiplanar reconstruction, and dynamic dose painting, the Monaco planning system transforms traditional teaching into interactive 3D visualization training. Therefore, the Monaco planning system's superior performance in radiotherapy demonstrates its potential for developing an immersive radiotherapy internship teaching mode, which based on these advantages, this study hypothesizes that internship teaching models utilizing the Monaco planning system outperform traditional pedagogical approaches.

The mini-clinical evaluation exercise (Mini-CEX) and the direct observation of procedural skills (DOPS) serve as multifaceted assessment scales that fulfill both educational and evaluative roles. These tools are increasingly recognized as integral components of formative assessment systems within medical education. Their widespread adoption is a testament to their effectiveness in shaping the development of medical professionals.^9-11 Research has indicated that both Mini-CEX and DOPS are valuable tools for formative assessment in clinical internship education, including in specialized fields like radiation oncology, due to their clear benefits.^12,13 In this study, we explored the effectiveness of an innovative teaching model based on the Monaco planning system in radiation therapy internship training and evaluated its teaching outcomes using Mini-CEX combined with DOPS. The study aimed to explore a new teaching approach to enhance practical training in radiation therapy, thereby improving students’ understanding and skills in this field.

Methods

The reporting of this study conforms to the CONSORT statement¹⁴ (Supplemental Material 1).

General Information

Between January and March 2023, 40 clinical undergraduate internship students were recruited from the Radiotherapy Center of the Third Affiliated Hospital of Chongqing Medical University (Chongqing, China) to participate in this study. Inclusion criteria included the following: (1) Full-time undergraduate interns in clinical medicine; (2) Completion of 4 years of basic medical and clinical medicine coursework and entry into the clinical internship phase; (3) Voluntary participation in this study; (4) No prior internship experience in radiation oncology; (5) Commitment to full participation in the study throughout the internship period. Exclusion criteria included the following: (1) Prior training or clinical work experience in radiation oncology (eg, individuals returning to school after clinical work experience); (2) Anticipated inability to complete required follow-ups or evaluations (eg, withdrawing from the internship midway or taking extended leave); (3) Severe physical or psychological conditions that may affect internship performance or evaluation outcomes. This study confirmed through baseline surveys that the experimental group had not received systematic lectures, workshops, or targeted training related to the research topic (including but not limited to theoretical courses or pedagogical instruction) prior to the intervention. Participants in both groups shared identical professional backgrounds and 4-year undergraduate education experiences. All 40 interns met the inclusion criteria and were randomly assigned to either the experimental group, which utilized the Monaco planning system-based teaching model, or the control group, which adhered to the traditional LBL and CBL teaching methods. Each group comprised 20 students, and all interns spent 4 weeks practicing in the oncology radiotherapy center.

Study Design

As shown in Figure 1, the experimental group received radiotherapy practice teaching facilitated by the Monaco planning system (Elekta, version 5.11.03). The comprehensive details are outlined as follows: Upon entering the radiotherapy center for practical experience, students are guided by an instructor through the following steps: (1) They embark on an observation of the radiotherapy mould-making process； (2) Subsequently, they visit the radiotherapy simulation positioning procedure, gaining insight into patient positioning for treatment； (3) Under the guidance of the instructor, the students utilized the Monaco planning system to outline the OAR and the radiotherapy target area, and subsequently compared the resulting image with the target area outlined by the instructor to identify any discrepancies; (4) They then observe the physicist's target area plan design and doctors’ radiotherapy plan verification process, deepening their understanding of the technical aspects; (5) Finally, students actively participate in the implementation of a patient's initial radiotherapy plan, gaining hands-on experience in the delivery of the treatment plan.

Figure 1.

Flowchart of this research.

Under the guidance of their practice teachers, students had the opportunity to engage hands-on with various radiotherapy processes using the Monaco planning system. This included image fusion, target delineation, the identification of organs at risk, and radiotherapy plan design. The Monaco planning system converts CT and MRI tomographic images into 3D, volumetric, and dynamic educational models. With a simple mouse click, students can study and observe patient imaging data from multiple anatomical planes—including coronal, sagittal, and axial views—enabling a comprehensive understanding of the critical steps in radiotherapy workflows (Figure 2).

Figure 2.

The Monaco planning system reconstructs CT scans into three-dimensional images. (A) CT images in axial, coronal, and sagittal planes; (B) 3D-reconstructed images viewable from multiple angles using the Monaco planning system.

For the control group, a conventional internship teaching approach was employed, encompassing LBL and CBL. The practice teachers utilized LBL and CBL to present the interns with the fundamental steps of tumor radiotherapy through PowerPoint presentations. This encompassed patient preparation before radiotherapy, mold making, target area delineation, plan verification, and radiotherapy implementation. In this teaching model, the practice teachers primarily relied on lecture methods to explain the radiotherapy process, guiding students to apply basic theories in analyzing and solving problems, thereby fulfilling the teaching objectives.

After the 4-week internship, both the control group and the experimental group underwent a formative evaluation encompassing Mini-CEX and DOPS assessments, as well as a summative assessment of theoretical knowledge and practical skills. Additionally, satisfaction surveys were conducted among the participants. Firstly, the specific procedure for the Mini-CEX assessment is as follows: With informed patient consent, conduct the initial consultation for newly admitted patients with malignant tumors (eg, stomach cancer, esophageal cancer, colorectal cancer, and lung cancer) on the same day. The Mini-CEX checklist evaluates 7 aspects: consultation skills, physical examination, specialized knowledge, clinical diagnosis, communication skills, humanistic care, and comprehensive ability. The scoring criteria are as follows: 1-3: Unsatisfactory, 4-6: Satisfactory, 7-9: Excellent. Secondly, the DOPS assessment method is as follows: It assesses the mastery of interns in the skills related to radiation oncology. It includes 11 items: knowledge reserve level, obtaining informed consent, preparation before operation, appropriate pain relief and sedation, clinical operations and technical capabilities, aseptic technique, seeking help in an appropriate time, related disposal after operation, communication skills, passionate care, and comprehensive performance. Each item is worth a maximum of 9 points, with 1-3 points indicating needs improvement, 4-6 points indicating satisfactory, and 7-9 points indicating excellent. The Mini-CEX and DOPS checklists are provided in Supplemental Material 2. Thirdly, at the conclusion of the internship, all interns are required to undergo a comprehensive after-department examination, encompassing both theoretical and skill assessments. The final rotation assessment content was strictly aligned with the pre-defined internship syllabus: the theoretical examination consisted of 25 multiple-choice questions randomly selected from the question bank (4 points per question, totaling 100 points), while the skill assessment evaluated trainees’ basic operational competencies in tumor radiotherapy using a standardized scoring rubric (total score: 100 points). Additionally, each internship student was expected to complete the internship satisfaction questionnaire, which was developed by the department and utilized a hundred-mark scoring system. A higher score indicated a higher level of satisfaction. To minimize assessment bias, evaluators were blinded to group allocation throughout the study period.

Statistical Methods

The statistical analysis was conducted using SPSS 27.0 software. The data were presented in the format of mean ± standard deviation (M ± SD), and an independent sample t-test was utilized to compare the differences between groups. The counting data were represented as case numbers or rates, and the comparison between groups was made using the X² test. Differences were deemed statistically significant if the P-value was less than .05.

Results

Comparison of Examination Results between 2 Distinct Groups by Utilizing the Mini-CEX and DOPS Evaluations

The baseline demographic characteristics, including gender and age, were comparable between the 2 groups, showing no significant differences (P > .05, Table 1). Upon completion of the 4-week internship period, both groups were administered a series of assessment tests. In the Mini-CEX, the experimental group surpassed the control group in achieving higher scores across 7 distinct items: consultation skills, physical examination, specialized knowledge, clinical diagnosis, communication skills, humanistic care, and comprehensive ability. Notably, statistical differences were observed between each of these items, indicating a significant advantage for the experimental group (P < .05, Table 2). Similarly, in the DOPS, the experimental group demonstrated superior performance over the control group in 11 key items, including but not limited to knowledge reserve level, obtaining informed consent, preparation before operation, appropriate pain relief and sedation, with the observed differences being statistically significant (P < .05, Table 3).

Table 1.

General Information of 2 Groups of Interns.

Group	Gender		Average age (M ± SD)
Group	Male	Female	Average age (M ± SD)
Experimental group (N = 20)	11	9	23.85 ± 0.75
Control group (N = 20)	10	10	23.65 ± 0.71

Abbreviations: N, number; M, mean; SD, standard deviation.

Table 2.

Comparison of Mini-CEX Scores between 2 Groups of Internship Students upon Completion of the Internship (scores, M ± SD).

Items	Scores (M ± SD)
Items	Control group (N = 20)	Experimental group (N = 20)	t-value	P-value
Consultation skills	5.25 ± 0.72	6.65 ± 0.74	6.05	<.001
Physical examination	5.30 ± 0.86	6.05 ± 0.94	2.62	.013
Specialized knowledge	5.65 ± 0.67	6.60 ± 1.13	3.38	.002
Clinical diagnosis	5.25 ± 1.55	6.30 ± 1.41	2.23	.031
Communication skills	5.60 ± 1.09	6.75 ± 0.96	3.52	.001
humanistic care	5.40 ± 1.23	6.60 ± 1.46	2.80	.008
Comprehensive ability	6.05 ± 0.88	6.95 ± 0.82	3.32	.002

Abbreviations: N, number; M, mean; SD, standard deviation.

Table 3.

Comparison of DOPS Scores between 2 Groups of Internship Students upon Completion of the Internship (Scores， M ± SD).

Items	Scores (M ± SD)
Items	Control group (N = 20）	Experimental group (N = 20）	t-value	P-value
Knowledge reserve level	5.50 ± 0.60	6.65 ± 0.67	5.68	<.001
Obtaining informed consent	6.80 ± 1.00	7.90 ± 0.78	3.85	<.001
Preparation before operation	6.55 ± 1.23	7.40 ± 1.14	2.26	.031
Appropriate pain relief and sedation	6.30 ± 1.03	7.15 ± 1.18	2.42	.021
Clinical operations and technical capabilities	5.80 ± 1.10	6.60 ± 1.14	2.35	.024
Aseptic technique	6.30 ± 1.17	7.15 ± 1.13	2.32	.025
Seek help in an appropriate time	5.50 ± 1.23	6.60 ± 0.99	3.11	.004
Related disposal after operation	5.95 ± 0.94	7.05 ± 1.05	3.48	.001
Communication skills	6.40 ± 1.04	7.60 ± 0.99	3.71	<.001
Humanistic care	6.50 ± 0.88	7.40 ± 0.99	3.01	.005
Comprehensive performance	6.35 ± 0.93	7.25 ± 0.91	3.08	.004
Total points	74.13 ± 3.41	78.75 ± 3.35	4.31	<0.001

Abbreviations: N: number; M: mean; SD: standard deviation.

Comparison of After-Department Examination Scores and Satisfaction Levels between the 2 Groups

Upon completion of the 4-week internship program, both groups of interns underwent comprehensive evaluations comprising a radiotherapy theory examination, skill examination, and satisfaction survey. The findings revealed that the experimental group outperformed the control group in all metrics, including theory examination scores (83.9 ± 1.37 vs. 77.40 ± 1.90, t = 12.38, P < .001), skill examination scores (80.00 ± 1.72 vs. 73.00 ± 3.00, t = 9.79, P < .001), and satisfaction scores (93.65 ± 1.46 vs. 86.60 ± 2.52, t = 10.81, P < .001), with these differences being statistically significant (Table 4).

Table 4.

Comparison of end-of-rotation Theoretical and Clinical Skills Assessment Scores between the 2 Groups (Scores, M ± SD).

Items	Control group （N = 20）	Experimental group (N = 20）	t-value	P-value
Theory examination scores	77.40 ± 1.90	83.90 ± 1.37	12.38	<.001
Skill examination scores	73.00 ± 3.00	80.00 ± 1.72	9.79	<.001
Satisfaction scores	86.60 ± 2.52	93.65 ± 1.46	10.81	<.001

Abbreviations: N, number; M, mean; SD, standard deviation.

Discussion

Radiation oncology serves as a pivotal aspect of internship teaching for clinical oncology undergraduate students, with approximately 60% of cancer patients undergoing radiotherapy or palliative radiotherapy throughout their course of disease.¹⁵ Radiation oncology stands out as a comprehensive and specialized discipline amidst other medical fields, with its technology advancing rapidly. Although previous studies have indicated that both LBL and CBL possess certain advantages when it comes to radiation oncology education,¹⁶ these traditional teaching models often require teachers to devote considerable time to face-to-face lectures. However, this approach tends to render students’ passive learners in practice, often unable to exercise their subjective initiative in the learning process. To proficiently grasp radiation therapy, it is imperative to acquaint oneself with a diverse array of knowledge encompassing clinical oncology, radiophysics, radiobiology, medical imaging, as well as systemic and local anatomy.¹ For undergraduate internship students newly entering the clinical internship, mastering the intricate process of tumor radiotherapy can be a challenging task. Hence, it is imperative for teachers to actively delve into enhanced teaching models in radiation oncology, enabling them to swiftly grasp the essence of this discipline within the limited time of their internship.

AI empowers various industries with its efficiency and precision. In clinical radiotherapy practice, AI-powered platforms enable radiotherapy planning systems to autonomously perform delineation of normal tissues and organs, thereby enhancing the efficiency and accuracy of cancer radiotherapy.^17,18 Meanwhile, researchers are actively exploring AI applications in radiation oncology education. Studies have demonstrated that compared to traditional teaching methods, AI-integrated approaches provide students with expanded educational resources, allowing repeated virtual target delineation practice, and facilitating participation in radiotherapy plan development. These innovations significantly improve pedagogical outcomes in radiation oncology training.¹⁸

The Treatment Planning System is a radiotherapy planning platform developed collaboratively by radiation physicists and computer specialists.¹⁹ Utilizing CT imaging as its foundation, it integrates computational power, intelligent target delineation, and radiotherapy plan generation to ensure precision and efficiency throughout cancer radiotherapy. Clinically prevalent systems include Varian Eclipse, Pinnacle, and Monaco planning system.^20,21 The Monaco system employs Monte Carlo algorithms to simulate particle trajectories in human tissues and calculate dose distribution. It incorporates radiobiological optimization models for direct biological effect integration, supports multimodal image fusion for targeting accuracy, and combines computer science with clinical oncology to deliver tumor-specific solutions. Statistical analysis and quality control tools ensure therapeutic safety.^6,22-26 The Monaco planning system reconstructs 3D images from 2D medical data, converting conventional imaging and irradiation field designs into interactive 3D teaching maps. Students can dynamically visualize radiotherapy targets across coronal, sagittal, and axial planes to assess spatial relationships between tumors and critical structures, enhancing teaching interactivity. Collaborative exchanges with instructors and peers further consolidate learning experiences.^27,28-30,31 Moreover, the Monaco planning system enables students to rapidly grasp multiple key aspects of cancer radiotherapy. Its rich and engaging content accelerates learning through simulated clinical cases, enhancing students’ ability to recognize anatomical structures and delineate targets, and deepening their understanding of the entire radiotherapy process. Since acquiring the Monaco planning system in 2019, our radiation oncology center has attained a high level of proficiency in utilizing the platform, and significantly enhanced the efficiency of our radiotherapy procedures.

Formative evaluation serves as a pivotal tool in the clinical internship teaching process, emphasizing the process rather than the result. It enables teachers to gain insights into students’ learning progress, allowing for timely adjustments in teaching strategies and fostering students’ continuous improvement.³² It has been reported that the integration of the Mini-CEX and DOPS evaluation systems can effectively complement each other's strengths, thereby enhancing clinical teaching outcomes. The DOPS can address the operational skill limitations of the Mini-CEX, while the Mini-CEX offers a more comprehensive assessment of the operator's qualities, including humanistic aspects, that are not captured by the DOPS.¹² Liu Hongzhi et al successfully implemented a clinical teaching evaluation approach in oncology that combined the Mini-CEX with DOPS. Their findings revealed that this integrated application offered the advantages of simplicity, comprehensiveness, direct observation, and an overall superior clinical teaching effect.³³

This study incorporated the Monaco planning system into clinical oncology internships, using Mini-CEX and DOPS for formative evaluation. The experimental group outperformed the traditional group across all key assessment domains. Firstly, guided by instructors, the experimental group utilized the Monaco planning system to perform clinical tasks (eg, target delineation, plan verification) throughout the radiotherapy workflow. This immersive approach enhanced understanding of treatment processes and allowed instructors to reallocate saved time to communication skills training. This dynamic engagement empowered students to seek timely consultation during radiotherapy procedures, thus developing superior consultation abilities compared to the traditional group. Secondly, during their internship, the experimental group utilized the Monaco planning system to holistically engage in the radiotherapy process and study medical imaging. Access to its rich patient case library enhanced their understanding of physical examination-imaging correlations. Through direct patient interaction and active participation in pivotal stages—including target delineation, radiotherapy verification, and treatment execution—this immersive experience improved their knowledge reserve level, specialized knowledge acquisition, physical examination skills, and clinical diagnostic abilities. Consequently, they obtained informed consent more smoothly, demonstrated superior preprocedural preparation and postprocedural management, and exhibited enhanced clinical operations and technical capabilities. Thirdly, the use of the Monaco planning system cultivated heightened responsibility and compassion in the experimental group beyond traditional methods. This fostered a robust patient-centered philosophy and profound understanding of patient suffering, enabling the provision of appropriate pain relief and sedation. Furthermore, their enhanced awareness of tumor patients’ immunocompromised status led to rigorous emphasis on aseptic technique during radiotherapy. Collectively, these competencies resulted in superior humanistic care literacy and elevated comprehensive ability relative to the control group. Moreover, the incorporation of the Monaco planning system-based teaching mode by the experimental group showed vivid and authentic learning experience. It not only empowered interns to comprehensively grasp the pertinent theoretical and practical aspects of radiotherapy but also ignited their passion for the field. Consequently, the experimental group surpassed the control group in both theory examination and skill examination, earning not just higher scores but also securing a higher satisfaction rating from interns. Thus, the Monaco planning system-centered teaching approach proves to be an effective means of enhancing interns’ knowledge mastery and elevating overall teaching satisfaction.

Consequently, this study effectively demonstrated the superior benefits of the Monaco planning system-integrated radiation oncology teaching model for intern training, using Mini-CEX and DOPS assessments for preliminary validation. The present study represents a novel exploration and endeavor aimed at enhancing the quality of radiation oncology teaching. This innovative teaching mode offers interns vivid and interesting content, effectively breaking away from traditional indoctrination-based methods. By actively engaging students in the teaching process, it better caters to the specific needs of radiation oncology teaching, thereby improving its overall effectiveness and contributing to the cultivation of proficient and passionate young doctors. Nevertheless, despite the progress made, there are still certain limitations to be addressed. Firstly, due to the limited number of undergraduates assigned to our department for radiation oncology internships during the study period (total 40 students), data saturation was not achieved in this study. While our data provided valuable insights, the lack of saturation may limit the completeness of the findings. In future research, we will expand the sample size to further investigate and confirm the conclusions of this study. Secondly, no existing studies have incorporated the Monaco planning system into radiation oncology teaching. The conclusions of this study need further validation in other radiation oncology centers. Addressing these challenges will be a priority for future improvements.

Conclusion

Within our institutional context, the Monaco planning system-based teaching model demonstrated significant improvements in radiation oncology education compared to local traditional teaching methods. The integrated Mini-CEX/DOPS assessment framework proved effective in enhancing clinical skill development, increasing student engagement, and elevating clinical teaching quality. While our department has validated this model's feasibility for radiation oncology internships, broader implementation requires further validation across diverse educational settings.

Supplemental Material

sj-doc-1-mde-10.1177_23821205251372109 - Supplemental material for Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study

Supplemental material, sj-doc-1-mde-10.1177_23821205251372109 for Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study by Hongbo Zou, Qichao Xie and Bijing Mao in Journal of Medical Education and Curricular Development

Supplemental Material

sj-pdf-2-mde-10.1177_23821205251372109 - Supplemental material for Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study

Supplemental material, sj-pdf-2-mde-10.1177_23821205251372109 for Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study by Hongbo Zou, Qichao Xie and Bijing Mao in Journal of Medical Education and Curricular Development

Supplemental Material

sj-pdf-3-mde-10.1177_23821205251372109 - Supplemental material for Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study

Supplemental material, sj-pdf-3-mde-10.1177_23821205251372109 for Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study by Hongbo Zou, Qichao Xie and Bijing Mao in Journal of Medical Education and Curricular Development

Supplemental Material

sj-pdf-4-mde-10.1177_23821205251372109 - Supplemental material for Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study

Supplemental material, sj-pdf-4-mde-10.1177_23821205251372109 for Practice and Evaluation of Competence in the Application of Monaco Planning System in Undergraduate Radiotherapy Internship Teaching: A Randomized Controlled Study by Hongbo Zou, Qichao Xie and Bijing Mao in Journal of Medical Education and Curricular Development

Footnotes

Acknowledgments

The authors wish to thank the Chongqing Medical University for providing student's data.

ORCID iDs

Hongbo Zou

Qichao Xie

Bijing Mao

Ethics Approval and Consent to Participate

Ethics Committee of the Third Affiliated Hospital of Chongqing Medical University has confirmed that no ethical approval is required (Supplemental Material 3). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardians

Authors’ Contributions

ZHB and MBJ carried out the studies and participated in collecting data. MBJ drafted the manuscript. ZHB performed the statistical analysis. ZHB, MBJ, and XQC participated in the acquisition, analysis, or interpretation of data. All authors read and approved the final manuscript.

Funding

This study was funded by the institutional fund project of The Third Affiliated Hospital of Chongqing Medical University (Project No. KY23071 and KY24040) and 2022 Research Project of Postgraduate Education and Teaching Reform of Chongqing Medical University (No. xyjg220222) to Mao bijing.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Declarations Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Material

Supplemental material for this article is available online.

References

Mireștean

Iancu

. Education in radiation oncology-current challenges and difficulties. Int J Environ Res Public Health. 2022;19(7):3772–3772. doi:10.3390/ijerph19073772

Zeng

Chen

Wang

. Effects of seminar teaching method versus lecture-based learning in medical education: a meta-analysis of randomized controlled trials. Med Teach. 2020;42(12):1343–1349. doi:10.1080/0142159x.2020.1805100

McLean

. Case-based learning and its application in medical and health-care fields: a review of worldwide literature. J Med Educ Curric Dev. 2016; 3. doi:10.4137/jmecd.S20377.

Dapper

Wijnen-Meijer

Rathfelder

, et al. Radiation oncology as part of medical education-current status and possible digital future prospects. Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft [et al.]. 2021;197(6):528–536. doi:10.1007/s00066-020-01712-x

Lee

Kulasegaram

. Artificial intelligence in undergraduate medical education: a scoping review. Acad Med. 2021;96(11 s):S62–s70. doi:10.1097/acm.0000000000004291

Clements

Schupp

Tattersall

Brown

Larson

. Monaco treatment planning system tools and optimization processes. Med Dosim. 2018;43(2):106–117. doi:10.1016/j.meddos.2018.02.005

Chamunyonga

Burbery

Caldwell

Rutledge

Fielding

Crowe

. Utilising the virtual environment for radiotherapy training system to support undergraduate teaching of IMRT, VMAT, DCAT treatment planning, and QA concepts. J Med Imaging Radiat Sci. 2018;49(1):31–38. doi:10.1016/j.jmir.2017.11.002

Benson

Rodgers

Nelder

, et al. The impact of an educational tool in cervix image registration across three imaging modalities. Br J Radiol. 2022;95(1137):20211402. doi:10.1259/bjr.20211402

Bismilla

Boyle

Mangold

, et al. Development of a simulation-based interprofessional teamwork assessment tool. J Grad Med Educ. 2019;11(2):168–176. doi:10.4300/jgme-d-18-00729.1

10.

Norcini

Burch

. Workplace-based assessment as an educational tool. AMEE Guide. 2007;29(9):855–871. 31. Medical teacher. doi:10.1080/01421590701775453

11.

Naeem N. Validity, Reliability, Feasibility . Acceptability and educational impact of direct observation of procedural skills (DOPS). Journal of College of Physicians And Surgeons Pakistan. 2013;23(1):77–82.

12.

Lörwald

Lahner

Nouns

, et al. The educational impact of Mini-clinical evaluation exercise (Mini-CEX) and direct observation of procedural skills (DOPS) and its association with implementation: a systematic review and meta-analysis. PloS one. 2018;13(6):e0198009. doi:10.1371/journal.pone.0198009

13.

Weissenbacher

Bolz

Zimmermann

Donaubauer

Stehr

Hempel

. Mentoring and workplace-based assessments for final year medical students: an effective way to increase satisfaction and competence? Anaesthesist. 2021;70(6):486–496. doi:10.1007/s00101-020-00902-7

14.

Schulz

Altman

Moher

DCONSORT

. Statement: updated guidelines for reporting parallel group randomised trials. J Pharmacol Pharmacother. 2010;1(2):100–107. doi:10.4103/0976-500x.72352

15.

de la Peña

Garcia-Linares

. Radiotherapy learning in medical undergraduate courses. Journal of Cancer Education: The Official Journal of the American Association for Cancer Education. 2016;31(4):660–665. doi:10.1007/s13187-015-0868-2

16.

Oncology

. Application of CBL combined with PBL teaching method in the teaching of radiotherapy of oncology department. China Continuing Medical Education. 2019;11(32):23–25. doi:10.3969/j.issn.1674-9308.2019.32.010

17.

Udupa

Liu

Jin

, et al. Combining natural and artificial intelligence for robust automatic anatomy segmentation: application in neck and thorax auto-contouring. Med Phys. 2022;49(11):7118–7149. doi:10.1002/mp.15854

18.

Kwon

Dohopolski

Morgan

, et al. Artificial intelligence-empowered radiation oncology residency education. Pract Radiat Oncol. 2023;13(1):8–10. doi:10.1016/j.prro.2022.09.003

19.

Hansen

Hussein

Bernchou

Zukauskaite

Thwaites

. Plan quality in radiotherapy treatment planning - review of the factors and challenges. J Med Imaging Radiat Oncol. 2022;66(2):267–278. doi:10.1111/1754-9485.13374

20.

Sahoo

Poenisch

Zhang

, et al. 3D treatment planning system-Varian eclipse for proton therapy planning. Med Dosim. 2018;43(2):184–194. doi:10.1016/j.meddos.2018.03.006

21.

Chen

Cai

Soon

, et al. Dosimetric comparison between intensity modulated radiation therapy (IMRT) vs dual arc volumetric arc therapy (VMAT) for nasopharyngeal cancer (NPC): systematic review and meta-analysis. J Med Imaging Radiat Sci. 2023;54(1):167–177. doi:10.1016/j.jmir.2022.10.195

22.

Snyder

Hyer

Flynn

Boczkowski

Wang

. The commissioning and validation of Monaco treatment planning system on an Elekta VersaHD linear accelerator. J Appl Clin Med Phys. 2019;20(1):184–193. doi:10.1002/acm2.12507

23.

Thewes

Eckl

Schneider

. Transmission probability filter optimization for agility MLC in Monaco treatment planning system. J Appl Clin Med Phys. 2023;24(9):e14105. doi:10.1002/acm2.14105

24.

Roche

Crane

Powers

Crabtree

. Agility MLC transmission optimization in the Monaco treatment planning system. J Appl Clin Med Phys. 2018;19(5):473–482. doi:10.1002/acm2.12399

25.

Møller

Pappot

Bernchou

, et al. Feasibility, usability and acceptance of weekly electronic patient-reported outcomes among patients receiving pelvic CT- or online MR-guided radiotherapy - a prospective pilot study. Tech Innov Patient Support Radiat Oncol. 2022;21:8–15. doi:10.1016/j.tipsro.2021.12.001

26.

Paudel

Kim

Sarfehnia

, et al. Experimental evaluation of a GPU-based Monte Carlo dose calculation algorithm in the Monaco treatment planning system. J Appl Clin Med Phys. 2016;17(6):230–241. doi:10.1120/jacmp.v17i6.6455

27.

Vikraman

Manigandan

Karrthick

, et al. Quantitative evaluation of 3D dosimetry for stereotactic volumetric-modulated arc delivery using COMPASS. J Appl Clin Med Phys. 2014;16(1):5128. doi:10.1120/jacmp.v16i1.5128

28.

Tugrul

. Comparison of Monaco treatment planning system algorithms and Monte Carlo simulation for small fields in anthropomorphic RANDO phantom: the esophagus case. J Cancer Res Ther. 2021;17(6):1370–1375. doi:10.4103/jcrt.JCRT_1143_20

29.

Szeverinski

Kowatsch

Künzler

Meinschad

Clemens

DeVries

. Evaluation of 4-Hz log files and secondary Monte Carlo dose calculation as patient-specific quality assurance for VMAT prostate plans. J Appl Clin Med Phys. 2021;22(7):235–244. doi:10.1002/acm2.13315

30.

Sathiyaraj

Manikandan

Varatharaj

Ganesh

Sathiyan

Ravikumar

. Dosimetric evaluation of hybrid and volumetric-modulated arc therapy plan for left-sided chest wall irradiation in MONACO treatment planning system. J Cancer Res Ther. 2022;18(6):1728–1732. doi:10.4103/jcrt.JCRT_707_20

31.

Soyama

Azumi

. A new tumor delineation method for brain metastases radiotherapy by jointly referring to contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Cureus. 2020;12(7):e9106. doi:10.7759/cureus.9106

32.

Wang

Liang

Zheng

. Application of formative evaluation and teaching feedback in PBL teaching of medical Genetics. Yi Chuan. Aug 20 2020;42(8):810–816. doi:10.16288/j.yczz.20-068

33.

Hongzhi

Yingsheng

, et al. Application of Mini-CEX combined with DOPS supported by WeChat in assessment of clinical teaching in oncology. Chinese Journal of Medical Education. 2020;40(5):5.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.21 MB

0.13 MB

0.20 MB

0.06 MB