The Role of Disease Label in Patient Perceptions and Treatment Decisions in the Setting of Low-Risk Malignant Neoplasms | Oncology | JAMA Oncology | JAMA Network
[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.  Visual Aids Used in Discrete Choice Experiment
Visual Aids Used in Discrete Choice Experiment

Participants were introduced to the hypothetical scenario (A) and treatment risks (B). These materials could be reviewed at any time. Each respondent indicated preference for 8 choice sets, an example of which is shown in panel C.

Figure 2.  Population Average Preference Weights for Each Attribute Level
Population Average Preference Weights for Each Attribute Level

Positive values indicate preference for the labeled level, and negative values indicate avoidance. The circles represent the posterior means and the error bars show 95% credible intervals.

Figure 3.  Violin Plots for Marginal Rates of Substitution
Violin Plots for Marginal Rates of Substitution

Marginal rates of substitution (MRS) are presented for nodule vs cancer (A) and tumor vs cancer (B) relative to preferences for progression risk and management options. An MRS of 1.0 indicates that changing the label to cancer made respondents equally likely to avoid the scenario as increasing the risk of progression to 5% or changing the management to surgery, and an MRS of greater than 1.0 means that the label was more influential than the indicated comparison. The posterior median is indicated by the central horizontal black line, the 95% credible intervals are indicated by the horizontal black lines above and below the posterior median, and the widths of the curved shapes represent probability density.

Table.  Self-reported Characteristics of Study Participants
Self-reported Characteristics of Study Participants
1.
Robb  KA, Simon  AE, Miles  A, Wardle  J.  Public perceptions of cancer: a qualitative study of the balance of positive and negative beliefs.  BMJ Open. 2014;4(7):e005434. doi:10.1136/bmjopen-2014-005434PubMedGoogle ScholarCrossref
2.
Strickland  KC, Eszlinger  M, Paschke  R,  et al.  Molecular testing of nodules with a suspicious or malignant cytologic diagnosis in the setting of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP).  Endocr Pathol. 2018;29(1):68-74. doi:10.1007/s12022-018-9515-xPubMedGoogle ScholarCrossref
3.
Esserman  LJ, Thompson  IM, Reid  B,  et al.  Addressing overdiagnosis and overtreatment in cancer: a prescription for change.  Lancet Oncol. 2014;15(6):e234-e242. doi:10.1016/S1470-2045(13)70598-9PubMedGoogle ScholarCrossref
4.
Howlader  N, Noone  AM, Krapcho  M,  et al, eds. SEER cancer statistics review, 1975-2014, National Cancer Institute. Bethesda, MD, based on November 2016 SEER data submission posted to the SEER website, April 2017. https://seer.cancer.gov/csr/1975_2014/. Accessed January 3, 2018.
5.
Haugen  BR, Alexander  EK, Bible  KC,  et al.  2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer.  Thyroid. 2016;26(1):1-133. doi:10.1089/thy.2015.0020PubMedGoogle ScholarCrossref
6.
Topstad  D, Dickinson  JA.  Thyroid cancer incidence in Canada: a national cancer registry analysis.  CMAJ Open. 2017;5(3):E612-E616. doi:10.9778/cmajo.20160162PubMedGoogle ScholarCrossref
7.
Nikiforov  YE, Seethala  RR, Tallini  G,  et al.  Nomenclature revision for encapsulated follicular variant of papillary thyroid carcinoma: a paradigm shift to reduce overtreatment of indolent tumors.  JAMA Oncol. 2016;2(8):1023-1029. doi:10.1001/jamaoncol.2016.0386PubMedGoogle ScholarCrossref
8.
Krishnamurti  T, Woloshin  S, Schwartz  LM, Fischhoff  B.  A randomized trial testing US Food And Drug Administration “breakthrough” language.  JAMA Intern Med. 2015;175(11):1856-1858. doi:10.1001/jamainternmed.2015.5355PubMedGoogle ScholarCrossref
9.
Oda  H, Miyauchi  A, Ito  Y,  et al.  Incidences of unfavorable events in the management of low-risk papillary microcarcinoma of the thyroid by active surveillance versus immediate surgery.  Thyroid. 2016;26(1):150-155. doi:10.1089/thy.2015.0313PubMedGoogle ScholarCrossref
10.
de Bekker-Grob  EW, Donkers  B, Jonker  MF, Stolk  EA.  Sample size requirements for discrete-choice experiments in healthcare: a practical guide.  Patient. 2015;8(5):373-384. doi:10.1007/s40271-015-0118-zPubMedGoogle ScholarCrossref
11.
Nelson  HD, Pappas  M, Cantor  A, Griffin  J, Daeges  M, Humphrey  L.  Harms of breast cancer screening: systematic review to update the 2009 U.S. Preventive Services Task Force recommendation.  Ann Intern Med. 2016;164(4):256-267. doi:10.7326/M15-0970PubMedGoogle ScholarCrossref
12.
Oeffinger  KC, Fontham  ETH, Etzioni  R,  et al; American Cancer Society.  Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society.  JAMA. 2015;314(15):1599-1614. doi:10.1001/jama.2015.12783PubMedGoogle ScholarCrossref
13.
Glusac  EJ.  The melanoma ‘epidemic’: lessons from prostate cancer.  J Cutan Pathol. 2012;39(1):17-20. doi:10.1111/j.1600-0560.2011.01848.xPubMedGoogle ScholarCrossref
14.
Patz  EF  Jr, Pinsky  P, Gatsonis  C,  et al; NLST Overdiagnosis Manuscript Writing Team.  Overdiagnosis in low-dose computed tomography screening for lung cancer.  JAMA Intern Med. 2014;174(2):269-274. doi:10.1001/jamainternmed.2013.12738PubMedGoogle ScholarCrossref
15.
Vickers  AJ, Sjoberg  DD, Ulmert  D,  et al.  Empirical estimates of prostate cancer overdiagnosis by age and prostate-specific antigen.  BMC Med. 2014;12:26. doi:10.1186/1741-7015-12-26PubMedGoogle ScholarCrossref
16.
Kasperson  RE, Renn  O, Slovic  P,  et al.  The social amplification of risk: a conceptual framework.  Risk Anal. 1988;8(2):177-187. doi:10.1111/j.1539-6924.1988.tb01168.xGoogle ScholarCrossref
17.
Omer  ZB, Hwang  ES, Esserman  LJ, Howe  R, Ozanne  EM.  Impact of ductal carcinoma in situ terminology on patient treatment preferences.  JAMA Intern Med. 2013;173(19):1830-1831. doi:10.1001/jamainternmed.2013.8405PubMedGoogle ScholarCrossref
18.
Nickel  B, Barratt  A, McGeechan  K,  et al.  Effect of a change in papillary thyroid cancer terminology on anxiety levels and treatment preferences: a randomized crossover trial.  JAMA Otolaryngol Head Neck Surg. 2018;144(10):867-874. doi:10.1001/jamaoto.2018.1272PubMedGoogle ScholarCrossref
19.
Jansen  J, Butow  PN, van Weert  JCM,  et al.  Does age really matter? recall of information presented to newly referred patients with cancer.  J Clin Oncol. 2008;26(33):5450-5457. doi:10.1200/JCO.2007.15.2322PubMedGoogle ScholarCrossref
20.
Sawka  AM, Ghai  S, Tomlinson  G,  et al.  A protocol for a Canadian prospective observational study of decision-making on active surveillance or surgery for low-risk papillary thyroid cancer.  BMJ Open. 2018;8(4):e020298. doi:10.1136/bmjopen-2017-020298PubMedGoogle ScholarCrossref
21.
Miyauchi  A, Kudo  T, Ito  Y,  et al.  Estimation of the lifetime probability of disease progression of papillary microcarcinoma of the thyroid during active surveillance.  Surgery. 2018;163(1):48-52. doi:10.1016/j.surg.2017.03.028PubMedGoogle ScholarCrossref
Original Investigation
March 21, 2019

The Role of Disease Label in Patient Perceptions and Treatment Decisions in the Setting of Low-Risk Malignant Neoplasms

Author Affiliations
  • 1Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
  • 2Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
  • 3Department of Surgery, University Health Network, Women’s College Hospital, and University of Toronto, Toronto, Ontario, Canada
  • 4Department of Laboratory Medicine and Pathobiology, University Health Network and University of Toronto, Toronto, Ontario, Canada
  • 5Department of Medicine, Mount Sinai Hospital and University of Toronto, Toronto, Ontario, Canada
  • 6Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
JAMA Oncol. 2019;5(6):817-823. doi:10.1001/jamaoncol.2019.0054
Key Points

Question  What role does disease label play in patients’ treatment decision making in the setting of low-risk epithelial malignant neoplasms such as papillary thyroid cancer?

Findings  The disease labels cancer, nodule, and tumor played a considerable role in patient decision making, with participants willing to accept a 4-percentage-point increased risk of progression or recurrence (from 1% to 5%) to avoid their disease being labeled as cancer in favor of nodule. The strength of the preference for the nodule label instead of cancer was similar to the preference for active surveillance instead of surgery.

Meaning  Omitting the word cancer from the disease label of low-risk epithelial malignant neoplasms may reduce overtreatment.

Abstract

Importance  The cancer disease label may lead to overtreatment of low-risk malignant neoplasms owing to a patient’s emotional response or misunderstanding of prognosis. Decision making should be driven by risks and benefits of treatment and prognosis rather than disease label.

Objective  To determine whether disease label plays a role in patient decision making in the setting of low-risk malignant neoplasms and to determine how the magnitude of the disease-label effect compares with preferences for treatment and prognosis.

Design, Setting, and Participants  A discrete choice experiment conducted using an online survey of 1314 US residents in which participants indicated their preferences between a series of 2 hypothetical vignettes describing the incidental discovery of a small thyroid lesion. Vignettes varied on 3 attributes: disease label (cancer, tumor, or nodule); treatment (active surveillance or hemithyroidectomy); and risk of progression or recurrence (0%, 1%, 2%, or 5%). The independent associations of each attribute with likelihood of vignette selection was estimated with a Bayesian mixed logit model.

Main Outcomes and Measures  The preference weight of the cancer disease label was compared with preference weights for other attributes.

Results  In 1068 predominantly healthy respondents (605 women and 463 men) with a median age of 35 years (range, 18-78 years), the cancer disease label played a considerable role in respondent decision making independent of treatment offered and risk of progression or recurrence. Participants accepted a 4-percentage-point increase in risk of progression or recurrence (from 1% to 5%) to avoid labeling their disease as cancer in favor of nodule (marginal rate of substitution [MRS], 1.0; 95% credible interval [CrI], 0.9-1.1). Preference for the nodule label instead of cancer was similar in magnitude to the preference for active surveillance over surgery (MRS, 1.0; 95% CrI, 0.9-1.1).

Conclusions and Relevance  Disease label plays a role in patient preference independent of treatment risks or prognosis. Raising the threshold for biopsy or removing the word cancer from the disease label may mitigate patient preference for aggressive treatment of low-risk lesions. Health care professionals should emphasize treatment risks and benefits and natural disease history when supporting treatment decisions for potentially innocuous epithelial malignant neoplasms.

Introduction

Sensitive screening and diagnostic tests detect many indolent cancers that pose a low risk of progression or mortality. Because many people associate the word cancer with an aggressive and lethal disease, labeling a person’s disease cancer may increase tolerance for treatment-associated morbidity, compounding the problem of overdiagnosis with overtreatment.1 An emerging understanding of molecular processes can facilitate reclassification of low-risk lesions that are unlikely to lead to progression or death.2

Recommendations from a panel convened by the National Cancer Institute in 2012 included removing the word cancer from description of low-risk lesions as data emerge to support a more appropriate descriptor.3 The incidence of thyroid cancer is rising faster than any other malignant neoplasm in the United States almost entirely owing to detection of small, low-risk lesions.4 Thyroidectomy remains the most common treatment, but guidelines suggest active surveillance may be appropriate for some low-risk tumors.5,6 A subset of thyroid cancers have been renamed noninvasive follicular thyroid neoplasm with papillarylike nuclear features in an effort to reduce the harms and costs of overtreating innocuous thyroid lesions labeled cancer.7 The ability of such taxonomic changes to reduce patients’ desire to undergo unnecessary treatment is unclear.

We sought to determine whether the label given to a thyroid neoplasm could play a role in patient decision making. We were interested not only in what treatment decisions patients make based on various disease labels but also in understanding how influential the label is relative to other attributes that accompany a cancer diagnosis, including its recommended treatment and prognosis. Discrete choice experiment (DCE) is a choice modeling method that quantifies preferences by analyzing decisions that respondents make. In DCEs, participants are presented with alternative scenarios that are systematically described according to several attributes and are asked to indicate which scenario they prefer. The probability of selecting a particular profile of attributes is proportional to their value to the respondent, which permits estimation of the relative importance of each attribute. We hypothesized that participants would be willing to make tradeoffs to avoid the cancer label by accepting less desirable treatment and prognosis.

Methods
Study Participants

We conducted an internet-based DCE, enrolling a voluntary sample of US residents aged 18 to 78 years. Respondents were recruited from Amazon’s Mechanical Turk online service, which facilitates the completion of posted tasks by registered users and has been used to investigate similar concepts.8 Self-reported demographic data were recorded and written informed consent was obtained from each respondent. The study was approved by the University Health Network Research Ethics Board.

Discrete Choice Experiment Design

Hypothetical vignettes depicting low-risk thyroid malignant neoplasms were constructed according to 3 attributes with 2 to 4 levels: (1) disease label (cancer, tumor, or nodule); (2) treatment offered (active surveillance or surgery); and (3) risk of progression or recurrence (0%, 1%, 2%, or 5%). Attribute and level selection are detailed in the eMethods in the Supplement. Participants were told their survival probability was greater than 99% in all vignettes.9 Descriptions of treatments and associated risks could be reviewed at any time (Figure 1A and B). Readability and comprehension were optimized through 5 pilot distributions to 100 participants each, with iterative adjustment in response to qualitative feedback until no novel feedback was provided.

Participants were presented with a series of 8 paired comparisons and asked each time to indicate which alternative they preferred (Figure 1C). The combinations of attributes and levels presented to each participant were selected according to a balanced overlap design (Sawtooth Software).

Respondents were excluded if they completed the survey in an unrealistically short time frame (<2 minutes; eMethods in the Supplement). Two attention-check choice sets were embedded, each fixed with identical disease label and treatment in both scenarios and an exaggerated difference in risk of progression or recurrence (0% vs 15%). Participants who selected the response option with the higher risk of progression or recurrence on either attention-check choice set were excluded.

Statistical Analysis

Bayesian logit models were used to estimate the relative priority respondents placed on each of the attributes (eMethods in the Supplement). Attribute levels with high, positive coefficients were strongly preferred, whereas those with negative coefficients were avoided. Coefficients were zero-centered and scaled such that the overall most preferred and most avoided levels were assigned values of +10 and −10, respectively. Individual variation in preferences was examined by graphically comparing coefficients across categories of age, sex, education, and overall health. The sensitivity of preference weights was tested by excluding respondents with a personal history of any cancer or thyroid surgery.

The strength of the desire to avoid the cancer label was expressed in terms of willingness to accept additional risk of recurrence or progression and willingness to accept more invasive treatment. This was reported as marginal rate of substitution (MRS), a ratio of difference in preferences between disease labels to difference in preferences between levels of treatment or prognosis. For example, if a change in the disease label from tumor to cancer was avoided to the same degree as an increase in the risk of recurrence from 0% to 1%, the MRS describing this effect would be 1.0. An MRS of greater than 1.0 indicates that the change in label is more influential than the compared change in recurrence risk, whereas MRS less than 1.0 indicates that the label change is less influential.

Minimum sample size for a DCE with this design varies from 125 to 300 subjects (eMethods in the Supplement). These estimations are intended to serve as guides and are not calculated for attainment of a specific power.10 We therefore sought to recruit a conservative sample of 1000 participants.

In an exploratory analysis, we examined whether a cancer diagnosis could induce decision making that appeared paradoxical. We defined a paradoxical choice as selection of a vignette with a less desirable treatment whose outcome was a less desirable prognosis when the disease label was the same in both vignettes. Desirability was defined by revealed preferences from the DCE. The proportion of paradoxical choices was compared between instances in which both vignettes delivered a cancer diagnosis vs when both vignettes delivered an alternative diagnosis (tumor or nodule).

Results
Participants

Of the 1314 participants who were enrolled, 34 (2.6%) did not complete the survey. We also excluded 63 participants (4.8%) who completed the survey in less than 2 minutes and an additional 149 (11.3%) who failed attention checks.

The 1068 included participants responded to 8544 choice sets, had a median age of 35 years (range 18-78 years), and 605 (56.7%) were female (Table). Good or very good overall health was reported by 721 (67.5%) participants, and 757 (70.9%) had completed at least 1 postsecondary degree. A personal history of any cancer or thyroid surgery was reported by 78 (7.3%). Excluded participants did not have materially different baseline characteristics.

Independent Attribute Level Preferences

The magnitude of model coefficients indicate the influence of each attribute level on vignette preference (Figure 2). Of all attribute levels, the most preferred was 0% risk of progression or recurrence and was assigned a mean preference weight of +10 (95% credible interval [CrI], +9.0 to +11.0). The most avoided attribute level was 5% risk of progression or recurrence, which was assigned a mean preference weight of −10 (95% CrI, −11.0 to −9.1). The active surveillance treatment strategy had a relative preference weight of +6.5 (95% CrI, +5.7 to +7.3) and was preferred over the surgery treatment strategy (−6.5; 95% CrI, −7.3 to −5.8). Disease label affected preference independent of treatment strategy or recurrence risk. Scenarios in which the disease was labeled nodule (+5.6; 95% CrI, +5.0 to +6.3) and tumor (+1.5; 95% CrI, +1.0 to +1.9) were preferred over those in which the disease was labeled cancer (−7.2; 95% CrI, −8.0 to −6.4).

The magnitude but not directionality of preferences varied slightly by demographic subgroup. Compared with respondents aged 18 to 30 years, decisions by those 50 years and older were more strongly influenced by disease label relative to progression risk, with stronger relative preference for nodule and avoidance of cancer (eFigure 1 in the Supplement). Older respondents also revealed slightly stronger avoidance of surgery, and those in good health more strongly preferred active surveillance (eFigures 1 and 2 in the Supplement). There appeared to be no other important differences according to sex, education, or overall health (eFigures 2-4 in the Supplement). Preference weights were not sensitive to exclusion of respondents with a history of cancer or thyroid surgery (eFigure 5 in the Supplement).

Disease Label Effect

The magnitude of preference for the nodule label vs cancer label was comparable to the magnitude of preference for 1% risk of progression or recurrence over a 5% risk (MRS, 1.0; 95% CrI, 0.9-1.1) (Figure 3). The preference for tumor label over cancer was slightly smaller, and closer to the preference for 5% risk over 2% risk of recurrence (MRS, 1.2; 95% CrI, 1.0-1.4). Participants were willing to accept surgery as their treatment if it meant that their disease was labeled nodule instead of cancer (MRS, 1.0; 95% CrI, 0.9-1.1). The strength of the preference for active surveillance was more influential than the preference for the tumor label over cancer (MRS, 0.7; 95% CrI, 0.6-0.8).

Paradoxical Decision Making Associated With Cancer Label

Considering choice sets in which the disease label was the same in both vignettes, a subset (n = 561) of choice sets presented the opportunity for the paradoxical selection of a vignette depicting a more invasive treatment whose outcome was a poorer prognosis. When the disease label was cancer in both vignettes, 57 of 209 (27.3%) responses were paradoxical, compared with 33 of 352 (9.4%) under any other disease label (risk ratio, 2.9).

Discussion

We found that disease label plays a role in how patients think about a low-risk malignant neoplasm. The magnitude of this role was large: disease label was equally important to participants as the treatment they received, and they were willing to accept a worse prognosis to avoid their disease being labeled as cancer. Although our study focused on thyroid cancer, similar findings may be present with other clinically indolent malignant neoplasms such as some forms of breast cancer,11,12 melanoma,13 lung cancer,14 and prostate cancer.15

The heterogeneity of cancer as a disease entity undoubtedly contributes to preference for overtreatment of low-risk malignant neoplasms. Preexisting perceptions about cancer from personal or others’ experiences with clinically aggressive malignant neoplasms may amplify responses to cancer risk.16 Participants preferred surgical treatment even when it resulted in poorer prognosis more frequently for a cancer diagnosis than for any other disease label. When treatment was considered independently, active surveillance was preferable to surgery. This paradoxical decision making suggests that removing the disease, even to the detriment of prognosis, is a strong instinct when we label the disease as cancer. Other demonstrations of preference for invasive treatment under a cancer label support this conclusion.17,18 This DCE has the advantage of concurrently estimating preferences for treatment and prognosis, thereby offering a meaningful quantification of the disease label effect. Furthermore, the simultaneous variation of all vignette attributes helped obscure the study hypothesis and preserve the validity of responses to successive choice sets.

Limitations

There were limitations of the study design. Online recruitment may select participants who are more technologically inclined; a bias reflected in the highly educated, young cohort studied. Older respondents were more strongly influenced by disease label and treatment relative to progression risk, and so our sample is likely to underestimate the relative influence of the word cancer in the US population. However, a young cohort may be appropriate for studying decision making in thyroid cancer; the thyroid is the second-most common site of malignant neoplasm (after breast) among young adults aged 30 to 35 years, and the median age of incident thyroid cancer is 51 years.4 Second, the online and hypothetical nature of the exercise may not have been conducive to realistically attentive decision making. To improve the likelihood of thoughtful deliberations, we restricted the analysis to participants who spent at least 2 minutes on the exercise and passed attention checks. Still, the time spent on the survey and counselling provided may not have been realistic. A deliberative, rational recognition that certain cancers are manageable or curable may be acknowledged by some patients after more extensive discussion.1 Emphasis on an excellent prognosis may improve comprehension of subsequent discussion and therefore be prioritized in reality, thereby moderating the role of disease label in decision making (see eDiscussion in the Supplement for discussion of attribute ordering).19 Nonetheless, our findings provide insight into the heuristics and cognitive biases that anchor patient preferences for management of low-risk malignant neoplasm at the time of diagnosis.

Direct clinical extrapolation of our findings is limited by the particular treatments and risks in our study. The recommended active surveillance protocol is a subject of ongoing study,20 and changes may affect its desirability. The range of potential progression or recurrence risks also vary according to clinical scenario and incident time frame discussed. Risks were meant to represent 5-year incidence rates but some participants may have interpreted these to be lifetime risks, which are higher in reality.21 Furthermore, treatment options and prognosis for other low-risk malignant neoplasms differ and results cannot, therefore, be directly extrapolated to other clinical settings. Despite variations in treatment and prognosis, the observed large magnitude of the cancer label suggests that disease label is likely to play a role in decision making for all low-risk malignant neoplasms.

Treatment decisions should be founded on discussion of treatment options, their associated risks, and prognosis rather than the disease label. Various strategies may mitigate the potential threat of the cancer label to clinically reasonable decision making. American Thyroid Association guidelines recommend increasing the threshold for biopsy of small, low-risk lesions in an effort to reduce overdiagnosis.5 Clinicians should inform patients of all of the consequences of diagnosing an indolent cancer when discussing the decision to biopsy, including risk of biasing decision making in favor of a potentially morbid treatment that has uncertain benefit over active surveillance. An alternative strategy is to reserve the word cancer for describing diseases with more substantial malignant potential, as the National Cancer Institute has suggested.3 This may be possible in conditions where pretreatment evaluation is able to identify low-risk lesions. In other instances, such as noninvasive follicular thyroid neoplasms with papillarylike nuclear features, histopathological examination of the entire thyroid nodule is required for diagnosis, and it may not be possible to use an alternative label before initial treatment. The patient’s perspective must also be considered. Retrospectively changing the name of a person’s disease has uncertain effects on survivorship and treatment regret. Furthermore, removing the word cancer may influence adherence to follow-up given the potential to contribute to confusion in the communication of risk. Confirming the absence of cancer may moderate patients’ motivation to regularly attend recommended surveillance investigations and the clinicians’ diligence in ordering and interpreting these investigations.

Finally, perhaps better acknowledgment of the potential decision-making bias induced by the cancer label can prompt corrective action. Nomenclature changes should not be viewed as a substitute for appropriately communicating the implications of a diagnosis, available treatments, and the natural history of low-risk malignant neoplasms.

Conclusions

The cancer label profoundly influences the choices that patients with low-risk malignant neoplasms make. It can induce paradoxical decision making that leads to overtreatment. Strategies to mitigate overtreatment include raising thresholds for biopsy of low-risk nodules, removing the word cancer from the description of low-risk lesions, and advancing public knowledge regarding the heterogeneity of cancer as a disease entity and the natural history of low-risk malignant neoplasms.

Back to top
Article Information

Accepted for Publication: December 22, 2018.

Corresponding Author: David R. Urbach, MD, MSc, Department of Surgery and Institute of Health Policy, Management and Evaluation, University of Toronto, Women’s College Hospital, 76 Grenville St, Room 8332, Toronto, ON M5S 1B2, Canada (David.Urbach@wchospital.ca).

Published Online: March 21, 2019. doi:10.1001/jamaoncol.2019.0054

Author Contributions: Dr Dixon had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Dixon, Tomlinson, Bell, Sawka, Goldstein, Urbach.

Acquisition, analysis, or interpretation of data: Dixon, Tomlinson, Pasternak, Mete, Sawka, Urbach.

Drafting of the manuscript: Dixon, Tomlinson, Urbach.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Dixon, Tomlinson.

Obtained funding: Urbach.

Administrative, technical, or material support: Dixon, Urbach.

Study supervision: Goldstein, Urbach.

Other—clinical content input: Pasternak, Sawka.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the Toronto General Research Institute with funds from the Canadian Institutes of Health Research.

Role of the Funder/Sponsor: The funders had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Robb  KA, Simon  AE, Miles  A, Wardle  J.  Public perceptions of cancer: a qualitative study of the balance of positive and negative beliefs.  BMJ Open. 2014;4(7):e005434. doi:10.1136/bmjopen-2014-005434PubMedGoogle ScholarCrossref
2.
Strickland  KC, Eszlinger  M, Paschke  R,  et al.  Molecular testing of nodules with a suspicious or malignant cytologic diagnosis in the setting of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP).  Endocr Pathol. 2018;29(1):68-74. doi:10.1007/s12022-018-9515-xPubMedGoogle ScholarCrossref
3.
Esserman  LJ, Thompson  IM, Reid  B,  et al.  Addressing overdiagnosis and overtreatment in cancer: a prescription for change.  Lancet Oncol. 2014;15(6):e234-e242. doi:10.1016/S1470-2045(13)70598-9PubMedGoogle ScholarCrossref
4.
Howlader  N, Noone  AM, Krapcho  M,  et al, eds. SEER cancer statistics review, 1975-2014, National Cancer Institute. Bethesda, MD, based on November 2016 SEER data submission posted to the SEER website, April 2017. https://seer.cancer.gov/csr/1975_2014/. Accessed January 3, 2018.
5.
Haugen  BR, Alexander  EK, Bible  KC,  et al.  2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer.  Thyroid. 2016;26(1):1-133. doi:10.1089/thy.2015.0020PubMedGoogle ScholarCrossref
6.
Topstad  D, Dickinson  JA.  Thyroid cancer incidence in Canada: a national cancer registry analysis.  CMAJ Open. 2017;5(3):E612-E616. doi:10.9778/cmajo.20160162PubMedGoogle ScholarCrossref
7.
Nikiforov  YE, Seethala  RR, Tallini  G,  et al.  Nomenclature revision for encapsulated follicular variant of papillary thyroid carcinoma: a paradigm shift to reduce overtreatment of indolent tumors.  JAMA Oncol. 2016;2(8):1023-1029. doi:10.1001/jamaoncol.2016.0386PubMedGoogle ScholarCrossref
8.
Krishnamurti  T, Woloshin  S, Schwartz  LM, Fischhoff  B.  A randomized trial testing US Food And Drug Administration “breakthrough” language.  JAMA Intern Med. 2015;175(11):1856-1858. doi:10.1001/jamainternmed.2015.5355PubMedGoogle ScholarCrossref
9.
Oda  H, Miyauchi  A, Ito  Y,  et al.  Incidences of unfavorable events in the management of low-risk papillary microcarcinoma of the thyroid by active surveillance versus immediate surgery.  Thyroid. 2016;26(1):150-155. doi:10.1089/thy.2015.0313PubMedGoogle ScholarCrossref
10.
de Bekker-Grob  EW, Donkers  B, Jonker  MF, Stolk  EA.  Sample size requirements for discrete-choice experiments in healthcare: a practical guide.  Patient. 2015;8(5):373-384. doi:10.1007/s40271-015-0118-zPubMedGoogle ScholarCrossref
11.
Nelson  HD, Pappas  M, Cantor  A, Griffin  J, Daeges  M, Humphrey  L.  Harms of breast cancer screening: systematic review to update the 2009 U.S. Preventive Services Task Force recommendation.  Ann Intern Med. 2016;164(4):256-267. doi:10.7326/M15-0970PubMedGoogle ScholarCrossref
12.
Oeffinger  KC, Fontham  ETH, Etzioni  R,  et al; American Cancer Society.  Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society.  JAMA. 2015;314(15):1599-1614. doi:10.1001/jama.2015.12783PubMedGoogle ScholarCrossref
13.
Glusac  EJ.  The melanoma ‘epidemic’: lessons from prostate cancer.  J Cutan Pathol. 2012;39(1):17-20. doi:10.1111/j.1600-0560.2011.01848.xPubMedGoogle ScholarCrossref
14.
Patz  EF  Jr, Pinsky  P, Gatsonis  C,  et al; NLST Overdiagnosis Manuscript Writing Team.  Overdiagnosis in low-dose computed tomography screening for lung cancer.  JAMA Intern Med. 2014;174(2):269-274. doi:10.1001/jamainternmed.2013.12738PubMedGoogle ScholarCrossref
15.
Vickers  AJ, Sjoberg  DD, Ulmert  D,  et al.  Empirical estimates of prostate cancer overdiagnosis by age and prostate-specific antigen.  BMC Med. 2014;12:26. doi:10.1186/1741-7015-12-26PubMedGoogle ScholarCrossref
16.
Kasperson  RE, Renn  O, Slovic  P,  et al.  The social amplification of risk: a conceptual framework.  Risk Anal. 1988;8(2):177-187. doi:10.1111/j.1539-6924.1988.tb01168.xGoogle ScholarCrossref
17.
Omer  ZB, Hwang  ES, Esserman  LJ, Howe  R, Ozanne  EM.  Impact of ductal carcinoma in situ terminology on patient treatment preferences.  JAMA Intern Med. 2013;173(19):1830-1831. doi:10.1001/jamainternmed.2013.8405PubMedGoogle ScholarCrossref
18.
Nickel  B, Barratt  A, McGeechan  K,  et al.  Effect of a change in papillary thyroid cancer terminology on anxiety levels and treatment preferences: a randomized crossover trial.  JAMA Otolaryngol Head Neck Surg. 2018;144(10):867-874. doi:10.1001/jamaoto.2018.1272PubMedGoogle ScholarCrossref
19.
Jansen  J, Butow  PN, van Weert  JCM,  et al.  Does age really matter? recall of information presented to newly referred patients with cancer.  J Clin Oncol. 2008;26(33):5450-5457. doi:10.1200/JCO.2007.15.2322PubMedGoogle ScholarCrossref
20.
Sawka  AM, Ghai  S, Tomlinson  G,  et al.  A protocol for a Canadian prospective observational study of decision-making on active surveillance or surgery for low-risk papillary thyroid cancer.  BMJ Open. 2018;8(4):e020298. doi:10.1136/bmjopen-2017-020298PubMedGoogle ScholarCrossref
21.
Miyauchi  A, Kudo  T, Ito  Y,  et al.  Estimation of the lifetime probability of disease progression of papillary microcarcinoma of the thyroid during active surveillance.  Surgery. 2018;163(1):48-52. doi:10.1016/j.surg.2017.03.028PubMedGoogle ScholarCrossref
×