Skip to main content
Download PDF
Download ePub

Determinants and mediating mechanisms of quality of life and disease-specific symptoms among thyroid cancer patients: the design of the WaTCh study

Thyroid Research volume 16, Article number: 23 (2023) Cite this article

Abstract

Background

Thyroid cancer (TC) patients are understudied but appear to be at risk for poor physical and psychosocial outcomes. Knowledge of the course and determinants of these deteriorated outcomes is lacking. Furthermore, little is known about mediating biological mechanisms.

Objectives

The WaTCh-study aims to;

  1. 1.

    Examine the course of physical and psychosocial outcomes.

  2. 2.

    Examine the association of demographic, environmental, clinical, physiological, and personality characteristics to those outcomes. In other words, who is at risk?

  3. 3.

    Reveal the association of mediating biological mechanisms (inflammation, kynurenine pathway) with poor physical and psychological outcomes. In other words, why is a person at risk?

Design and methods

Newly diagnosed TC patients from 13 Dutch hospitals will be invited. Data collection will take place before treatment, and at 6, 12 and 24 months after diagnosis. Sociodemographic and clinical information is available from the Netherlands Cancer Registry. Patients fill-out validated questionnaires at each time-point to assess quality of life, TC-specific symptoms, physical activity, anxiety, depression, health care use, and employment. Patients are asked to donate blood three times to assess inflammation and kynurenine pathway. Optionally, at each occasion, patients can use a weighing scale with bioelectrical impedance analysis (BIA) system to assess body composition; can register food intake using an online food diary; and can wear an activity tracker to assess physical activity and sleep duration/quality. Representative Dutch normative data on the studied physical and psychosocial outcomes is already available.

Impact

WaTCh will reveal the course of physical and psychosocial outcomes among TC patients over time and answers the question who is at risk for poor outcomes, and why. This knowledge can be used to provide personalized information, to improve screening, to develop and provide tailored treatment strategies and supportive care, to optimize outcomes, and ultimately increase the number of TC survivors that live in good health.

Background

Thyroid cancer (TC) is rare; the age-standardized incidence rate in Europe is 6.3 per 100.000 per year [1]. However, incidence rates are increasing [2,3,4,5]. Whereas differentiated TCs like papillary and follicular have very good prognosis with 20-year relative survival rates of 95% [6], the rarer medullary cancers have 10-year survival rates of 75–85% [7,8,9], while patients with rare anaplastic tumors often do not survive > 6 months [8]

Treatment of the majority of differentiated TC generally consists of surgery and, depending on tumour size and classification, followed by radioactive iodine therapy. This is accompanied by lifelong requirement of substitution therapy with levothyroxine, in selected patients with dosing regimens suppressing thyroid stimulating hormone (TSH) production [10, 11] causing (subclinical) hyperthyroidism. This can have profound effects on well-being [12,13,14]. For medullary or anaplastic TC, various treatment options are available that may also result in symptom relief, but little is known on its impact on patients’ lives.

Due to the good prognosis and the perceived mild treatment (that is, compared to chemotherapy for example among other malignancies), it has long been assumed that patients with differentiated TC are less distressed than other cancer patients, and that long-term quality of life (QOL) is good. Especially, impaired QOL has been reported in association with thyroxine withdrawal preceding treatment with radioactive iodide [15]. The latter is required to ensure sufficient TSH stimulation to increase the radioactive iodide uptake in thyroid (tumour) remnants. The introduction during the past decade of recombinant human TSH (rhTSH) for this indication, particularly for low-risk patients, has reduced this problem [16,17,18,19]. Nonetheless, recent studies reported much lower levels of physical and psychosocial outcomes compared to normative populations [20,21,22]. The vulnerable and understudied group of TC survivors might thus be in need for additional care [18,19,20,21,22]. Moreover, TC treatment is often based on poor quality evidence, confirmed by large treatment variation, leaving room for shared decision-making. In addition to that, most survivors require lifelong hormonal substitution therapy, which has a profound impact on physical and psychological functioning even when the TSH is kept within the normal range and needs to be adjusted individually to maintain QOL [10, 11, 13, 14, 21]. Besides the lack of knowledge on the course of physical and psychosocial outcomes over time, the role of other determinants such as diet, body weight, body composition, and physical activity, and the role of possible mediating biological mechanisms is mostly lacking for the TC survivors. We do however know that they play a role in other cancer patient populations, and they provide a lead for interventions and thus better outcomes.

To shed more light onto the outcomes of TC survivors, we set up the WaTCh-study, which stands for ‘Well-being after Thyroid Cancer’. Our main objective is to reveal the course of physical and psychosocial outcomes over time, and to answer the question who is at risk for low physical and psychosocial outcomes and why. To achieve this objective, we have formulated three complementary research directions:

Objective 1: To examine the course of physical and psychosocial outcomes (QOL, TC-specific symptoms (e.g., fatigue, sleep disturbances), physical activity, anxiety, depression, health care use, and employment) over time among TC patients. We hypothesize that these outcomes will worsen after diagnosis and during initial treatment, but that they will partially recover during follow-up. This is based on results observed in other cancer patient populations [23, 24]. However, given the lifelong requirement of thyroxine substitution that is known to have numerous effects on well-being in case of over- or under substitution, this could add another dimension to the findings that might be specific for the TC survivors.

Objective 2: To examine the role of demographic (age, sex), environmental (food intake, body weight, body composition), clinical (tumour stage, treatment), physiological (physical activity, sleep), and personality characteristics (optimism) on physical and psychosocial outcomes of TC patients. In other words, who is at risk? We expect demographic, clinical and personality characteristics to have a profound negative impact on patients’ physical and psychosocial outcomes [12, 25,26,27,28,29]. Also, we hypothesize that TC patients will report worse outcomes compared to an age- and sex matched normative population at all assessments [21, 24, 30]. However, the role of other determinants like food intake, body weight, body composition, physical activity and sleep on patients’ physical and psychosocial outcomes is less clear. Currently, no studies have investigated whether food intake is associated with physical and psychosocial outcomes in TC patients, and little is known about the influence of body composition. Some studies report weight gain under TSH suppression after total thyroidectomy [31] whereas other studies do not [32]. Furthermore, lower muscle mass due to a higher dose of thyroid hormones leads to fatigue and impaired activities and thus probably worse outcomes [33]. From studies among other cancer populations, we learned that food intake, body mass index (BMI), waist circumference, physical activity, and sleep influence health related QOL [34,35,36,37,38,39,40,41,42].

Objective 3: To reveal the association of mediating biological mechanisms (inflammation, kynurenine pathway) with poor physical and psychosocial outcomes in TC patients. In other words, why is a person at risk? To our knowledge, no studies have been performed on biological mechanisms involved in physical and psychosocial well-being among TC survivors. Nevertheless, evidence is mounting for a biological basis (i.e., increased levels of smouldering inflammation) related to experiences like fatigue, pain, and QOL among (non-)cancer populations [43,44,45,46,47,48,49]. Knowledge on underlying biological mechanisms can tailor future interventions. For example, physical activity is known to reduce pro-inflammatory cytokines related to experienced symptoms like fatigue [50]. However, the role of inflammation on physical and psychosocial outcomes is yet unknown among TC patients.

Design and methods

Conceptual model

To understand if, how and why TC survivors experience declining physical and psychosocial outcomes, we use the revised Wilson&Cleary conceptual model of patient outcomes (Fig. 1 [51, 52]). Information on the selection of determinants, underlying mediating mechanisms, and outcome variables used in WaTCh are based upon this model and are described below. Next, we describe the recruitment, study population, and the various data collection methods.

Fig. 1
figure 1

Revised Wilson & Cleary (1995) Model Adapted from Wilson, I.B., & Cleary, P.D (1995). Linking Clinical Variables with Health-Related Quality of Life: A Conceptual Model of Patient Outcomes. JAMA. 273, 59–65 (Ferrans, C. E., et al. (2005). Conceptual model of health-related quality of life. J Nurs Scholarsh. 37, 336–342.)

Full size image

Determinants

Characteristics of the individual

Characteristics of the individual in the revised Wilson&Cleary model are categorized as demographic, psychological and biological factors that influence health outcomes [51]. Numerous studies among various cancer types showed that demographic (i.e., sex, age, education, and marital status) and personality characteristics (optimism) have an influence on physical and psychosocial outcomes [25,26,27,28]. Therefore, they are included as determinants.

Characteristics of the environment

Wilson&Cleary categorize characteristics of the environment as social or physical. Social characteristics are the interpersonal or social influences (e.g. presence of a partner) on health outcomes, whereas physical characteristics are settings such as the home, neighborhood and work place [52]. We hypothesize that environmental characteristics affect health behaviour such as food choices, smoking and alcohol use. An increasing body of evidence supports the relationship between environmental factors such as (mal)nutrition and carcinogenesis [53]. Also, obesity and metabolic syndrome are linked to an increased TC risk [54]. Inflammation is also important in cancer promotion and metastasis [55]. Foods, nutrients, and dietary patterns have potential inflammatory effects reflected by the Dietary Inflammatory Index score (DII-score). A higher DII-score has been associated with higher cancer risk and mortality [56]. No studies have investigated whether individual diets based on their inflammatory potential were associated with physical and psychosocial outcomes. In addition, little is known about the influence of body composition (i.e., percentages of fat and fat free mass) on these outcomes in TC patients. Furthermore, lower muscle mass due to a higher dose of thyroid hormones to suppress TSH production leads to fatigue, weakness, and impaired daily activities [33]. Whether the method of preparation for radioactive iodine ablation therapy and changes in dose of replacement therapy influences weight and body composition and whether this affects short-term and long-term physical and psychosocial outcomes is unknown.

Mediating biological mechanisms

In the revised Wilson&Cleary model, biological factors are incorporated in the conceptualization of the relationships between symptoms and QOL as part of a continuum of biological factors at one end, and increasing in complexity to include physical and psychosocial outcomes at the other end [51]. Evidence is mounting for a biological basis of a range of subjective experiences including fatigue, pain, and overall QOL among non-cancer populations [43, 44].

Inflammation

Chronic inflammation is regarded as an "enabling" characteristic of cancer since it affects the development of cancer and promotes all stages of tumorigenesis [57, 58]. Also, inflammation plays an important role in mediating the severity of cancer-related symptoms. Inflammation is a strong mechanism underlying fatigue [44]. Fatigue, which is common among TC patients, negatively impacts other physical and psychosocial outcomes [59, 60]. Increased levels of pro-inflammatory cytokines were reported in cancer patients, and they also have effects on the central nervous system through which they modulate perception of fatigue [61]. One of the factors reducing levels of inflammation is physical activity [50].

Kynurenine pathway

The immune system is linked to the kynurenine pathway which consists of a series of linked chemical reactions converting tryptophan into a variety of substances called ‘kynurenines’ [62]. The kynurenine pathway plays an important role in how tumour cells evade immune systems [62]. As elevated pro-inflammatory cytokine levels are linked to symptoms often experienced by cancer survivors, the kynurenine pathway may be involved as well [63].

Outcomes

Symptoms, functional status, general health perceptions and overall QOL

TC and its treatment can have a profound impact on patients’ physical and psychosocial outcomes [12]. The most important outcomes, and most often assessed among other cancer patient populations, are QOL, symptoms, fatigue, sleep, physical activity, anxiety, depression, health care utilization, and employment. These constructs are our focus.

Study population

WaTCh is a longitudinal population-based study, in which all newly diagnosed adult TC patients from 13 Dutch hospitals will be invited, except those with cognitive impairment, and those not able to read or write Dutch. As medullary and anaplastic TC are very rare but understudied, they will be included, but only with the purpose to perform descriptive analyses on their well-being.

Normative data will be used to determine the functional impairment and symptom burden after TC cancer in the context of normal aging [64] and comorbidity. Each year, information on symptoms, functional decline, comorbidities and behavioral variables is collected within the CentERpanel, a longitudinal cohort of 2.000 persons without cancer, designed to be representative of the Dutch-speaking population in the Netherlands [65,66,67] generated by CentERdata (www.centerdata.nl). Of this group of 2000 persons, an age and sex-matched selection will be made to match our TC patients.

Recruitment

At the hospital, newly diagnosed TC patients will be informed by a physician. Patients receive a patient information letter and informed consent form. Informed consent forms are sent back to the PROFILES (Patient Reported Outcomes Following Initial treatment and Long term Evaluation of Survivorship) registry that coordinates this study from that moment onwards [68]. On the informed consent form, patients can indicate whether they prefer paper or online questionnaires, and whether they are interested in optional assessments (see below).

Data collection

In short, patients fill-out internationally validated questionnaires on physical and psychosocial outcomes at four occasions: before treatment, and 6, 12 and 24 months after diagnosis (Fig. 2). Patients have the option to compare their questionnaire scores to their previous scores, to scores of other TC patients, and/or a normative population (see ‘Patient feedback’ below). Patients are asked to donate blood three times (before treatment, 1 and 2 years after diagnosis). Survivors’ sociodemographic and clinical information is available from the questionnaires and the Netherlands Cancer Registry (NCR). Optionally, patients can use a weighing scale with bioelectrical impedance analysis (BIA) system to assess body composition, register food intake using an online food diary, and wear an activity tracker to assess physical activity and sleep duration and quality at all four occasions. These parts are optional to minimize patient burden, to support inclusion, and prevent dropout. Details of the data collection are described below in more detail.

Fig. 2
figure 2

Design of the WaTCh-study

Full size image

Determinants

Characteristics of the individual

Basic sociodemographic and clinical information is either self-reported or available from the Netherlands Cancer Registry, which records clinical data of all newly diagnosed cancer patients in the Netherlands. Additional clinical data (e.g., iodine status at diagnosis, number of I131 treatments, TSH, free T3, free T4, type of surgery, systemic treatment, radiotherapy, dose of radio-active iodine ablation, thyroid hormone substitution type and dose (and possibly switch), current disease status and survival) will be extracted from patients’ medical files.

Characteristics of the environment

Food intake (optional) is assessed by asking patients to register all foods and drinks in portion sizes or gram/ml, they have consumed during the day [69] during two weekdays and one weekend-day using the ‘Eetmeter’ from the Dutch ‘Voedingscentrum’ (Netherlands Nutrition Center) at each assessment. The Eetmeter is connected to the Dutch Nutrients Database (NEVO) [70, 71] so the quantity of micro- and macronutrients (i.e., kcal, fat, protein, carbohydrates, vitamins and minerals) will be calculated immediately. Patients receive a link to create an Eetmeter account. When patients have registered their food intake, results will be sent to the secure PROFILES environment [68].

BMI and weight changes are assessed by questions on body height, usual body weight, and current body weight. Also, the questionnaire includes instructions and a tape measure so that patients can assess their waist-, hip-, and calf circumference.

Body composition (optional) is assessed using special weighing scales containing a hand-to-foot BIA system using four pressure contact foot- and four hand pad electrodes (Inbody Dial H20N, Inbody Corporation Europe, Amsterdam, the Netherlands). This indirect method for assessing body composition is based on the electrical conductivity of the different compartments of the human body [72]. BIA measurement can determine the amount of intracellular, extracellular, and total body water. Because fat free mass contains virtually all the body water, BIA methods can be used to estimate non-fat compartments [73]. The InBody DIAL H20N is a two-frequency BIA which sends a current through the body with two frequencies. By using specific formula, fat free mass can be estimated, fat mass is calculated by subtracting fat free mass from body weight. The Inbody Dial is provided 2 weeks at each assessment. Participants are then asked to use the weighing scale each morning immediately after awakening and voiding.

Smoking is assessed by standardized questions on smoking habits (i.e., cigarettes/shag, cigars, pipe tobacco, or e-cigarettes; number of years; number each day). We classify TC survivors in; never, ex, and current smokers.

Biological mechanisms: inflammation and kynurenine pathway

Blood samples are collected by vena puncture before treatment, 12 and 24 months after diagnosis at the hospitals among all patients using a standard protocol. Participants receive a lab form and a short questionnaire. This questionnaire contains questions concerning medication use, and sickness at the time of blood sampling, as these factors can have an impact on the biological markers of interest. Time of blood donation will be marked on the questionnaire. Directly after blood sampling, the serum blood sample will be allowed to clot at room temperature and is centrifuged. The EDTA blood sample will be centrifuged at room temperature directly after blood sampling. The subtracted plasma, serum, and buffy coat samples will be processed within 2 h of collection and are stored at –80 °C until further analyses. After local processing and temporal storage, the samples will be stored centrally at Biobank Maastricht.

Following, appropriate ELISA and ILLUMINA analyses will take place to determine the biological markers. Blood samples will be stored in the biobank for later analyses of biomarkers.

Outcomes

Psychosocial outcomes will be assessed with internationally validated questionnaires to assess QOL (EORCT QLQ-C30 [74]), TC-specific symptoms (EORTC QLQ-THY34 [75]), psychological distress (Hospital Anxiety and Depression Scale [76]), fatigue (Multidimensional Fatigue Inventory [77]), sleep (Pittsburgh Sleep Quality Index [78]), personality (optimism, Life orientation test [79]), and comorbidity (Self-administered Comorbidity Questionnaire [80]).

Patients have the option to wear the Fitbit Inspire HR, an activity tracker, which they can keep after the study for personal use as an incentive for participating in the study. They wear the Fitbit for 2 weeks on their non-dominant arm, day, and night. It assesses energy expenditure, metabolic equivalent (MET), exercise intensity, sedentary bouts, activity bouts, steps taken, heart rate, sleep duration, sleep latency, wake after sleep onset, and sleep efficiency.

Patient feedback

Within WaTCh, we aim to inform patients about TC survivorship for their specific situation: patients can choose to compare their questionnaire scores to their previous scores, to scores of other TC patients, and/or a normative population to see whether their scores are average or not using a traffic light model [81, 82]. Patients with a score in the red part of the chart, so a score that is too high, receive the advice to contact their doctor. Patients are assisted in understanding the graphs. Feedback works well in our other studies; it empowers patients and it is an incentive to participate [83].

Data analyses

Central to WaTCh is a deep phenotyping approach, meaning that for all patients data will be collected not only at multiple time points but also from different viewpoints (psychological, clinical, environmental, demographic, biological). Jointly analysing these data and making the results insightful requires advanced modelling techniques. To answer the question that belongs to our first objective, we will model physical and psychosocial outcomes over time using multilevel repeated measures models (this is, growth curve models). In addition, data can be explored for subpopulations that have similar profiles over time using latent class variants thereof [84]. A similar type of modelling approach will be used to answer objective two but including covariates. Variants of structural equation models will be used that allow to obtain insight in data consisting of large and multiple collections of variables to answer the question belonging to objective three. Examples of such methods are sparse partial least squares and covariates regression [85, 86].

Sample size

About 625 Dutch TC patients are diagnosed each year (389 in the included 13 hospitals). Response rates in comparable PROFILES studies are 60% at baseline and 90% at subsequent measurements. Hence, we expect that at least 233 TC survivors respond at baseline, and 210, 189 and 170 respondents respectively at 6, 12 and 24 months after diagnosis which will be sufficient to answer our research questions. In short, to assess the course of physical and psychosocial outcomes over time, 17 participants are necessary to detect a within subjects effect on QOL of d = 0.5, with a power of 0.80, and an alpha of 0,5, given the correlation of 0.6 between the 4 measurement occasions. To assess who is at risk for poor outcomes (i.e., QOL), our power analysis indicates that we need at least 152 participants to detect small to medium effects with a power of 0.80. Regarding our third objective, we conducted a power-analysis concerning the hypothesis that depression is associated with C-reactive protein (CRP) over time. For 3 CRP assessments, 2 depression groups and a correlation between the CRP assessments of 0.5, we need 114 participants to discover an interaction effect of d = 0.22 with a power of 0.80.

Data security/disclosure of original documents

Confidentiality and anonymity of participants will be guaranteed by assigning a study number to each participant. Collected data will all be stored in a secured location (PROFILES registry) for 15 years. PROFILES obtained the Data Seal of Approval in 2016 [87]. The anonymized blood samples will be stored at the Biobank Maastricht.

Discussion

WaTCh will reveal the course of physical and psychosocial outcomes among TC patients over time and help answer the question who is at risk for low physical and psychosocial outcomes, and why. This information can be used to inform patients about TC survivorship for their specific situation. Knowing who is at risk for low physical and psychosocial outcomes is also crucial since most patients require lifelong substitution therapy that needs to be adjusted individually. Understanding the driving mechanisms and identifying those at risk for poor outcomes improves personalized treatment, and follow-up. Furthermore, this knowledge can be used to improve screening for physical and psychosocial problems and for developing interventions that could be applied early during the treatment trajectory to optimize outcomes and increase the number of TC survivors that live in good health.

Trial status

The inclusion of patients started in September 2020. The COVID-19 pandemic delayed patient inclusion.

Availability of data and materials

After finishing the data collection, the data will be freely available for non-commercial scientific research, subject to study question, privacy and confidentiality restrictions, and registration (www.profilesregistry.nl) and the statistical code through www.github.com. This will enable researchers from many different disciplines answer their research questions which will help facilitate dissemination of results and increase the impact of our findings among this vulnerable and understudied population.

Abbreviations

BIA:

Bioelectrical impedance analysis

BMI:

Body mass index

CRP:

C-reactive protein

DII score:

Dietary Inflammatory Index score

MET:

Metabolic equivalent

NCR:

Netherlands Cancer Registry

NEVO:

Dutch Nutrients Database

PROFILES:

Patient Reported Outcomes Following Initial treatment and Long term Evaluation of Survivorship

QOL:

Quality of life

TC:

Thyroid cancer

TSH:

Thyroid stimulating hormone

References

  1. Ferlay J, et al. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2013;49(6):1374–403.

    Article  CAS  PubMed  Google Scholar 

  2. Amphlett B, et al. Recent trends in the incidence, geographical distribution, and survival from thyroid cancer in Wales, 1985–2010. Thyroid. 2013;23(11):1470–8.

    Article  PubMed  Google Scholar 

  3. Husson O, et al. Rising incidence, no change in survival and decreasing mortality from thyroid cancer in The Netherlands since 1989. Endocr Relat Cancer. 2013;20(2):263–71.

    Article  PubMed  Google Scholar 

  4. Carlberg M, et al. Increasing incidence of thyroid cancer in the Nordic countries with main focus on Swedish data. BMC Cancer. 2016;16:426.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Morris LG, Tuttle RM, Davies L. Changing Trends in the Incidence of Thyroid Cancer in the United States. JAMA Otolaryngol Head Neck Surg. 2016;142(7):709–11.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Brenner H. Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis. Lancet. 2002;360(9340):1131–5.

    Article  PubMed  Google Scholar 

  7. Wu LM, et al. The accuracy of ultrasonography in the preoperative diagnosis of cervical lymph node metastasis in patients with papillary thyroid carcinoma: A meta-analysis. Eur J Radiol. 2012;81(8):1798–805.

    Article  PubMed  Google Scholar 

  8. Brown RL, de Souza JA, Cohen EE. Thyroid cancer: burden of illness and management of disease. J Cancer. 2011;2:193–9.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pacini F, et al. Medullary thyroid carcinoma. Clin Oncol (R Coll Radiol). 2010;22(6):475–85.

    Article  CAS  PubMed  Google Scholar 

  10. Pacini F, et al. Thyroid cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2010;21(Suppl 5):v214–9.

    Article  PubMed  Google Scholar 

  11. Links TP, et al. Guideline “Differentiated thyroid carcinoma”, including diagnosis of thyroid nodules. Ned Tijdschr Geneeskd. 2007;151(32):1777–82.

    CAS  PubMed  Google Scholar 

  12. Husson O, et al. Health-related quality of life among thyroid cancer survivors: a systematic review. Clin Endocrinol (Oxf). 2011;75(4):544–54.

    Article  PubMed  Google Scholar 

  13. Applewhite MK, et al. Quality of Life in Thyroid Cancer is Similar to That of Other Cancers with Worse Survival. World J Surg. 2016;40(3):551–61.

    Article  PubMed  Google Scholar 

  14. Gamper EM, et al. Persistent quality of life impairments in differentiated thyroid cancer patients: results from a monitoring programme. Eur J Nucl Med Mol Imaging. 2015;42(8):1179–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Duntas LH, Biondi B. Short-term hypothyroidism after Levothyroxine-withdrawal in patients with differentiated thyroid cancer: clinical and quality of life consequences. Eur J Endocrinol. 2007;156(1):13–9.

    Article  CAS  PubMed  Google Scholar 

  16. Dow KH, Ferrell BR, Anello C. Quality-of-life changes in patients with thyroid cancer after withdrawal of thyroid hormone therapy. Thyroid. 1997;7(4):613–9.

    Article  CAS  PubMed  Google Scholar 

  17. Borget I, et al. Sick leave for follow-up control in thyroid cancer patients: comparison between stimulation with Thyrogen and thyroid hormone withdrawal. Eur J Endocrinol. 2007;156(5):531–8.

    Article  CAS  PubMed  Google Scholar 

  18. Crevenna R, et al. Quality of life in patients with non-metastatic differentiated thyroid cancer under thyroxine supplementation therapy. Support Care Cancer. 2003;11(9):597–603.

    Article  PubMed  Google Scholar 

  19. Schroeder PR, et al. A comparison of short-term changes in health-related quality of life in thyroid carcinoma patients undergoing diagnostic evaluation with recombinant human thyrotropin compared with thyroid hormone withdrawal. J Clin Endocrinol Metab. 2006;91(3):878–84.

    Article  CAS  PubMed  Google Scholar 

  20. Roerink SH, et al. High level of distress in long-term survivors of thyroid carcinoma: results of rapid screening using the distress thermometer. Acta Oncol. 2013;52(1):128–37.

    Article  PubMed  Google Scholar 

  21. Husson O, et al. Health-related quality of life and disease specific symptoms in long-term thyroid cancer survivors: a study from the population-based PROFILES registry. Acta Oncol. 2013;52(2):249–58.

    Article  PubMed  Google Scholar 

  22. Singer S, et al. Quality of Life in Patients with Thyroid Cancer Compared with the General Population. Thyroid. 2012;22(2):117–24.

    Article  PubMed  Google Scholar 

  23. Bonhof CS, et al. The course of peripheral neuropathy and its association with health-related quality of life among colorectal cancer patients. J Cancer Surviv. 2021;15(2):190–200.

    Article  PubMed  Google Scholar 

  24. Husson O, et al. Health-Related Quality of Life in Adolescent and Young Adult Patients With Cancer: A Longitudinal Study. J Clin Oncol. 2017;35(6):652–9.

    Article  PubMed  Google Scholar 

  25. Misra S, et al. Patients’ experiences following local-regional recurrence of thyroid cancer: a qualitative study. J Surg Oncol. 2013;108(1):47–51.

    Article  PubMed  Google Scholar 

  26. Kung S, et al. Association of optimism-pessimism with quality of life in patients with head and neck and thyroid cancers. Mayo Clin Proc. 2006;81(12):1545–52.

    Article  PubMed  Google Scholar 

  27. Aschebrook-Kilfoy B, et al. Risk Factors for Decreased Quality of Life in Thyroid Cancer Survivors: Initial Findings from the North American Thyroid Cancer Survivorship Study. Thyroid. 2015;25(12):1313–21.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Han KT, et al. Associations between quality of life and marital status in cancer patients and survivors. Asian Pac J Cancer Prev. 2014;15(13):5287–91.

    Article  PubMed  Google Scholar 

  29. Verhaar S, Vissers PA, Maas H, van de Poll-Franse LV, van Erning FN, Mols F. Treatment-related differences in health related quality of life and disease specific symptoms among colon cancer survivors: results from the population-based PROFILES registry. Eur J Cancer. 2015;51(10):1263–73. https://doi.org/10.1016/j.ejca.2015.04.004.

  30. Mols F, et al. Age-related differences in health-related quality of life among thyroid cancer survivors compared with a normative sample: Results from the PROFILES Registry. Head Neck. 2018;40(10):2235–45.

    Article  PubMed  Google Scholar 

  31. Sohn SY, et al. Weight Changes in Patients with Differentiated Thyroid Carcinoma during Postoperative Long-Term Follow-up under Thyroid Stimulating Hormone Suppression. Endocrinol Metab (Seoul). 2015;30(3):343–51.

    Article  CAS  PubMed  Google Scholar 

  32. Weinreb JT, Yang Y, Braunstein GD. Do patients gain weight after thyroidectomy for thyroid cancer? Thyroid. 2011;21(12):1339–42.

    Article  CAS  PubMed  Google Scholar 

  33. Vigario Pdos S, et al. Effects of physical activity on body composition and fatigue perception in patients on thyrotropin-suppressive therapy for differentiated thyroid carcinoma. Thyroid. 2011;21(7):695–700.

    Article  PubMed  Google Scholar 

  34. Revesz D, et al. Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains. BMC Med Inform Decis Mak. 2020;20(1):54.

    Article  PubMed  PubMed Central  Google Scholar 

  35. van Veen MR, Mols F, Bours MJL, Weijenberg MP, Kampman E, Beijer S. Adherence to the World Cancer Research Fund/American Institute for Cancer Research recommendations for cancer prevention is associated with better health-related quality of life among long-term colorectal cancer survivors: results of the PROFILES registry. Support Care Cancer. 2019;27(12):4565–74. https://doi.org/10.1007/s00520-019-04735-y.

  36. Vissers PAJ, et al. The Impact of Body Mass Index and Waist Circumference on Health-related Quality of Life Among Colorectal Cancer Survivors: Results from the PROFILES Registry. Nutr Cancer. 2017;69(8):1177–84.

    Article  PubMed  Google Scholar 

  37. Vissers PA, et al. Prospectively measured lifestyle factors and BMI explain differences in health-related quality of life between colorectal cancer patients with and without comorbid diabetes. Support Care Cancer. 2016;24(6):2591–601.

    Article  PubMed  Google Scholar 

  38. Oldenburg CS, et al. The relationship of body mass index with quality of life among endometrial cancer survivors: A study from the population-based PROFILES registry. Gynecol Oncol. 2013;129(1):216–21.

    Article  PubMed  Google Scholar 

  39. Husson O, et al. High levels of physical activity are associated with lower levels of fatigue among lymphoma patients: Results from the longitudinal PROFILES registry. Acta Oncol. 2015;54(5):678–84.

    Article  PubMed  Google Scholar 

  40. Oerlemans S, Issa DE, van den Broek EC, Nijziel MR, Coebergh JW, Huijgens PC, Mols F, van de Poll-Franse LV. Health-related quality of life and persistent symptoms in relation to (R-)CHOP14, (R-)CHOP21, and other therapies among patients with diffuse large B-cell lymphoma: results of the population-based PHAROS-registry. Ann Hematol. 2014;93(10):1705–15. https://doi.org/10.1007/s00277-014-2099-8.

  41. Oerlemans S, et al. A high level of fatigue among long-term survivors of non-Hodgkin’s lymphoma: results from the longitudinal population-based PROFILES registry in the south of the Netherlands. Haematologica. 2013;98(3):479–86.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Thong MSY, et al. Identifying the subtypes of cancer-related fatigue: results from the population-based PROFILES registry. J Cancer Surviv. 2018;12(1):38–46.

    Article  PubMed  Google Scholar 

  43. Sprangers MA, et al. Scientific imperatives, clinical implications, and theoretical underpinnings for the investigation of the relationship between genetic variables and patient-reported quality-of-life outcomes. Qual Life Res. 2010;19(10):1395–403.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Sprangers MA, et al. Biological pathways, candidate genes, and molecular markers associated with quality-of-life domains: an update. Qual Life Res. 2014;23(7):1997–2013.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Taylor PN, et al. Clinical review: A review of the clinical consequences of variation in thyroid function within the reference range. J Clin Endocrinol Metab. 2013;98(9):3562–71.

    Article  CAS  PubMed  Google Scholar 

  46. Wartofsky L. Combination L-T3 and L-T4 therapy for hypothyroidism. Curr Opin Endocrinol Diabetes Obes. 2013;20(5):460–6.

    Article  CAS  PubMed  Google Scholar 

  47. Panicker V, et al. Common variation in the DIO2 gene predicts baseline psychological well-being and response to combination thyroxine plus triiodothyronine therapy in hypothyroid patients. J Clin Endocrinol Metab. 2009;94(5):1623–9.

    Article  CAS  PubMed  Google Scholar 

  48. Torlontano M, et al. Type 2 deiodinase polymorphism (threonine 92 alanine) predicts L-thyroxine dose to achieve target thyrotropin levels in thyroidectomized patients. J Clin Endocrinol Metab. 2008;93(3):910–3.

    Article  CAS  PubMed  Google Scholar 

  49. Butler PW, et al. The Thr92Ala 5’ type 2 deiodinase gene polymorphism is associated with a delayed triiodothyronine secretion in response to the thyrotropin-releasing hormone-stimulation test: a pharmacogenomic study. Thyroid. 2010;20(12):1407–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ballard-Barbash R, et al. Physical activity, biomarkers, and disease outcomes in cancer survivors: a systematic review. J Natl Cancer Inst. 2012;104(11):815–40.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA. 1995;273(1):59–65.

    Article  CAS  PubMed  Google Scholar 

  52. Ferrans CE, et al. Conceptual model of health-related quality of life. J Nurs Scholarsh. 2005;37(4):336–42.

    Article  PubMed  Google Scholar 

  53. Zitvogel L, Pietrocola F, Kroemer G. Nutrition, inflammation and cancer. Nat Immunol. 2017;18(8):843–50.

    Article  CAS  PubMed  Google Scholar 

  54. Matrone A, et al. Obesity as a risk factor for thyroid cancer. Curr Opin Endocrinol Diabetes Obes. 2020;27(5):358–63.

    Article  CAS  PubMed  Google Scholar 

  55. Greten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51(1):27–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zahedi H, et al. A Higher Dietary Inflammatory Index Score is Associated with a Higher Risk of Incidence and Mortality of Cancer: A Comprehensive Systematic Review and Meta-Analysis. Int J Prev Med. 2020;11:15.

    PubMed  PubMed Central  Google Scholar 

  57. Muzza M, et al. The tight relationship between papillary thyroid cancer, autoimmunity and inflammation: clinical and molecular studies. Clin Endocrinol (Oxf). 2010;72(5):702–8.

    Article  CAS  PubMed  Google Scholar 

  58. Thapa D, Ghosh R. Chronic inflammatory mediators enhance prostate cancer development and progression. Biochem Pharmacol. 2015;94(2):53–62.

    Article  CAS  PubMed  Google Scholar 

  59. Husson O, et al. Fatigue among short- and long-term thyroid cancer survivors: results from the population-based PROFILES registry. Thyroid. 2013;23(10):1247–55.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Husson O, et al. Variation in fatigue among 6011 (long-term) cancer survivors and a normative population: a study from the population-based PROFILES registry. Support Care Cancer. 2015;23(7):2165–74.

    Article  PubMed  Google Scholar 

  61. Roerink ME, et al. Interleukin-1 as a mediator of fatigue in disease: a narrative review. J Neuroinflammation. 2017;14(1):16.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Gouasmi R, Ferraro-Peyret C, Nancey S, Coste I, Renno T, Chaveroux C, Aznar N, Ansieau S. The Kynurenine Pathway and Cancer: Why Keep It Simple When You Can Make It Complicated. Cancers (Basel). 2022;14(11):2793. https://doi.org/10.3390/cancers14112793.

  63. Li H, et al. Systematic Review of the Kynurenine Pathway and Psychoneurological Symptoms Among Adult Cancer Survivors. Biol Res Nurs. 2020;22(4):472–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Gallicchio L, Tonorezos E, de Moor JS, Elena J, Farrell M, Green P, Mitchell SA, Mollica MA, Perna F, Saiontz NG, Zhu L, Rowland J, Mayer DK. Evidence Gaps in Cancer Survivorship Care: A Report From the 2019 National Cancer Institute Cancer Survivorship Workshop. J Natl Cancer Inst. 2021;113(9):1136–42. https://doi.org/10.1093/jnci/djab049.

  65. van de Poll-Franse LV, Mols F, Gundy CM, Creutzberg CL, Nout RA, Verdonck-de Leeuw IM, Taphoorn MJ, Aaronson NK. Normative data for the EORTC QLQ-C30 and EORTC-sexuality items in the general Dutch population. Eur J Cancer. 2011;47(5):667–75. https://doi.org/10.1016/j.ejca.2010.11.004.

  66. Mols F, et al. Reference data of the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-CIPN20 Questionnaire in the general Dutch population. Eur J Cancer. 2016;69:28–38.

    Article  PubMed  Google Scholar 

  67. Mols F, Husson O, Oudejans M, Vlooswijk C, Horevoorts N, van de Poll-Franse LV. Reference data of the EORTC QLQ-C30 questionnaire: five consecutive annual assessments of approximately 2000 representative Dutch men and women. Acta Oncol. 2018;57(10):1381–91. https://doi.org/10.1080/0284186X.2018.1481293.

  68. van de Poll-Franse LV, Horevoorts N, Schoormans D, Beijer S, Ezendam NPM, Husson O, Oerlemans S, Schagen SB, Hageman GJ, Van Deun K, van den Hurk C, van Eenbergen M, Mols F; PROFILES Registry Group. Measuring Clinical, Biological, and Behavioral Variables to Elucidate Trajectories of Patient-Reported Outcomes: The PROFILES Registry. J Natl Cancer Inst. 2022;114(6):800–7. https://doi.org/10.1093/jnci/djac047.

  69. Eetmeter. The Netherlands Nutrition Centre, the Hague [cited 2019; Available from: https://mijn.voedingscentrum.nl/nl/eetmeter/.

  70. Dutch Food Composition Database (NEVO). . National Institute for Public Health and the Environment (RIVM), Bilthoven NEVO online version 2016/5.0; Available from: https://nevo-online.rivm.nl/.

  71. NEVO-online 2016: background information Dutch Food Composition Database 2016. . National Institute for Public Health and the Environment (RIVM), Bilthoven, 2016; Available from: https://www.rivm.nl/sites/default/files/2018-11/NEVO%20online%202016.%20Background%20information_final_13-9-2016.pdf.

  72. Kyle UG, et al. Bioelectrical impedance analysis–part I: review of principles and methods. Clin Nutr. 2004;23(5):1226–43.

    Article  PubMed  Google Scholar 

  73. Sergi G, et al. Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res. 2017;29(4):591–7.

    Article  PubMed  Google Scholar 

  74. Aaronson NK, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993;85(5):365–76.

    Article  CAS  PubMed  Google Scholar 

  75. Singer S, et al. The EORTC module for quality of life in patients with thyroid cancer: phase III. Endocr Relat Cancer. 2017;24(4):197–207.

    Article  PubMed  Google Scholar 

  76. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67(6):361–70.

    Article  CAS  PubMed  Google Scholar 

  77. Smets EM, et al. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res. 1995;39(3):315–25.

    Article  CAS  PubMed  Google Scholar 

  78. Buysse DJ, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

    Article  CAS  PubMed  Google Scholar 

  79. Glaesmer H, et al. Psychometric properties and population-based norms of the Life Orientation Test Revised (LOT-R). Br J Health Psychol. 2012;17(2):432–45.

    Article  PubMed  Google Scholar 

  80. Sangha O, et al. The Self-Administered Comorbidity Questionnaire: a new method to assess comorbidity for clinical and health services research. Arthritis Rheum. 2003;49(2):156–63.

    Article  PubMed  Google Scholar 

  81. Brundage M, et al. Communicating quality of life information to cancer patients: a study of six presentation formats. J Clin Oncol. 2005;23(28):6949–56.

    Article  CAS  PubMed  Google Scholar 

  82. Kuijpers W, et al. Patients’ and health professionals’ understanding of and preferences for graphical presentation styles for individual-level EORTC QLQ-C30 scores. Qual Life Res. 2016;25(3):595–604.

    Article  CAS  PubMed  Google Scholar 

  83. Oerlemans S, et al. “Am I normal?” The Wishes of Patients With Lymphoma to Compare Their Patient-Reported Outcomes With Those of Their Peers. J Med Internet Res. 2017;19(8): e288.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Clouth FJ, et al. Heterogeneity in Quality of Life of Long-Term Colon Cancer Survivors: A Latent Class Analysis of the Population-Based PROFILES Registry. Oncologist. 2021;26(3):e492–e499.

  85. Singh A, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35(17):3055–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Van Deun K, Crompvoets EAV, Ceulemans E. Obtaining insights from high-dimensional data: sparse principal covariates regression. BMC Bioinformatics. 2018;19(1):104.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Leeuw, L.d.D. Data Seal of Approval (DSA). . 2019; Available from: https://doi.org/10.17026/dans-28z-njxq.

Download references

Acknowledgements

We would like to thank all doctors, nurses, and research coordinators involved with the WaTCh-study in the following hospitals and institutions for their cooperation: Radboud University Medical Center, Nijmegen; University Medical Center Groningen, Groningen, Maastricht University Medical Center, Maastricht; Maxima Medical Center, Veldhoven; Noordwest Ziekenhuisgroep, Alkmaar; Zuyderland MC hospital, Sittard; Catharina hospital, Eindhoven; Antoni van Leeuwenhoek Hospital/Netherlands Cancer Institute, Amsterdam; Albert Schweitzer Hospital, Dordrecht; University Medical Center Utrecht, Utrecht; Elisabeth TweeSteden hospital, Tilburg; Rijnstate, Arnhem; CWZ, Nijmegen.

Funding

The study is supported with an Investment Subsidy Large (#91101002) of the Netherlands Organization for Scientific Research (The Hague, The Netherlands). This funding body did not have any role in the design of this study, and did not have any role in the collection, analyses and interpretation of data and in writing any of the manuscripts that will result from this study.

Author information

Authors and Affiliations

  1. CoRPS - Center of Research On Psychological Disorders and Somatic Diseases, Department of Medical and Clinical Psychology, Tilburg University, Tilburg, the Netherlands

    Floortje Mols, Dounya Schoormans & Lonneke van de Poll-Franse

  2. Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands

    Floortje Mols, Sandra Beijer & Lonneke van de Poll-Franse

  3. Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

    Romana Netea-Maier

  4. Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Olga Husson & Lonneke van de Poll-Franse

  5. Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Olga Husson

  6. Department of Surgical Oncology, Erasmus Medical Center, Rotterdam, the Netherlands

    Olga Husson

  7. Maastricht University Medical Center (MUMC), Maastricht, the Netherlands

    Sandra Beijer & Marleen Kars

  8. Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands

    Katrijn Van Deun

  9. Department of Endocrinology, Groningen University, University Medical Center Groningen, Groningen, The Netherlands

    Wouter Zandee

  10. Maxima Medical Center, Veldhoven, The Netherlands

    Pleun C. M. Wouters van Poppel

  11. Noordwest Ziekenhuisgroep, Alkmaar, The Netherlands

    Suat Simsek

  12. Zuyderland MC Hospital, Heerlen, The Netherlands

    Patrick van Battum

  13. Catharina Hospital, Eindhoven, The Netherlands

    Jérôme M. H. Kisters

  14. Antoni Van Leeuwenhoek Hospital, Netherlands Cancer Institute, Amsterdam, The Netherlands

    Jan Paul de Boer

  15. Albert Schweitzer Hospital, Dordrecht, The Netherlands

    Elske Massolt

  16. Department of Endocrine Oncology, University Medical Center Utrecht, Utrecht, The Netherlands

    Rachel van Leeuwaarde

  17. ETZ, Tilburg, The Netherlands

    Wilma Oranje

  18. Rijnstate, Arnhem, The Netherlands

    Sean Roerink

  19. CWZ, Nijmegen, The Netherlands

    Mechteld Vermeulen

Authors
  1. Floortje Mols
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dounya Schoormans
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Romana Netea-Maier
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olga Husson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sandra Beijer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Katrijn Van Deun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wouter Zandee
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Marleen Kars
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Pleun C. M. Wouters van Poppel
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Suat Simsek
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Patrick van Battum
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jérôme M. H. Kisters
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jan Paul de Boer
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Elske Massolt
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Rachel van Leeuwaarde
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Wilma Oranje
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sean Roerink
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Mechteld Vermeulen
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Lonneke van de Poll-Franse
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FM designed the study in collaboration with OH, LP, RNM, DS, SB, KVD. The following authors contributed to the data collection: FM, DS, RNM, SB, WZ, MK, PWP, SS, PvB, JK, JPdB, MS, EM, RvL, WO, SR, and MV. FM drafted the manuscript. All authors provided input into revisions of the manuscript and have approved the final manuscript.

Corresponding author

Correspondence to Floortje Mols.

Ethics declarations

Ethics approval and consent to participate

WaTCh received approval from the ‘Medisch Ethische Toetsingscommissie Brabant’ (NL65161.028.18). In addition, the study has been reviewed and approved by the local ethics committees of the participating centers. All participants will provide written informed consent prior to participation.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mols, F., Schoormans, D., Netea-Maier, R. et al. Determinants and mediating mechanisms of quality of life and disease-specific symptoms among thyroid cancer patients: the design of the WaTCh study. Thyroid Res 16, 23 (2023). https://doi.org/10.1186/s13044-023-00165-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13044-023-00165-5

Keywords

Thyroid Research

ISSN: 1756-6614