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With an incidence of almost 3000 people a year, head and neck cancers (HNC) take up 2,7% of all new diagnosed cancers in the Netherlands in 2015 and the numbers are growing1. Around 90% of all HNCs are squamous cell carcinoma’s (SCC), a malignancy derived from the surface epithelial cells2. Oral cancer is a subgroup of head and neck carcinomas and makes up 17% of this cancer type. It includes the lips, tongue, floor of mouth, alveolar ridges, gingiva, buccal mucosa and hard palate. From each of these subsites a squamous cell carcinoma may arise, mist frequently at the lateral border of the tongue and the floor of mouth3. A SCC of the dorsal surface is rare. The 5 year survival rate is only 60%1. Oral cancers are believed to be a multifactorial disease. Tobacco use is the single biggest risk factor and alcohol appears to have a synergistic effect4–7.  Dietary habits, occupational activities, socioeconomic status, exposure to external agents and genetic susceptibility are also risk factors8. Unlike most Oropharyngeal cancers, oral cancers can easily be detected by oral examination. Techniques that can assist in de detection of cancerous and precancerous lesions are visual auto fluoresce, narrow band imaging, near infrared fluorescence imaging and4. The preferred treatment for oral SCC’s is surgery followed by radiotherapy or chemo radiation in case of an incomplete resection or too narrow tumour resection margins. Surgery often includes a neck dissection as 20% of the patients with a SCC in the oral cavity have several lymph node metastasis6. Alternatives to classical surgery, radiation and chemotherapy that can also be applied are: CO2 laser surgery, trans oral robotic surgery and photodynamic therapy. Tumour characteristics such as size, location, extension, histology and stage are important for the choice of treatment. Also age, condition, compliance and the patient’s choice are considerations for the choice of treatment.

The best treatment should minimize patient morbidity and thus improve survival and quality of life (QOL). Post-operative QOL depends largely on the patients ability to swallow, speak or masticate after surgery. This is called the functional outcome. Due the complexity of the structures, functional outcome of interventions in the oral cavity are often hard to predict. However it’s very hard to say to which extend these impairments will occur. Surgical removal has a direct influence on the functional outcome regarding swallowing, mastication and speech. But the functional sequelae of radiation and in particular chemo radiation, can also be substantial. Xerostomia and fibrosis are common toxicities of this treatment modality. The choice for a certain treatment is therefore directly correlated to the physicians experience or multidisciplinary board. When there isn’t a clear insight in the functional outcome after treatment it’s hard for the patient to make an appropriate choice for a certain treatment. A term often used by physicians is: “functional inoperability”. It’s a term to indicate that the expected functional outcome would be unacceptable for the patient. This is not to be confused with anatomical inoperability. A tumour is anatomically inoperable if due to removal of vital structures, in case of radical removal of the tumour, the chance of not surviving the operation would be too high. Functional inoperability is a hard threshold in the middle of a grey area. This area is grey because we do not have the tools yet to predict the functional consequences in full detail whereas a small detail can have big consequences.

Predicting functional consequences is difficult because of the complex synergistic activities of many muscles and neural structures involved in the swallowing and speech process. A lot of different anatomical structures are used and work together during speech or the swallowing action. Especially the tongue is a very complex structure. It consists of eight muscles. The muscles and there abbreviations that are relevant in this thesis are listed in Table 1. Appendix A will address Anatomy of the tongue in more detail.

Because of this complexity many researchers are challenged to search for prognostic factors of certain functional outcomes without knowing the exact anatomical orientation or tissue properties. Thanks to these reports it’s now known that size and location of the tumour are very important for functional outcome. For example: tumours at the lateral side of the tongue appear to have a less drastic effect on speech than tumours of the floor of the mouth9 Also patients have a greater risk on severe swallowing problems if they undergo a tumour resection with adjuvant Radiotherapy. The same applies for patients that have a stage T3 or T4 tumour or have tumours located at the floor of the mouth10,11.

Extrinsic muscles Intrinsic muscles

Genioglossus (GG) Superior longitudinal muscle (SL)

Hyoglossus (HG) Inferior longitudinal muscle (IL)

Styloglossus (STY) Transverse muscle (TRANS)

Palatoglossus* Vertical muscle (VERT)

Geniohyoidus (GH)**

Mylohyoid Muscle (MH)**

*The palatoglossus is not simulated in the biomechanical model

** these are muscles from the floor of the mouth, but are also embedded in the biomechanical models

 Table 1: list of tongue muscles and there abbreviations

Naturally the biggest improvement in function can be seen in the first months after surgery. Speech doesn’t significantly improve in the period between 6 and 12 months, but it appears that articulation function can still improve over the years11,12. Articulation intelligibility is also better in patients not receiving grafts (such as free-flap reconstruction) than in those receiving grafts13.

In 2009 A. Kreeft14 showed that there is no absolute consensus with regard to functional results for most treatments in oral oropharyngeal cancers. In respond to this the Virtual Therapy project at the Netherlands Cancer Institute (NKI) was started. The project is aimed at findings tools to predict functional loss in order to choose the right treatment for the patient (Appendix B ). Resent research within this project showed that not tumour stage but tumour volume is the best indicator for the extent of post-surgical functional impairment15. Other ongoing research already shows that an extensive pre-operative Range Of Motion (ROM) of the tongue has positive prognostic effects on functional outcome. However the biggest aim of the Virtual Therapy project is to create a biomechanical model that is able to predict function loss after treatment of Oral and Oropharyngeal cancers, mainly aimed at the tongue.

In most cases, a biomechanical model is created using the Finite Element Method (FEM). FEM is used in engineering to divide structures in smaller parts wherein stress, strain, motion or temperature can be calculated which then can be used to calculate the change of that certain property of the complete structure. The mathematical details of this method can be found in Appendix C. Creating FEM models of the tongue is not a new idea. FEM creations of the tongue date back 1975 when S, Kiritani et al. 16 created an elastic system to study the physiological functions of certain intrinsic and extrinsic muscles in speech production. The system was grouped in 14 units which were given a certain force to mimic a certain muscle. For every set of muscle contractions only a few iterations could be calculated. With the computational power of today, there are much more possibilities. There are a number recent studies that have focused on the creation of FEM models of the tongue, but there goals are slightly different. In research of J. Gérard et al. (2006)17,18 the main focus is to create a FEM model to study speech production. Also van Alphen et al. (2013)19 from the Virtual Therapy project created an FEM model in Matlab complete from scratch. Other studies focused on creating a technically faster and better model of the tongue20. Fujita et al. (2007)21 created a personal tongue model specifically for the simulation of a glossectomy. This study gave promising results for the usage of the finite element method to predict functional loss. They used a simple preoperative and postoperative model and compared pre and postoperative motion of the tongue. This method was slow because two manually pre- and postoperative models needed to be created for just one patient. The model was completely adapted to the patient as only one case study was used. Also Buchaillard et al. (2007)22 edited their original model to show the potential of biomechanical modelling for the prediction of functional outcome after surgery. Both a Hemiglossectomy ( Removal of lateral border of the tongue) and a floor of the mouth resection with a free-flap reconstruction where performed by changing tissue properties of elements at those specific locations on the model. The reconstructed part is then given different amounts of stiffness to simulate the effect of radiation on the new tissue. Especially the amount of stiffness showed a huge impact on the mobility of the tongue and thus on the ability to speak. This research showed promising results for the use of biomechanical modelling in the prediction of postoperative motion, but it was limited to changing tissue properties in certain elements.

The aim of this thesis is to take virtual surgery of the tongue to the next level by creating a surgery tool that is capable of creating every type of resection and simulate the postoperative tongue motion. This virtual surgery will be done on an edited version  biomechanical FEM model created in Buchaillard et al (2009)23, from now on called the “initial model”. Suturing is never simulated on a biomechanical model of the tongue, while it’s common in most surgery’s for patients with a SCC on the tongue in the Netherlands14. In addition, postoperative scarring (or fibrosis) effects have to be implemented as scarring is an important factor for postoperative motion impairment. While creating this model also other things have to be considered. What modifications are needed to perform virtual surgery on the initial model? How is the resection area selected on a 3D model? What tool is needed to create every resection area while still being easy to use? How can the initial FEM model be edited to simulate the postoperative tongue? How can the resection be closed and how can sutures be simulated? How can scar tissue be implemented in this model? As this is the first step towards detailed virtual surgery it’s not expected that this model will give highly accurate and personalized results. This research will therefore primarily focus on the simulation of the surgery and less on the personalization of the initial biomechanical model. The surgery tool created in this work can eventually be applied on highly accurate and personalized models of the tongue. When the impairment of motion can be simulated successfully it would ultimately be able to predict functional outcomes such as: swallowing, mastication and speech.

Validation of the model is needed to get answers on the following question: is the edited virtual surgery model still comparable to the initial model? Is it possible to simulate a real surgical resection and is the postoperative motion of this model comparable to the postoperative tongue motion of real patients?

Data for the pre and postoperative tongue motion of real patient will be acquired from the ongoing research of van Dijk et al. Details of this research can be found in Appendix F.

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