The role of magnetic resonance imaging and computed tomography in oral squamous cell carcinoma patients' preoperative staging

Introduction

The aim of the study was to evaluate the accuracy of MRI and CT with regard to the detection of lymph node metastases based on the data of specific patients with OSCC who received bilateral neck dissection.

Materials and methods

In a retrospective analysis from 01/2014 to 12/2020 patients who underwent primary tumor resection and bilateral neck dissection were evaluated.

Results

174 preoperative MRI (78.74%, N=137) and CT (21.26%, N=37) were correlated with the histopathological findings. CT had a sensitivity of 67% and specificity of 68% (p=0.76). MRI showed an overall sensitivity of 66% and a specificity of 68% (p=0.76). In 52.87% of all cases no differences between cN and pN were found. MRI is the method to overestimate lymph node involvement compared to CT (overestimation in 27% vs. 21.62%).

Conclusion

The current data indicate that MR and CT show poor efficacy in the detection of cervical metastases. Accordingly, attention must be paid to alternatives to correct local staging modalities. The application of structured bilateral neck dissection needs to be questioned.

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.

Undergraduate medical students need more training in craniomaxillofacial surgery: a comparative study between medical and dental students

Purpose:

To compare the performance in oral, craniomaxillofacial, and facial plastic surgery (CMF)-specific surgical skills between medical students (MS) and dental students (DS) and hence adjust the current CMF training to student-specific needs. The investigators hypothesized that there would be no performance differences between MS and DS.

Methods:

The investigators implemented a comparative retrospective item-based analysis of student performance in a CMF-specific objective structured clinical examination (OSCE) from 2008 to 2015. The sample was composed of 1010 MS and 225 DS who completed a standardized CMF training and OSCE. Three OSCE scenarios [management mandible fracture (MMF), management zygomatic fracture (MZF), and structured facial examination (SFE)] were included in the study because learning objectives were equal. Descriptive and bivariate statistics were computed and the p value was set at 0.05.

Results:

In all of the analyzed OSCE scenarios, DS significantly outperformed MS (MMF p<0.001; MZF p=0.013; SFE p<0.001). DS especially appeared to be better in the correct interpretation of radiological findings (five of seven items, MMF) and the correct allocation of anatomical structures (four of five items, MZF) as well as the symptom-oriented examination of the eye (three of three items, SFE).

Discussion and conclusion:

DS overall seem to be perform better in typical CMF skills. The reasons for this performance gap could be a more profound knowledge of the facial anatomy as well as a higher awareness for CMF as a related specialty to dentistry. CMF should be included in medical curricula in a larger scale, and possible career paths should be highlighted to MS and DS to raise attraction for the specialty. Further studies should focus on the implementation of modern teaching methods in CMF education.

Structured evaluation and need-based restructuring of the cranio-maxillofacial surgery module within surgical clerkship

BACKGROUND

Evaluations are important for teaching courses and contribute to educational quality assurance. CMF surgery provides a module in the skills-lab week in preparation for surgical clerkship. Even though the CMF module receives positive evaluations, the students report deviating content. Subsequently, exams skills were often not mastered correctly. The aim of this study is to gather the contents taught within the course and to revise the module accordingly.

METHODS

A structured evaluation sheet was used to evaluate the CMF modules. The detailed time frame used, teaching methods integrated, and learning objectives taught were documented. Based on the results, the module was restructured and re-evaluated twice.

RESULTS

There were substantial fluctuations among the taught learning objectives in the first evaluation (21%-47% of the objectives were totally covered). The deployed time (160.50 ± 32.55 min) for the module was much shorter than scheduled (210 min). After restructuring, more learning objectives were totally covered (44%-100%), which corresponds to a significant gain (p = .024). The deployed teaching time for the modules was used more efficiently (183.65 ± 21.10 min/p = .005), and the additional time (51.89 ± 21.23 min vs. 37.55 ± 16.06 min before/p = .011) was used mainly for practical exercises.

CONCLUSION

Structured evaluations are a meaningful tool for gaining valuable insights regarding the contents and quality of teaching courses and pinpointing potential for improvement. Key factors for the improvement of an educational module are the definition of learning goals within the context of a transparent and structured module.

An eFace-Template Method for Efficiently Generating Patient-Specific Anatomically-Detailed Facial Soft Tissue FE Models for Craniomaxillofacial Surgery Simulation

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft-tissue changes following osteotomy. This can only be accomplished on an anatomically-detailed facial soft tissue model. However, current anatomically-detailed facial soft tissue model generation is not appropriate for clinical applications due to the time intensive nature of manual segmentation and volumetric mesh generation. This paper presents a novel semi-automatic approach, named eFace-template method, for efficiently and accurately generating a patient-specific facial soft tissue model. Our novel approach is based on the volumetric deformation of an anatomically-detailed template to be fitted to the shape of each individual patient. The adaptation of the template is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. This methodology was validated using 4 visible human datasets (regarded as gold standards) and 30 patient models. The results indicated that our approach can accurately preserve the internal anatomical correspondence (i.e., muscles) for finite element modeling. Additionally, our hybrid approach was able to achieve an optimal balance among the patient shape fitting accuracy, anatomical correspondence and mesh quality. Furthermore, the statistical analysis showed that our hybrid approach was superior to two previously published methods: mesh-matching and landmark-based transformation. Ultimately, our eFace-template method can be directly and effectively used clinically to simulate the facial soft tissue changes in the clinical application.