Identification and application of image biomarkers for the prediction of radiotherapy treatment response in head and neck cancer patients

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FACULDADE DE CIÊNCIAS

DEPARTAMENTO DE FÍSICA

Identification and Application of Image Biomarkers for

the Prediction of Radiotherapy Treatment Response in

Head and Neck Cancer Patients

Cássia Oraboni Ribeiro

Orientadores

Dra. Marianna Sijtsema, Física médica, Department of Radiation Oncology of the

University Medical Center Groningen, Groningen, The Netherlands

Prof. Dr. Luis Peralta, Professor associado, Departamento de Física da Faculdade de

Ciências da Universidade de Lisboa, Lisboa, Portugal

Mestrado Integrado em Engenharia Biomédica e Biofísica

Perfil Radiações em Diagnóstico e Terapia

DISSERTAÇÃO

-2015-

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Acknowledgments

First of all, I would like to express my sincere gratitude to all the people, both from the University in Portugal and the University Medical Center Groningen (UMCG), that allow for the possibility of this internship and the help provided in the arrangements. This was undoubtedly a unique life experiencing, which made me a different and better person, both personally and professionally.

The chair of the department of Radiation Oncology of the UMCG, Prof. Dr. Johannes Langendijk, was the first person I made contact with for the possibility of this internship. I am extremely thankful for all his availability and the excellent conditions he ensured during my stay.

Dr. Marianna Sijtsema and Dr. Roel Steenbakkers gave me the opportunity of working in the department and without them this internship would not have been possible. Both of them were always a great support and their professionalism and motivation in supervising me were always present. I am also sincerely grateful to Sanne van Dijk for all her guidance and help. Without her presence, this project would not be possible at all. I also would like to thank all the staff from the department, they were always kind and available. I am happy to say that all of them turned out to be very special people in my life and I am grateful for their friendship. Prof. Luis Peralta, as the internal supervisor, was always present and his rapid response and concern was outstanding. I am also thankful to Marianna, Roel, Sanne and Prof. Luis for all the patience, support and guidance during the execution of this thesis.

Inês Lourenço and Patricia Queiroz turned this nine months in The Netherlands absolutely amazing and succeeded in making this experience one of the most important of my life. I am sincerely grateful for their support and true friendship. Also my boyfriend, Nelson Afonso, was fundamental for all my achievements and without him everything would be more difficult.

A very special affection goes to my role models, my parents, without whom I would not have the possibility to undergo for all the amazing experience of working and living abroad. I am also very grateful for all their support during this period from Portugal.

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Resumo

A radioterapia é uma modalidade de tratamento de cancro amplamente utilizada em todo o mundo. Este tipo de terapia tem como principal objectivo focalizar uma alta dose de radiação para o tumor e ao mesmo tempo minimizar a exposição dos tecidos saudáveis presentes à volta do mesmo.

A concretização deste tipo de tratamento com alguma precisão e eficiência já se torna possível graças às mais modernas técnicas, tais como a Radioterapia Conformacional em 3D (3D-‐CRT), Radioterapia de Feixe de Intensidade Modulada (IMRT) e Terapia Arc Modulado Volumétrico (VMAT). O surgimento destas técnicas foi possível devido à modernização de modalidades de imagem usadas em oncologia. Mais concretamente, deve-‐se ao surgimento da Tomografia Computadorizada, usualmente conhecida por CT (do inglês Computed Tomography), que é o tipo de imagem principal e mais utilizada em radioterapia.

Contudo, apesar de todos estes avanços, atualmente ainda não é possível prever com precisão o resultado de um determinado tratamento de radioterapia para um paciente específico. Seria bastante útil conseguir obter esta informação mesmo antes de se sujeitar o paciente à radiação, por forma a adaptar o tratamento de acordo com as necessidades e características da doença. Esta prática designa-‐se por medicina personalizada e permite resultados muito mais promissores para uma determinada terapia.

A CT tem a vantagem de ser um método não invasivo. É possível através das imagens a 3 dimensões produzidas conseguir-‐se distinguir (visualmente) diferentes características de um tumor, relativamente à sua intensidade, forma, tamanho ou textura. Neste contexto, surge um campo emergente em processamento de imagem designado por

radiomics. Este método permite caracterizar uma imagem quantitativamente, o que

possibilita a definição muito mais precisa do fenótipo de um determinado tumor. Radiomics trata da extração das designadas, ao longo desta tese, características quantitativas de uma imagem.

Assim, neste projeto de estágio foi estudada a possibilidade de se conseguir prever a eficiência de um tratamento de radioterapia através da aplicação de radiomics em imagens de CT. A população em estudo foram os doentes com carcinoma espinocelular de cabeça e pescoço (HNSCC). A eficiência do tratamento foi estudada através da análise de sobrevivência de 310 pacientes portadores desta doença e que fizeram tratamento com

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radioterapia no University Medical Center Groningen (UMCG). Este centro hospitalar, localizado na cidade de Groningen (no norte da Holanda), foi o local do estágio efectuado por 9 meses para a realização deste projeto.

Utilizou-‐se o Matlab para implementar 125 algoritmos diferentes de características de imagem. Posteriormente, os valores destas características de imagem foram extraídos das imagens de CT dos tumores dos 310 pacientes analisadas. O software de estatística designado por R foi o programa maioritariamente utilizado na parte da análise dos valores obtidos das características de imagem para os pacientes deste estudo.

As características de imagem implementadas foram divididas em 3 grupos diferentes. O primeiro grupo consiste no cálculo de métricas a partir do histograma de intensidades de todos os voxels da imagem de CT que contém o tumor. O segundo grupo traduz a forma e tamanho do tumor e por fim, o último e terceiro grupo, quantifica a heterogeneidade do mesmo. Apenas as características de imagem implementadas que mostrem uma diferença significativa (entre valores baixos e altos dessa mesma característica) na sobrevivência após o início do tratamento serão investigadas em mais pormenor e designados ao longo desta tese por biomarcadores de imagem.

O objectivo deste projeto é encontrar estes biomarcadores de imagem e determinar o seu desempenho na previsão do resultado de um tratamento de radioterapia.

Em publicações anteriores, já se provou a relação de certas características quantitativas de uma imagem, que traduzem especificações do tumor, com a sobrevivência [1]. Por isso, numa primeira fase da análise estatística deste projeto, alguns dos resultados já obtidos por trabalhos anteriores foram validados com os pacientes disponíveis para este estudo.

A mais-‐valia que esta tese traz para o campo de radiomics é a análise da influência dos valores das características de imagem na, para além da sobrevivência no geral, recorrência do tumor e no aparecimento de metástases. Novas e diferentes características de imagem foram também implementadas.

Após o início da análise dos dados ao longo do estágio, foram encontrados 55 biomarcadores de imagem (44% das 125 características de imagem implementadas), o que mostra o potencial do conceito de radiomics na prática clínica de radioterapia.

Para além disso, através de um método eficiente de seleção de variáveis, pretendeu-‐se escolher as 3 melhores características de imagem, de forma a combiná-‐las num modelo capaz de prever a sobrevivência de pacientes com carcinoma espinocelular de cabeça e pescoço tratados com radioterapia. O método de seleção garantiu que as

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vii características de imagem selecionadas tivessem valores estáveis, não fossem correlacionadas entre si e apresentassem (individualmente e em combinação) os mais altos valores de desempenho de prognóstico possíveis.

Seguindo este método, os 3 biomarcadores de imagem selecionados designam-‐se por: Uniformity, Surface density e Size zone non-‐uniformity, correspondestes ao primeiro, segundo e terceiro grupo, respectivamente. Estas 3 características em combinação designam-‐se por assinatura de biomarcadores de imagem. O desempenho de um modelo contendo estes 3 biomarcadores de imagem foi muito promissor e após validação interna obteve-‐se um valor de aproximadamente 0.65 (numa escala de 0.5 até 1), que é considerada uma performance de prognóstico muito promissora.

De forma a analisar o valor suplementar de características quantitativas de imagem na prática de radioterapia, foram comparados o desempenho de prognóstico da assinatura de biomarcadores de imagem com inúmeros parâmetros específicos de pacientes já comummente utilizados na clínica. A assinatura de biomarcadores de imagem obteve um desempenho superior a todos os parâmetros excepto o designado WHO, que se trata de uma pontuação dada pelo médico antes do tratamento que incide sobre o estado físico do paciente (se é totalmente independente e ativo). É natural que este parâmetro consiga prever minuciosamente o resultado do tratamento visto que a eficiência do mesmo depende bastante das condições físicas prévias do paciente.

Para se conseguir um melhor poder de prognóstico, decidiu-‐se então combinar a assinatura de biomarcadores de imagem com a pontuação WHO num modelo designado por modelo final. Com o modelo final, obteve-‐se uma performance de aproximadamente 0.73, que indica uma capacidade excelente de predição.

Finalmente, após a obtenção de uma assinatura de biomarcadores de imagem e de um modelo final, desenvolveu-‐se sistemas de estratificação de risco pré-‐tratamento, que acaba por ser mais uma das grandes novidades que este projeto traz para o campo de

radiomics no geral. Estes sistemas baseiam-‐se na assinatura de biomarcadores de imagem

e no modelo final para diferenciar (previamente ao tratamento) os pacientes considerados com baixo risco e com alto risco de morte, recorrência de tumor ou metástases. As diferenças obtidas para os dois grupos de risco foram bastante significativas em qualquer um dos casos (morte, recorrência ou metástases). Neste projeto, estes sistemas de risco foram desenvolvidos com a informação dos pacientes disponíveis, mas a ideia é serem aplicados a um conjunto externo de doentes pertencentes à mesma população. Isto traz inúmeros benefícios clínicos, pois torna-‐se possível o desenvolvimento da medicina

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personalizada, e consequentemente, o aumento das taxas de sucesso de radioterapia nos pacientes com carcinoma espinocelular de cabeça e pescoço.

Os resultados obtidos ao longo do estágio foram bastante promissores, indicando o poder de prognóstico das características de imagem implementadas. Contudo, torna-‐se necessário validar tanto os modelos desenvolvidos como os sistemas de estratificação de risco pré-‐tratamento num conjunto externo de pacientes com carcinoma espinocelular de cabeça e pescoço por forma a se obter um desempenho menos optimista, mas possivelmente mais realista e mais representativo da população no geral. Este será talvez o próximo passo para o complexo processo da introdução de biomarcadores de imagem na prática clínica de radioterapia.

Palavras-‐chave: radioterapia, radiomics, CT, tumor, biomarcadores de imagem.

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Abstract

Each year, about 300 patients with head and neck squamous cell carcinoma (HNSCC) are treated with radiation therapy (either primary or post-‐operative) at the department of Radiation Oncology of the University Medical Center Groningen (UMCG), Groningen, The Netherlands. To our knowledge, this hospital has the largest database of information on toxicity in the world for the head and neck cancer (HNC) patients treated with radiotherapy.

In literature it was shown that specific image characteristics (440 in total) of a tumour are correlated to its patient overall survival (OS) [1]. Radiomics is the field that allows large amounts of image characteristics (features) to be extracted automatically from radiographic images. The features that demonstrate a correlation to survival can be also called image biomarkers.

The main goal of this work is to investigate in more detail the prognostic value of image features in the planning CT-‐scans of HNC patients (310 in total) for their response to radiotherapy (RT). In addition, the relation between the image features and recurrence-‐ free survival (RFS) will be assessed.

Overall, the obtained results showed the promising power of radiomics. Particularly, 55 image features demonstrated significant differences for OS. Moreover, a prognostic performance of approximately 0.65 (scored from 0.5 to 1) was obtained for the selected image features in the prediction of RT outcome for a HNC patient with similar characteristics as the dataset used for this project. Also the better performance of the image biomarkers in relation to other commonly used patient-‐specific parameters is shown. Additionally, the ability of image features in distinguishing between high and low risk patients beforehand is demonstrated, which can contribute to a more tailored treatment, and consequently, higher success rates.

Therefore, different but very promising results were obtained when compared to previous work. The methodology followed for this project revealed the promising power of the image biomarkers to accurately predict the RT treatment response in HNC patients. Therefore, the next step will consist in validating the developed models in external datasets of patients for a possible clinical use in the future.

Key words: radiotherapy, radiomics, CT, tumour, image biomarkers.

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Acronyms

2D: Two-‐Dimensional

3D-‐CRT: 3D Conformal Radiation Therapy

3D: Three-‐Dimensional

ANOVA: ANalysis Of VAriance AUC: Area Under the Curve c-‐index: concordance index

CERR: Computational Environment for Radiotherapy Research CT: Computed Tomography

CTV: Clinical Target Volume DVH: Dose-‐Volume Histogram

FDG-‐PET: 18F-‐FluorDesoxyGlucose Positron Emission Tomography FDG: 18F-‐FluorDesoxyGlucose

FDR: False Discovery Rate

GLCM: Grey-‐Level Co-‐occurrence Matrix GLRLM: Grey-‐Level Run-‐Length Matrix GLSZM: Grey-‐Level Size-‐Zone Matrix GTV-‐LN: GTV of the Lymph Nodes GTV-‐PT: GTV of the Primary Tumour GTV: Gross Tumour Volume

HNC: Head and Neck Cancer

HNSCC: Head and Neck Squamous Cell Carcinoma HPV: Human Papillomavirus

HU: Hounsfield Units

ICC: Intraclass Correlation Coefficient

IMRT: Intensity-‐Modulated Radiation Therapy LINAC: LINear ACcelerator

MLC: MultiLeaf Collimator

MRI: Magnetic Resonance Imaging

NGTDM: Neighbourhood Grey-‐Tone Difference Matrix NSCLC: Non-‐Small Cell Lung Carcinoma

NTCP: Probability of Normal Tissue Complications OAR: Organs At Risk

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OS: Overall Survival

PEG: Percutaneous Endoscopic Gastrostomy PET: Positron Emission Tomography

PH: Proportional Hazards PTV: Planning Target Volume RFS: Recurrence-‐Free Survival

ROC: Receiver Operating Characteristic RT: Radiation Therapy

SCC: Squamous Cell Carcinoma TCP: Tumour Control Probability

UICC: Union for International Cancer Control UMCG: University Medical Center Groningen VMAT: Volumetric Modulated Arc Therapy VOI: Volume Of Interest

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List of Figures

Figure 2.1 -‐ The anatomical subsites of the upper aerodigestive tract [70]. ... 4

Figure 2.2 -‐ Endoscopic examination of a SCC of the larynx [71]. ... 5

Figure 2.3 -‐ Stage grouping for hypopharyngeal cancer [22]. ... 6

Figure 2.4 -‐ An MLC perpendicular to the eye view of the beam [11]. ... 9

Figure 2.5 -‐ Dose distribution in a typical head and neck IMRT plan [11]. ... 11

Figure 2.6 -‐ TCP and NTCP curves [72]. ... 12

Figure 2.7 – DVH for a HNC case for 3 different delineated structures in a radiotherapy treatment plan. The pairs of lines of the same colour stand for the comparison between 2 different radiotherapy techniques [73]. ... 13

Figure 2.8 -‐ Individually moulded thermoplastic mask [74]. ... 14

Figure 2.9 -‐ Clinical and planning tumour volumes (CTV and PTV) [75]. ... 15

Figure 3.1 -‐ CT images of different lung tumours. Tumour contours are in the left and their respective three-‐dimensional (3D) visualizations are in the right [1]. ... 20

Figure 3.2 -‐ Followed procedure of radiomics [1]. ... 20

Figure 5.1 -‐ Kaplan-‐Meier Curve for the 310 HNSCC patients of the database. ... 28

Figure 5.2 –2D example of a GLCM filling from a 4x4 image with four grey levels. In this case, Ng=4, δ=1 and α=0 (horizontal direction). ... 31

Figure 5.3 – 2D example of a GLRLM filling from a 4x4 image with four grey levels. In this case, Ng=4 and θ=0 (horizontal direction). ... 31

Figure 5.4 -‐ 2D example of a NGTDM filling from a 5x5 image with five grey levels. In this case, d=1, resulting in a 3x3 neighbourhood. Therefore, this neighbourhood can only be centered on the pixels within the indicated yellow square. The pixels outside this yellow square are considered as being located in the periphery of the image. As an example (in red), the first entry of the matrix NGTDM(1) is given by 1-‐!"_! ≈ 2.63 since there is only one pixel within the yellow square with the grey level 1.. ... 32

Figure 5.5 -‐ 2D example of a GLSZM filling from a 4x4 image with four grey levels. ... 33

Figure 5.6 -‐ Transversal, coronal and sagittal head and neck CT image data visualized using CERR (Computational Environment for Radiotherapy Research) [76]. ... 34

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Figure 5.7 – Transversal view of the delineation of the GTV-‐PT in the planning CT (left) and the respective cropped on countering 3D image (right). The features are extracted from the image shown in the right. All this image data illustrating the strategy used for feature extraction was visualized using CERR [76]. ... 34 Figure 5.8 – Graph representation of the stability rank for different manual delineations of the tumour (x-‐axis) vs. the c-‐index obtained (y-‐axis) for each one of the 440 image features implemented in previous work. A stability rank of 1 indicates the most robust feature and 440 the least stable one. Every dot indicates one feature and the grey area in the regression line represents the confidence interval [1]. ... 40 Figure 6.1 -‐ Common Kaplan-‐Meier curves for the features Compactness (2) and Sphericity. ... 47 Figure 6.2 -‐ Kaplan-‐Meier curves for the feature Grey level non-‐uniformity mean. ... 48 Figure 6.3 -‐ Plot of ICC vs. c-‐index and respective regression line. Every circle indicates one feature. The green line is the linear fit and the broken red lines represent the 95% confidence interval for the prediction (R!_{=0.12
and
p-‐value=6.71E-‐05).
...}₅₂ Figure 6.4 – Pairs of Kaplan-‐Meier curves for the features of the image biomarker signature: (A) Uniformity, (B) Surface density and (C) Size zone non-‐uniformity. ... 55 Figure 6.5 – Schoenfeld residuals method to check the PH assumption for the feature/covariate (A) Uniformity, (B) Surface density and (C) Size zone non-‐ uniformity. Plots of the scaled Schoenfeld residuals against transformed time (in months) for each one of the covariates in the model fitted to the 310 HNSCC patients. The solid line is a smoothing-‐spline fit to the plot and the dashed lines represent a ± 2-‐standard-‐error band around the fit. ... 56 Figure 6.6 -‐ Pre-‐treatment risk stratification systems for OS based in the image biomarker signature (A) and in the final model (B). ... 61 Figure 6.7 -‐ Pre-‐treatment risk stratification systems for RFS based in the image biomarker signature (A) and in the final model (B). ... 62

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List of Tables

Table 6.1 -‐ Probability of survival after 2 years of the start of the RT treatment for the 310 HNSCC patients. These results are shown according to the values of the 3 features

describing compactness/sphericity and heterogeneity. ... 48

Table 6.2 – Prognostic performances obtained in the available dataset for 3 image features of the signature from Aerts et al. [1]. ... 49

Table 6.3 -‐ Significant features of Group 1 obtained by controlling the FDR. ... 49

Table 6.6 – The 10 most prognostic image features and their respective c-‐index by descending order. ... 51

Table 6.7 – ICC for the 10 most prognostic image features. ... 52

Table 6.8 -‐ Image biomarker signature prognostic performance and PH test result. ... 53

Table 6.9 – Prognostic performance (both optimistic and after validation) of the multivariable Cox model containing the image biomarker signature. ... 54

Table 6.10 – DVH parameters, their obtained p-‐value for the log rank test and the prognostic performances. The variables that obtained significant survival differences are highlighted in bold. ... 57

Table 6.11 – Log rank test result for clinical factors and basic metrics. The variables that obtained significant survival differences are highlighted in bold. ... 58

Table 6.12 – Non-‐validated prognostic performances of D98 and the other patient-‐specific parameters analysed. The variables that obtained significant survival differences are highlighted in bold. ... 58

Table 6.13 -‐ Prognostic performance of the image biomarker signature in the modality groups. ... 58

Table 6.14 -‐ Prognostic performance of the image biomarker signature in the HPV status groups. ... 58

Table 6.15– Complementary effect of the image biomarker signature in commonly used parameters. ... 59

Table 6.16 – Prognostic performances of patient-‐specific variables in combination with the image biomarker signature. ... 59

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Table 6.17 – Log rank test and probability of survival after 2 years for the pre-‐treatment risk stratification systems. ... 60 Table A.1 -‐ Image features of Group 1. ... II Table A.2 – Image features of Group 2. ... IV Table A.3 – Image features of Group 3 extracted from the GLCM. ... VII Table A.4– Image features of Group 3 extracted from the GLRLM. ... X Table A.5 – Image features of Group 3 extracted from the NGTDM. ... XII Table A.6 – Image features of Group 3 extracted from the GLSZM. ... XIV

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Chapter 1 Introduction

Head and neck cancer (HNC) is ranked as the sixth most common type of cancer, accounting for an estimated 650 000 new cases and 350 000 deaths worldwide, every year [2]. The most common histological type of HNC is the squamous cell carcinoma (SCC), which is usually detected in an advanced stage disease [3]. In the last two decades, an increase of 6.5% (from 27.2% to 33.7%) in the five-‐year overall survival (OS) of patients with SCC of the head and neck has been achieved [4]. This is mostly due to recent developments of treatment options, such as the administration of concomitant chemotherapy in patients treated with radiotherapy (RT). However, survival rates still remain low mainly due to loco-‐regional recurrences and distant failures (distant metastasis), which have poor treatment outcomes. Five-‐year local-‐ and distant failure rates after receiving concomitant chemoradiotherapy are 50.8% and 16.8%, respectively [4].

In order to improve the OS for HNC patients treated with RT, it would be useful to know beforehand, by their tumour characteristics, if a certain patient is high or low risk. The treatment could be planned accordingly and, consequently, the outcomes could be much more positive. The increasingly tailored treatment based on specific characteristics of the patient and their disease is called personalized medicine [1].

Currently, treatment-‐ and patient-‐specific parameters, like dose-‐volume histogram (DVH) parameters and clinical tumour information, such as TNM, are used to estimate radiotherapy treatment outcome. However, based on these parameters it is not possible to obtain an accurate prediction of the probability of tumour control for a specific patient. Furthermore, these parameters do not provide a molecular characterization of the tumours.

Molecular characterization using genomics and proteomics technologies are already used to characterize and classify tumours. These techniques require biopsies or invasive surgeries to extract and analyse only small portions of tumour tissue, which do not allow for a complete characterization. The applicability of these procedures is limited because tumours are spatially and temporally heterogeneous. Furthermore, genomics and proteomics are still challenging techniques to implement into clinical routine.

In this context, medical imaging arises as a good option as is routinely used worldwide in clinical practice, for oncologic diagnosis and treatment guidance. In addition, imaging is a non-‐invasive procedure and can provide a more comprehensive

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2 view of the entire tumour, having great potential to monitor the development and progression of the disease or its response to therapy. The existing modalities in oncology, such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Computed Tomography (CT), can exhibit strong phenotypic differences in tumours. The most widely used imaging modality in oncology is X-‐Ray CT, which evaluates tissue density. This technique will be the one used for this project.

The hypothesis for this project is that different tumour phenotypes, that can be detected by image modalities, relate to patient-‐specific response to RT. The process that allows this extraction is called radiomics.

Radiomics is an emerging field able to characterize an image quantitatively. Therefore, the ultimate aim of this project is to predict RT treatment outcome of a specific patient based on their quantitative tumour CT image characteristics.

Briefly, the remainder of the thesis is organized as follows: Chapter 2 provides a background for HNC and explains in detail the followed steps in the University Medical Center Groningen (UMCG) for the treatment of HNC through RT; In Chapter 3, the concepts of radiomics are introduced, followed by the state of the art and, subsequently, a description of the main contributions of the present study in the field; Chapter 4 exposes some definitions regarding survival analysis that need to be retained for the understanding of the rest of the thesis; In Chapter 5 the detailed description of the patients used for this study and the methods of both image processing and data analysis are explained; Chapter 6 shows all the results obtained and Chapter 7 their respective discussion; Finally, Chapter 8 gives a conclusion of all the work performed, mentioning some current limitations of this study and also interesting topics to develop as future work.

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Chapter 2 Background

An overview of head and neck cancer (HNC), some important aspects of radiotherapy, and also the treatment of HNC through radiotherapy are given in this chapter for the comprehension of the aim of this project.

2.1. Head and Neck Cancer

Head and neck cancer (HNC) represents about 6% of all diagnosed cancer cases [2]. The median age for diagnosis of HNC patients is the early sixties and it is a type of malignancy more common in male patients.

Cigarette smoking, the use of smokeless tobacco and also alcohol abuse have shown to be the major environmental risk factors for HNC [5]. In addition, Human Papillomavirus (HPV) was suggested to be of influence in the development of HNC [6]. Common symptoms of HNCs consist of resistant mucosal ulcers, sore throat, referred otalgia, dysphagia, chronic cough and neck mass.

2.1.1. Diagnosis

For the classification and attribution of stages for HNC, the diagnosis of the tumour has to be concluded. To achieve a definitive diagnosis, physical examination, blood tests and histological biopsy can be performed. In addition, imaging modalities, such as X-‐rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound and 18F-‐Fluordesoxyglucose Positron Emission Tomography (FDG-‐PET) can be used. Particularly, ultrasound complemented with fine needle aspiration, provide the extraction and pathological evaluation of a portion of the tumour.

In addition, for examination of mucosa in the upper aerodigestive tract, a nasopharyngolaryngoscope is used. By displacing this flexible fibre-‐optic endoscope through the naso-‐, oro-‐, hypopharynx and superior region of the larynx, optical information on location and superficial local invasion of the malignant tumour can be acquired.

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2.1.1.1. Classification

Cancers can be classified in two ways: by the type of tissue in which the cancer originates (histological type) and by primary site, which is the location in the body where the cancer was first developed.

The primary site of HNCs, as the name implies, is in the head and neck region. Despite a small number that arises in the paranasal sinuses, thyroid and salivary glands, most of HNCs arise from the upper aerodigestive tract, which is composed by four anatomical subsites: oral cavity, oropharynx, hypopharynx and larynx (Figure 2.1).

More precisely, the oral cavity is defined anteriorly by the lips, superiorly by the hard and soft palate junction and inferiorly by the circumvallate papillae. The oropharynx is defined anteriorly by the circumvallate papillae and posteriorly by the posterior pharyngeal wall. The hypopharynx is superiorly bounded by the hyoid bone and extends to the inferior border of the cricoid cartilage. Finally, the larynx extends from the tip of the epiglottis to the inferior edge of the cricoid cartilage.

Regarding the classification of cancers based on histological type, there are hundreds of different cancers, which can be grouped into the following categories: carcinoma, sarcoma, myeloma, leukaemia, lymphoma, blastoma and mixed types. However, carcinoma is the most common histological type and it consists in malignancies derived from epithelial cells. Epithelial tissue is present in the skin, as well

Figure 2.1 -‐ The anatomical subsites of the upper aerodigestive tract [70].

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as in the covering and lining of organs and internal passageways, such as the gastrointestinal tract. Carcinomas are divided into two major subtypes: adenocarcinoma, which develops in an organ or gland, and squamous cell carcinoma (SCC), which originates in the squamous epithelium. Additionally, adenocarcinomas generally occur in mucus membranes and SCCs can appear in many areas of the body.

Since all of the mentioned subsites of the upper aerodigestive tract are lined by squamous mucosa, the most common histological type of malignancies in these regions is the carcinoma, particularly, the SCC (Figure 2.2). In fact, over 90% of all upper aerodigestive tract malignancies are SCCs [5]. In the stage of presentation, these types of malignancies usually involve regional lymph nodes but the presence of distant metastasis is uncommon.

2.1.1.2. TNM Staging

The TNM (Tumour, Node, Metastasis) is a cancer staging notation system, developed and maintained by the Union for International Cancer Control (UICC), which is currently used in the oncology field for risk stratification and treatment decision-‐ making.

With this classification system, consensus on one globally recognised standard for the classification of the extent of spread of cancer of a patient is achieved. Since this notation is only used for malignancies originated from a solid tumour, it is applicable for HNC, in which each type has its own TNM classification.

Figure 2.2 -‐ Endoscopic examination of a SCC of the larynx [71].

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6 Adopting TNM brings several benefits to the clinical field, such as helping to plan the treatment and giving an indication of its prognosis. In addition, the TNM system can assist in the evaluation of the results of treatment and also enables facilities around the world to interchange information more productively [7].

In this notation, T describes the size of the original (primary) tumour and whether it has invaded nearby tissue. N describes nearby (regional) lymph nodes that are involved. Finally, M describes distant metastasis (spread of cancer from one part of the body to another). After obtaining a value for each one of these three parameters, it is possible to group specific values of the three parameters to define different stages, which are associated with prognosis (Figure 2.3).

2.1.2. Treatment

The predominant treatment modalities for HNC patients consist of surgery, radiation therapy (RT) and chemotherapy, individually or in combination. The selection and planning of the most appropriate treatment for head and neck squamous cell carcinoma (HNSCC) is often complicated and can involve an evaluation by a multidisciplinary team, including head and neck surgeons, medical oncologists, dieticians, radiation oncologists, radiologists, plastic surgeons and dentists. In addition, primary tumour site, stage, resectability and also patient factors (as swallowing, airway considerations, desire for organ preservation and illness) can interfere in the treatment selection [2].

Oral cavity and oropharyngeal SCCs are the most common types of HNC. In general, early stage lesions can be treated with either RT or surgery, with radiotherapy generally yielding better functional outcomes [8]. However, stages III and IV cancers,

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with worse prognosis, are treated with surgery followed by RT. In case of unresectable lesions in these late stages, the cancer is treated with combined radiation and chemotherapy.

The treatment of early stages of hypopharyngeal cancers can be accomplished through either surgery or RT. In contrast, higher stage lesions require surgery followed by RT. The spread of this type of cancer to regional lymph nodes (loco-‐regional metastasis) is common and must be addressed with neck dissection and/or RT [5].

Early laryngeal cancer can be treated with RT or surgery. More advanced lesions can be treated with either surgery followed by RT or combined RT and chemotherapy. In all of these procedures, a considerable effort is performed in order to spare the larynx.

2.2. Radiation Therapy

2.2.1. External Beam Radiotherapy Modalities

Intensity-‐Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) are two modalities for external beam RT that arise as alternatives to the former standard modality Three-‐Dimensional Conformal Radiotherapy (3D-‐CRT).

2.2.1.1. Conformal Radiation Therapy

In Three-‐Dimensional Conformal Radiotherapy (3D-‐CRT), precise tumour definition and more accurate dose calculations (by accounting for axial anatomy and complex tissue contours) are achieved by the use of Computed Tomography (CT) images in the planning of the treatment [9]. Additionally, in conventional 3D-‐CRT, delivery patterns (beam arrangement, filtration and shaping) are introduced into the treatment planning system in order to deliver an adequate radiation treatment to the patient.

With this delivery technique, it is possible to achieve suitable target coverage, while sparing healthy tissue, by the variation of the number and direction of fields and weight of the beams. Additionally, 3D-‐CRT can be delivered due to the emergence of certain components in the treatment head of the linear accelerator (LINAC), which will be subsequently explained in more detail in this text. For the photon mode therapy, wedge

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8 filters and custom blocks or multileaf collimators (MLCs) can be used. Conversely, for electron therapy, applicators are inserted instead.

Custom blocks are thick pieces of lead that are used to shape the field. They can be loaded into the accessory tray as an additional means of collimating the beam. These blocks are specifically for an individual patient to shield sensitive tissue or structures and can be made to the exact shape. However, custom blocks have fallen out of use due to multileaf collimation, which is faster, can provide more flexibility and is less taxing on staff.

MLCs have been introduced for use in radiotherapy in 1980 and are now widely used, having become an integral part of any radiotherapy department [10]. These devices have up to 80 pairs of individual leaves made of high atomic numbered material, usually tungsten and can move independently in and out of the path of a particle beam in order to form the desired field shape (Figure 2.4).

There are frequent occasions when, instead of a uniform intensity across the beam profile, a wedge-‐shaped intensity profile is required for the photon beam. The wedge is used to compensate for different attenuations caused by the tissues, thus being possible to obtain a homogeneous dose distribution. To this end, the wedge angle is previously defined, and this is the angle from the perpendicular line of the central axis to the isodose curve within the treated volume.

Finally, applicators are used to provide additional collimation with the aim of keeping a sharp electron beam edge within the treatment volume [10]. The lower sections are made up of successive collimation layers that absorb electrons that would scatter out of the field. In addition, the applicator typically has an additional tray for the insertion of electron cut-‐outs. Unlike the custom blocks for photon treatments, the electron cut-‐outs are much thinner and lie very close to the patient’s surface in order to reduce the size of the side of the beam (or penumbra).

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2.2.1.2. Intensity-Modulated Radiation Therapy

Since 2006, Intensity-‐Modulated Radiation Therapy (IMRT) replaced 3D-‐CRT for the treatment of the majority of HNC patients in the UMCG. IMRT has been considered the most exciting development in RT since the introduction of 3D imaging (CT) to the treatment planning process [11]. Besides IMRT being an advanced form of 3D-‐CRT, these two techniques differ in the procedure for treatment planning and treatment execution at the LINAC.

Fluence can be seen as the amount of radiation that comes out of the LINAC in a certain direction. The great innovation that arises with IMRT is the possibility of creation of multiple segments in one beam by the MLC, which leads to a non-‐uniform radiation fluence in each field direction (modulated intensities). Therefore, the superposition of modulated beams allows the delivery of a highly conformal dose distribution to the patient, in which high doses are addressed to tumour (target) tissues (Figure 2.5). Simultaneously, since concavities in the high-‐dose irradiated volume are present, this type of radiotherapy allows the sparing of healthy and critical structures adjacent to the tumour, such as the parotid glands, spinal cord and brainstem [12]. The side effects caused by the radiation delivered with IMRT are generally reduced in comparison to 3D-‐CRT [13]. In addition, IMRT has shown to be effective for the loco-‐ regional control of HNC [14].

Figure 2.4 -‐ An MLC perpendicular to the eye view of the beam [11].

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10 However, IMRT requires more time for the delivery of the treatment and an increased number of monitor units (MU) -‐ the LINAC output -‐ for a given fraction, resulting in a greater integral body dose, which may lead to a higher risk for the development of second malignancies [15], [9].

The modulation of fluence caused by the MLC in IMRT can be achieved with dynamic or static field segments (step and shoot). In the step and shoot technique, whenever the MLC is changing its position, the irradiation stops and starts in the following arrangement of the leaves. Conversely, in dynamic IMRT the radiation is delivered while the leaves move to create the next segment [9].

Unlike 3D-‐CRT, in IMRT, the specific dose and volume constraints previously defined by the clinician are used as an input and, subsequently, an individualized optimal treatment plan that complies these requirements is developed (so called inverse treatment planning) [16]. However, the resulting optimal plan is impaired by known limitations of the treatment unit, such as physical and mechanical characteristics of the MLC (leakage and gaps between the leaves). Therefore, the computer-‐based optimization techniques used in IMRT planning are based on finding the global minimum of the cost function, which can be represented in each interaction point (or fluence) by:

_{𝑐𝑜𝑠𝑡 = 𝐼(𝑥, 𝑦, 𝑧) 𝐷 𝑥, 𝑦, 𝑧 − 𝐷}

!

_{(𝑥, 𝑦, 𝑧)}

!_, _{Eq.
2.1}

where 𝐼(𝑥, 𝑦, 𝑧) is the importance of the voxel defined by (𝑥, 𝑦, 𝑧), 𝐷!_{(𝑥, 𝑦, 𝑧)
is
the} required prescribed dose distribution and 𝐷 𝑥, 𝑦, 𝑧 the actual delivered dose [12]. The importance 𝐼(𝑥, 𝑦, 𝑧) of the cost function can be varied by the planner according to the pathology of the patient, by giving priority in complying the constraints in the voxels of the target or healthy tissues, or even creating a compromise between the two.

Volumetric Modulated Arc Therapy (VMAT) is a novel RT modality, which is introduced recently for the treatment of HNC patients in the UMCG. This technique delivers radiation dose (with constant or variable rate) accurately and efficiently using a 360° range of directions in a single gantry arc [15]. In comparison to IMRT, VMAT includes a larger number of beam directions, providing a much faster treatment with reduction in the number of MU required [17], [18].

However, this technique has the drawback of requiring additional constraints on the MLC leaf motions because of the continuous motion of the gantry throughout delivery of the treatment [19]. This is one of the reasons why VMAT is still not as widely implemented as IMRT in the clinical RT departments.

Identification and application of image biomarkers for the prediction of radiotherapy treatment response in head and neck cancer patients

FACULDADE DE CIÊNCIAS

DEPARTAMENTO DE FÍSICA