[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.  Age-Adjusted Incidence of Thyroid Carcinoma Variants From 2000 to 2016
Age-Adjusted Incidence of Thyroid Carcinoma Variants From 2000 to 2016

Significant annual percentage change (APC) was observed for each subtype. A significantly larger APC was also noted in aggressive papillary thyroid carcinoma (PTC) relative to other variants. Pairwise tests of parallelism were performed between aggressive PTC vs well-differentiated papillary thyroid carcinoma (WDPTC), aggressive PTC vs anaplastic variant, and WDPTC vs anaplastic variant. The circles along the dotted lines represent discrete age-adjusted incidence values for a given thyroid variant at a given year.

Figure 2.  Distribution of Clinicopathologic Covariates Across Papillary Thyroid Carcinoma Aggressive Variants
Distribution of Clinicopathologic Covariates Across Papillary Thyroid Carcinoma Aggressive Variants

The T stage data are based on American Joint Committee on Cancer 7th Edition staging. N+ indicates node positivity; PDTC, poorly differentiated thyroid carcinoma; WDPTC, well-differentiated papillary thyroid carcinoma.

Figure 3.  Kaplan-Meier Curves Comparing Overall Survival and Disease-Specific Survival Across Aggressive Papillary Thyroid Carcinoma Variants
Kaplan-Meier Curves Comparing Overall Survival and Disease-Specific Survival Across Aggressive Papillary Thyroid Carcinoma Variants

Well-differentiated papillary thyroid carcinoma (WDPTC) and anaplastic thyroid carcinoma (ATC) cases were included for reference. Disease-specific survival was calculated using Surveillance, Epidemiology, and End Results data, while overall survival was determined using National Cancer Data Base data. PDTC indicates poorly differentiated thyroid cancer.

Table.  Univariate and Multivariable Analysis of Overall Survival in Thyroid Cancer
Univariate and Multivariable Analysis of Overall Survival in Thyroid Cancer
1.
Cancer Genome Atlas Research Network.  Integrated genomic characterization of papillary thyroid carcinoma.  Cell. 2014;159(3):676-690. doi:10.1016/j.cell.2014.09.050PubMedGoogle ScholarCrossref
2.
Cibas  ES, Ali  SZ.  The 2017 Bethesda system for reporting thyroid cytopathology.  Thyroid. 2017;27(11):1341-1346. doi:10.1089/thy.2017.0500PubMedGoogle ScholarCrossref
3.
Xing  M, Liu  R, Liu  X,  et al.  BRAF V600E and TERT promoter mutations cooperatively identify the most aggressive papillary thyroid cancer with highest recurrence.  J Clin Oncol. 2014;32(25):2718-2726. doi:10.1200/JCO.2014.55.5094PubMedGoogle ScholarCrossref
4.
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
5.
Tuttle  RM, Fagin  JA, Minkowitz  G,  et al.  Natural history and tumor volume kinetics of papillary thyroid cancers during active surveillance.  JAMA Otolaryngol Head Neck Surg. 2017;143(10):1015-1020. doi:10.1001/jamaoto.2017.1442PubMedGoogle ScholarCrossref
6.
Ho  AS, Luu  M, Zalt  C,  et al.  Mortality risk of nonoperative papillary thyroid carcinoma: a corollary for active surveillance.  Thyroid. 2019;29(10):1409-1417. doi:10.1089/thy.2019.0060PubMedGoogle ScholarCrossref
7.
Sywak  M, Pasieka  JL, Ogilvie  T.  A review of thyroid cancer with intermediate differentiation.  J Surg Oncol. 2004;86(1):44-54. doi:10.1002/jso.20044PubMedGoogle ScholarCrossref
8.
Landa  I, Ibrahimpasic  T, Boucai  L,  et al.  Genomic and transcriptomic hallmarks of poorly differentiated and anaplastic thyroid cancers.  J Clin Invest. 2016;126(3):1052-1066. doi:10.1172/JCI85271PubMedGoogle ScholarCrossref
9.
Regalbuto  C, Malandrino  P, Tumminia  A, Le Moli  R, Vigneri  R, Pezzino  V.  A diffuse sclerosing variant of papillary thyroid carcinoma: clinical and pathologic features and outcomes of 34 consecutive cases.  Thyroid. 2011;21(4):383-389. doi:10.1089/thy.2010.0331PubMedGoogle ScholarCrossref
10.
Lam  AK, Lo  CY.  Diffuse sclerosing variant of papillary carcinoma of the thyroid: a 35-year comparative study at a single institution.  Ann Surg Oncol. 2006;13(2):176-181. doi:10.1245/ASO.2006.03.062PubMedGoogle ScholarCrossref
11.
Vuong  HG, Kondo  T, Pham  TQ,  et al.  Prognostic significance of diffuse sclerosing variant papillary thyroid carcinoma: a systematic review and meta-analysis.  Eur J Endocrinol. 2017;176(4):433-441. doi:10.1530/EJE-16-0863PubMedGoogle ScholarCrossref
12.
Malandrino  P, Russo  M, Regalbuto  C,  et al.  Outcome of the diffuse sclerosing variant of papillary thyroid cancer: a meta-analysis.  Thyroid. 2016;26(9):1285-1292. doi:10.1089/thy.2016.0168PubMedGoogle ScholarCrossref
13.
Ghossein  RA, Leboeuf  R, Patel  KN,  et al.  Tall cell variant of papillary thyroid carcinoma without extrathyroid extension: biologic behavior and clinical implications.  Thyroid. 2007;17(7):655-661. doi:10.1089/thy.2007.0061PubMedGoogle ScholarCrossref
14.
Morris  LG, Shaha  AR, Tuttle  RM, Sikora  AG, Ganly  I.  Tall-cell variant of papillary thyroid carcinoma: a matched-pair analysis of survival.  Thyroid. 2010;20(2):153-158. doi:10.1089/thy.2009.0352PubMedGoogle ScholarCrossref
15.
Ganly  I, Ibrahimpasic  T, Rivera  M,  et al.  Prognostic implications of papillary thyroid carcinoma with tall-cell features.  Thyroid. 2014;24(4):662-670. doi:10.1089/thy.2013.0503PubMedGoogle ScholarCrossref
16.
Silver  CE, Owen  RP, Rodrigo  JP, Rinaldo  A, Devaney  KO, Ferlito  A.  Aggressive variants of papillary thyroid carcinoma.  Head Neck. 2011;33(7):1052-1059. doi:10.1002/hed.21494PubMedGoogle ScholarCrossref
17.
Haddad  RI, Nasr  C, Bischoff  L,  et al.  NCCN guidelines insights: thyroid carcinoma, version 2.2018.  J Natl Compr Canc Netw. 2018;16(12):1429-1440. doi:10.6004/jnccn.2018.0089PubMedGoogle ScholarCrossref
18.
Janz  TA, Graboyes  EM, Nguyen  SA,  et al.  A comparison of the NCDB and SEER database for research involving head and neck cancer.  Otolaryngol Head Neck Surg. 2019;160(2):284-294. doi:10.1177/0194599818792205PubMedGoogle ScholarCrossref
19.
Boffa  DJ, Rosen  JE, Mallin  K,  et al.  Using the National Cancer database for outcomes research: a review.  JAMA Oncol. 2017;3(12):1722-1728. doi:10.1001/jamaoncol.2016.6905PubMedGoogle ScholarCrossref
20.
Galliano  G, Frishberg  DP. Pathology and classification of thyroid tumors. In: Braunstein  G, ed.  Thyroid Cancer. New York, NY: Springer; 2012. doi:10.1007/978-1-4614-0875-8_1
21.
National Cancer Data Base. 2016 PUF. http://www.urlhere.com. Accessed February 4, 2020.
22.
Surveillance, Epidemiology, and End Results. Nov 2018 sub (2000-2016) (Katrina/Rita population adjustment), with additional treatment fields. http://www.urlhere.com. Accessed February 4, 2020.
23.
US Department of Commerce. Current population reports: population projections of the United States by age, sex, race, and Hispanic origin, 1995 to 2050. https://www.census.gov/prod/1/pop/p25-1130/p251130.pdf. Published February 1996. Accessed February 4, 2020.
24.
Tiwari  RC, Clegg  LX, Zou  Z.  Efficient interval estimation for age-adjusted cancer rates.  Stat Methods Med Res. 2006;15(6):547-569. doi:10.1177/0962280206070621PubMedGoogle ScholarCrossref
25.
Kim  HJ, Fay  MP, Feuer  EJ, Midthune  DN.  Permutation tests for joinpoint regression with applications to cancer rates.  Stat Med. 2000;19(3):335-351. doi:10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-ZPubMedGoogle ScholarCrossref
26.
Venables  WN, Ripley  BD.  Modern Applied Statistics with S. 4th ed. New York, NY: Springer; 2002. doi:10.1007/978-0-387-21706-2
27.
Grambsch  PM, Therneau  TM.  Proportional hazards tests and diagnostics based on weighted residuals.  Biometrika. 1994;81(3):515-526. doi:10.1093/biomet/81.3.515Google ScholarCrossref
28.
Rosenbaum  PR, Rubin  DB.  The central role of the propensity score in observational studies for causal effects.  Biometrika. 1983;70(1):41-55. doi:10.1093/biomet/70.1.41Google ScholarCrossref
29.
Becker  SO, Ichino  A.  Estimation of average treatment effects based on propensity scores.  Stata J. 2002;2(4):358-377. doi:10.1177/1536867X0200200403Google ScholarCrossref
30.
Team  RCR. A language and environment for statistical computing. 2018; https://www.R-project.org/.
31.
Davies  L, Welch  HG.  Current thyroid cancer trends in the United States.  JAMA Otolaryngol Head Neck Surg. 2014;140(4):317-322. doi:10.1001/jamaoto.2014.1PubMedGoogle ScholarCrossref
32.
Ho  AS, Davies  L, Nixon  IJ,  et al.  Increasing diagnosis of subclinical thyroid cancers leads to spurious improvements in survival rates.  Cancer. 2015;121(11):1793-1799. doi:10.1002/cncr.29289PubMedGoogle ScholarCrossref
33.
Freeman  M, Jemal  A.  Abstract LB-171: global variation in prostate cancer incidence and mortality rates, 1980-2013.  Cancer Res. 2019;79(13)(suppl):LB-171-LB-171. doi:10.1158/1538-7445.AM2019-LB-171Google Scholar
34.
de Groot  PM, Wu  CC, Carter  BW, Munden  RF.  The epidemiology of lung cancer.  Transl Lung Cancer Res. 2018;7(3):220-233. doi:10.21037/tlcr.2018.05.06PubMedGoogle ScholarCrossref
35.
Ansa  BE, Coughlin  SS, Alema-Mensah  E, Smith  SA.  Evaluation of colorectal cancer incidence trends in the United States (2000-2014).  J Clin Med. 2018;7(2):E22. doi:10.3390/jcm7020022PubMedGoogle Scholar
36.
Siegel  RL, Miller  KD, Jemal  A.  Cancer statistics, 2018.  CA Cancer J Clin. 2018;68(1):7-30. doi:10.3322/caac.21442PubMedGoogle ScholarCrossref
37.
Bibbins-Domingo  K, Grossman  DC, Curry  SJ,  et al; US Preventive Services Task Force.  Screening for thyroid cancer: US Preventive Services Task Force Recommendation statement.  JAMA. 2017;317(18):1882-1887. doi:10.1001/jama.2017.4011PubMedGoogle ScholarCrossref
38.
Davies  L, Morris  LGT.  The USPSTF recommendation on thyroid cancer screening: don’t “check your neck”.  JAMA Otolaryngol Head Neck Surg. 2017;143(8):755-756. doi:10.1001/jamaoto.2017.0502PubMedGoogle ScholarCrossref
39.
Walgama  E, Sacks  WL, Ho  AS.  Papillary thyroid microcarcinoma: optimal management versus overtreatment.  Curr Opin Oncol. 2020;32(1):1-6. doi:10.1097/CCO.0000000000000595PubMedGoogle ScholarCrossref
40.
Ho  AS, Chen  I, Melany  M, Sacks  WL.  Evolving management considerations in active surveillance for micropapillary thyroid carcinoma.  Curr Opin Endocrinol Diabetes Obes. 2018;25(5):353-359. doi:10.1097/MED.0000000000000438PubMedGoogle ScholarCrossref
41.
Michels  JJ, Jacques  M, Henry-Amar  M, Bardet  S.  Prevalence and prognostic significance of tall cell variant of papillary thyroid carcinoma.  Hum Pathol. 2007;38(2):212-219. doi:10.1016/j.humpath.2006.08.001PubMedGoogle ScholarCrossref
42.
Kuo  EJ, Goffredo  P, Sosa  JA, Roman  SA.  Aggressive variants of papillary thyroid microcarcinoma are associated with extrathyroidal spread and lymph-node metastases: a population-level analysis.  Thyroid. 2013;23(10):1305-1311. doi:10.1089/thy.2012.0563PubMedGoogle ScholarCrossref
43.
Regalbuto  C, Malandrino  P, Frasca  F,  et al.  The tall cell variant of papillary thyroid carcinoma: clinical and pathological features and outcomes.  J Endocrinol Invest. 2013;36(4):249-254.PubMedGoogle Scholar
44.
Akaishi  J, Kondo  T, Sugino  K,  et al.  Prognostic impact of the Turin criteria in poorly differentiated thyroid carcinoma.  World J Surg. 2019;43(9):2235-2244. doi:10.1007/s00268-019-05028-5PubMedGoogle ScholarCrossref
45.
Joung  JY, Kim  TH, Jeong  DJ,  et al.  Diffuse sclerosing variant of papillary thyroid carcinoma: major genetic alterations and prognostic implications.  Histopathology. 2016;69(1):45-53. doi:10.1111/his.12902PubMedGoogle ScholarCrossref
46.
Xia  F, Jiang  B, Chen  Y,  et al.  Prediction of novel target genes and pathways involved in tall cell variant papillary thyroid carcinoma.  Medicine (Baltimore). 2018;97(51):e13802. doi:10.1097/MD.0000000000013802PubMedGoogle Scholar
47.
Pozdeyev  N, Gay  LM, Sokol  ES,  et al.  Genetic analysis of 779 advanced differentiated and anaplastic thyroid cancers.  Clin Cancer Res. 2018;24(13):3059-3068. doi:10.1158/1078-0432.CCR-18-0373PubMedGoogle ScholarCrossref
48.
Momesso  DP, Vaisman  F, Yang  SP,  et al.  Dynamic risk stratification in patients with differentiated thyroid cancer treated without radioactive iodine.  J Clin Endocrinol Metab. 2016;101(7):2692-2700. doi:10.1210/jc.2015-4290PubMedGoogle ScholarCrossref
49.
Tuttle  RM, Alzahrani  AS.  Risk stratification in differentiated thyroid cancer: from detection to final follow-up.  J Clin Endocrinol Metab. 2019;jc.2019-00177. doi:10.1210/jc.2019-00177PubMedGoogle Scholar
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Views 1,979
    Citations 0
    Original Investigation
    March 5, 2020

    Incidence and Mortality Risk Spectrum Across Aggressive Variants of Papillary Thyroid Carcinoma

    Author Affiliations
    • 1Samuel Oschin Comprehensive Cancer Institute, Los Angeles, California
    • 2Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, California
    • 3Biostatistics and Bioinformatics Research Center, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
    • 4Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, California
    • 5Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, California
    • 6Division of Endocrinology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
    • 7Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California
    • 8Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, California
    JAMA Oncol. Published online March 5, 2020. doi:10.1001/jamaoncol.2019.6851
    Key Points

    Question  How do aggressive variants of papillary thyroid carcinoma (PTC), typically consolidated as a single intermediate-risk group, differ among histologic subtypes?

    Findings  This cohort study of 5447 patients found that, between 2000 and 2016, annualized growth in incidence of aggressive PTC variants significantly outpaced that of well-differentiated PTC. Comparative prognostic features and survival outcomes significantly differed between individual variants of PTC as well as relative to well-differentiated and anaplastic subtypes.

    Meaning  Given the disproportionately rising prevalence of aggressive PTC variants and their wide range of outcomes, greater emphasis on tailored treatment approaches is needed for these histologically and prognostically distinct subtypes.

    Abstract

    Importance  While well-differentiated papillary thyroid carcinoma (WDPTC) outcomes have been well characterized, the prognostic implications of more aggressive variants are far less defined. The rarity of these subtypes has led to their consolidation as intermediate risk for what are in fact likely heterogeneous diseases.

    Objective  To analyze incidence, clinicopathologic characteristics, and outcomes for aggressive variants of papillary thyroid carcinoma (PTC).

    Design, Setting, and Participants  This cohort study used data from 2000 to 2016 from hospital-based and population-based US cancer registries to analyze aggressive PTC variants, including diffuse sclerosing (DSV), tall-cell (TCV), insular, and poorly differentiated (PDTC) subtypes. These variants were compared against WDPTC and anaplastic cases. Data analysis was conducted from January 2019 to October 2019.

    Main Outcomes and Measures  Age-adjusted incidence was calculated via annual percentage change (APC) using the weighted least-squares method. Overall survival and disease-specific survival were analyzed via Cox regression. Propensity-score matching was used to adjust survival analyses for clinical and demographic covariates.

    Results  Collectively, 5447 aggressive PTC variants were identified (including 415 DSV, 3339 TCV, 362 insular, and 1331 PDTC cases), as well as 35 812 WDPTC and 2249 anaplastic cases. Over the study period, a substantial increase in aggressive variant incidence was observed (APC, 9.1 [95% CI, 7.33-10.89]; P < .001), surpassing the relative increases observed in WDPTC (APC, 5.1 [95% CI, 3.98-6.12]; P < .001) and anaplastic cases (APC, 1.9 [95% CI, 0.75-3.05]; P = .003; parallelism P < .007). Survival varied markedly based on histologic subtype, with a wide spectrum of mortality risk noted; 10-year overall survival was 85.4% (95% CI, 84.6%-86.3%) in WDPTC, 79.2% (95% CI, 73.6%-85.3%) in DSV, 71.9% (95% CI, 68.4%-75.6%) in TCV, 45.1% (95% CI, 40.2%-50.6%) in PDTC, 27.9% (95% CI, 20.0%-38.9%) in the insular variant, and 8.9% (95% CI, 7.5%-10.6%) in anaplastic cases (P < .001). These differences largely persisted even after adjusting for inherent differences in baseline characteristics by multivariable Cox regression and propensity-score matching.

    Conclusions and Relevance  An upsurge in aggressive PTC incidence was observed at a rate beyond that seen in WDPTC or anaplastic thyroid carcinoma. Moreover, long-term survival outcomes for aggressive PTC subgroups exhibit heterogeneous clinical behavior and a wide range of mortality risk, suggesting that treatment should be tailored to specific histologic subtypes. Given increasing prevalence and disparate outcomes, further investigation to identify optimal therapeutic strategies is needed in these diverse, understudied populations.

    Introduction

    Although well-differentiated papillary thyroid carcinoma (WDPTC) has risen in incidence, it has in turn become increasingly understood on a molecular, diagnostic, and prognostic level.1-3 Evolving staging systems and treatment paradigms have likewise reflected expanded insights into its biologic and clinical behavior.4-6

    In contrast, aggressive papillary thyroid carcinoma (PTC) variants remain sparsely defined and largely understudied.7,8 Perhaps because of their rarity, clinicopathologic features remain controversial, as have expected outcomes. Diffuse sclerosing variant (DSV), for instance, has been curiously described as both indolent9,10 and high risk,11,12 depending on the study. Another confounding issue is the lack of precise histologic definitions across both institutions and time. As an example, considerable disagreement exists among investigators regarding the threshold percentage of tall cells (30%-70%) that constitute true tall-cell variant (TCV) PTC.13-15

    Aggressive PTC variants represent a unique subset of patients that have been historically underrepresented in clinical trials and large-scale genomic studies, despite appearing to manifest with higher rates of regional spread, distant metastasis, and mortality risk.16 These features may in fact correspond with the progressively undifferentiated state of each variant—yet relative qualities have not been well appraised or compared.

    Collectively, aggressive PTC subtypes are often lumped together as intermediate risk4,17: too serious to ignore but too nebulous to separate. This is even though they are morphologically discrete and may harbor a spectrum of mortality risk. Cancer registries offer a valuable opportunity to delineate this spectrum by accruing higher case volumes of rare tumor variants. Here, we analyze shifts in the incidence of aggressive PTC subtypes and characterize their clinicopathologic outcomes in comparison with their conventional and anaplastic counterparts.

    Methods
    Data Sources

    Data were extracted from both the Surveillance, Epidemiology, and End Results (SEER)–21 database, as well as the National Cancer Data Base (NCDB). The SEER database is derived from 21 cancer registries and covers more than 28% of incident cases in the United States (https://seer.cancer.gov/). The NCDB is a tumor registry jointly maintained by the American Cancer Society and the Commission on Cancer of the American College of Surgeons and captures 70% of all cancers treated in the United States. Data between the 2 databases were maintained separately because there is likely patient overlap.18,19 The SEER data were used only to assess incidence and disease-specific survival; the remainder of analyses were performed with NCDB data, given the larger cohort size. This study was deemed exempt from formal review because it used publicly available, deidentified data, with a waiver of informed consent granted by the Cedars-Sinai institutional review board.

    Thyroid Cancer Histologic Subtypes

    Cases were selected from International Classification of Diseases for Oncology, Third Revision (ICD-O-3) histology codes, which are based on nomenclature adopted by the World Health Organization International Histological Classification of Tumors (Blue Books). Only cases with topographic code C73 were included. Morphologic subtypes recognized by the World Health Organization and selected for analysis included DSV (8350), TCV (8344), poorly differentiated thyroid cancer (PDTC) (8020), and insular variant (8337). Insular variant was included because of its association with PDTC and the lack of universal agreement on its origin.20 Columnar-cell variant is currently classified together with TCV (https://codes.iarc.fr). Other known, aggressive thyroid cancer subtypes with too few cases for inclusion were solid (8230) and hobnail/micropapillary variants (8265).

    For comparison, anaplastic thyroid carcinoma (8021) cases were selected, as were WDPTC cases (papillary carcinoma not otherwise specified [8050], papillary carcinoma of thyroid [8260], follicular variant of PTC [8340], papillary microcarcinoma [8341], and encapsulated papillary carcinoma [8343]). The WDPTC cases designated as poorly differentiated grade or undifferentiated/anaplastic grade were recategorized as PDTC and anaplastic thyroid carcinoma, respectively.

    NCDB/SEER Selection Criteria

    Cases from both the NCDB21 and SEER22 data sets were selected with the described thyroid cancer histologic subtypes. Patients were excluded if they had unknown diagnostic confirmation, missing tumor size, an unknown grade or differentiation, unknown regional lymph-node data, an unknown T stage, an unknown N stage, radiation before surgery or radiation and surgery in an unknown sequence, systemic therapy before surgery or an unknown sequence, an unknown follow-up status, or an unknown vital status. In the NCDB, patients were further excluded if they were missing data on academic facility, income, great circle distance, or Charlson/Deyo comorbidity score. In the SEER database, patients were further excluded if they lacked disease-specific survival data elements.

    Statistical Methods

    For annual percentage change (APC), rates were calculated per 100 000 and age adjusted to the 2000 US standard population (19 age groups23); 95% CIs for rates (Tiwari modification24) and trends were calculated. Percentage changes were calculated using 1 year for each end point; APCs were calculated using the weighted least-squares method. Comparison of incidence trends was performed with the Joinpoint Regression Program version 4.7.0.0 (IMS Inc).25 A pairwise test of parallelism was used to compare the APC among each of the PTC subtypes with a Bonferroni-corrected level of P < .02 (0.05/3) for the 3 comparisons considered significant (PTC vs WDPTC, aggressive PTC vs anaplastic PTC, and WDPTC vs anaplastic PTC).

    Aggressive-variant PTC cases were described using descriptive statistics such as mean, median, counts, and proportions. The PTC subtypes were then stratified and compared using analysis of variance, Kruskal-Wallis tests, and Pearson χ2 tests for continuous and categorical variables, as appropriate. Overall survival was calculated using the Kaplan-Meier method with survival curves compared using the log-rank test. Disease-specific survival was calculated with the cumulative incidence method and curves compared using the k-sample test. Median follow-up was determined by the reverse Kaplan-Meier method.

    Univariate and multivariable survival analyses were performed using the Cox proportional-hazards model. Variable selection was performed using a backward stepwise variable-selection procedure, optimizing for the Akaike information criterion.26 The proportional hazards assumption was assessed by Schoenfeld residuals and the goodness-of-fit test proposed by Grambsch and Therneau.27 Multicollinearity was assessed by the variable inflation factor.

    To further adjust for potential bias within our cohorts, propensity scores were used to match each aggressive PTC subtype with the WDPTC subtype.28 Propensity scores were estimated for each individual using a multivariable logistic regression model adjusting for facility, patient, and clinical covariates, such as facility academic status, region, age, sex, race, insurance, income, education, urban, distance from center, comorbidity, lymph node size, number of nodes positive, number of nodes examined, T staging, N staging, M staging, extrathyroidal extension (ETE), whether surgery resection was performed, margin, radiation therapy, and chemotherapy. Propensity scores were then matched among each aggressive PTC subtype cohort with WDPTC using the nearest-neighbor method.29 Quality of the propensity score–matched cohort was assessed visually using density histograms of the propensity scores. All statistical analyses were performed from [month year] to [month year] using R software package version 3.6.1 (R Foundation for Statistical Computing), with a 2-sided test and P values less than .05 considered significant.30

    Results

    For the primary NCDB analysis, 5447 aggressive-variant PTC cases were reported across 895 institutions (eTable 1 and eFigure 1 in the Supplement), with a median follow-up of 51.2 (95% CI, 50.8-51.6) months. Additionally, 35 812 WDPTC and 2249 anaplastic cases were identified for comparison.

    From 2000 to 2016, the age-adjusted incidence of aggressive PTC variants significantly increased (APC, 9.1 [95% CI, 7.33-10.89]; P < .001). This rate of change occurred at a much greater pace compared with anaplastic thyroid cancer (APC, 1.9 [95% CI, 0.75-3.05]; P = .003) or WDPTC (APC, 5.1 [95% CI, 3.98-6.13]; P < .001) (Figure 1). The pairwise test of parallelism between aggressive PTC vs WDPTC (P = .007), aggressive PTC vs anaplastic PTC (P = .001), and WDPTC vs anaplastic PTC (P < .001) all showed significant differences in APC.

    Overall, a broad progression of aggressive features was observed across aggressive PTC variants, corresponding to mean age and mean survival (Figure 2; eTable 2 in the Supplement). Relative to WDPTC, each aggressive PTC subtype tended to present in patients older in age (mean [SD] age: WDPTC, 56.3 [10.8] years; DSV, 56.9 [11.8] years; TCV, 58.5 [11.9] years; PDTC, 63.1 [12.8] years; insular variant, 64.7 [12.5] years; anaplastic cases, 69.7 [11.7] years; P < .001), with larger tumor size (mean [SD] size: WDPTC, 1.6 [2.3] cm; DSV, 1.9 [1.7] cm; TCV, 2.4 [2.2] cm; PDTC, 4.3 [4.3] cm; insular variant, 6.1 [4.2] cm; anaplastic cases, 6.5 [6.4] cm; P < .001), with a higher mean metastatic lymph node number (mean [SD] number: WDPTC, 1.6 [3.9] nodes; DSV, 4.5 [7.8] nodes; TCV, 3.5 [5.9] nodes; PDTC, 3.8 [8.0] nodes; insular variant, 2.8 [6.6] nodes; anaplastic cases, 3.4 [6.4] nodes; P < .001), greater rate of ETE (WDPTC, 6032 patients [16.8%]; DSV, 186 patients [44.9%]; TCV, 1860 patients [55.8%]; PDTC, 694 patients [52.3%]; insular variant, 189 patients [52.4%]; anaplastic cases, 1870 patients [85.0%]; P < .001), and greater incidence of M1 disease (WDPTC, 319 patients [0.9%]; DSV, 9 patients [2.3%]; TCV, 126 patients [4.0%]; PDTC, 204 patients [16.1%]; insular variant, 85 patients [24.3%]; anaplastic cases, 851 patients [39.1%]; P < .001). Conversely, these covariates were favorable relative to anaplastic cases (P < .001 for each comparison with the data presented above). Diffuse sclerosing variant and TCV appeared in patients younger in mean (SD) age compared with those with PDTC and insular variants (DSV, 56.9 [11.8] years; TCV, 58.5 [11.9] years; PDTC, 63.1 [12.8] years; insular variants, 64.7 [12.5] years; P < .001). Similar mean (SD) differences were seen in tumor size (DSV, 1.9 [1.7] cm; TCV, 2.4 [2.2] cm; PDTC, 4.3 [4.2] cm; insular variants, 6.1 [4.2] cm; P < .001) and M1 incidence on presentation (DSV, 9 patients [2.3%]; TCV, 126 patients [4.0%]; PDTC, 204 patients [16.1%]; insular variant, 85 patients [24.3%]; P < .001).

    On univariate analysis, aggressive PTC subtype (reference) was strongly associated with worsening overall survival compared with other subtypes (hazard ratios: DSV, 1.998 [95% CI, 1.532-2.605]; TCV, 2.163 [95% CI, 1.946-2.404]; PDTC, 6.947 [95% CI, 6.289-7.675]; insular variant, 8.426 [95% CI, 7.189-9.877]; anaplastic cases, 45.741 [95% CI, 42.908-48.76]; P < .001 for all comparisons; Table; eTable 3 in the Supplement). Kaplan-Meier survival plots depicted a wide range of outcomes across aggressive PTC variants. Estimated 10-year overall survival (in the NCDB) was significantly different between subtypes (79.2% [95% CI, 73.6%-85.3%], 71.9% [95% CI, 68.4%-75.6%], 45.1% [95% CI, 40.2%-50.6%], and 27.9% [95% CI, 20.0%-38.9%] for DSV, TCV, PDTC, and insular variants, respectively; P < .001; Figure 3A). Estimated 10-year disease-specific survival (in the SEER database) was also significantly different between subtypes (96.7% [95% CI, 95.0%-98.4%], 89.6% [95% CI, 87.6%-91.7%], 70.0% [95% CI, 67.3%-72.8%], and 59.1% [95% CI, 51.0%-68.4%] for DSV, TCV, PDTC, and insular variants, respectively; P < .001; Figure 3B). On multivariable analysis, all subtypes continued to demonstrate significantly higher hazard ratios relative to WDPTC (hazard ratios: DSV, 1.596 [95% CI, 1.124-2.268]; TCV, 1.413 [95% CI, 1.223-1.632]; PDTC, 2.822 [95% CI, 2.421-3.290]; insular variant, 3.016 [95% CI, 2.366-3.844]; anaplastic cases, 5.719 [95% CI, 4.835-6.765; P < .001 for all comparisons; Table; eTable 3 in the Supplement).

    To account for potential confounders, propensity-score analysis was performed to assess for subtype differences in overall survival. On comparison with WDPTC, the difference in overall survival for DSV disappeared (eFigure 2 in the Supplement). The remaining aggressive variants exhibited sustained overall survival differences relative to WDPTC (10-year overall survival: TCV, 72.4% [95% CI, 67.8%-77.3%]; PDTC, 44.3% [95% CI, 37.4%-52.4%]; insular variant, 24.3% [95% CI, 13.8%-42.9%]; P < .001; eFigure 2 in the Supplement).

    Discussion

    In this study, we show a rise in aggressive PTC incidence, with a 9.1% increase in incidence each year over the last 2 decades. We further illustrate the wide, heterogeneous range of outcomes among PTC subtypes normally consolidated into a single risk category. Using a multivariable regression model, we observe that histologic subtype is a key independent factor associated with mortality, on a magnitude approximating or surpassing recognized factors, such as extrathyroidal extension and nodal metastasis (Table).

    Our results reframe the conventional view regarding the rise in incidence of well-differentiated thyroid cancers.31,32 While the increase documented for WDPTC is substantial, the growth for aggressive PTC variants is even greater over the same period and beyond what might be expected by chance. In addition, although the increase in WDPTC appears to show signs of plateau since 2012, there is no evidence of this for aggressive PTC histologic subtypes. This likewise contrasts with the broader decline in incidence for more common malignant conditions, such as colon, prostate, and lung cancers.33-36

    Importantly, the aggressive PTC rise in incidence is less likely to be drawn from incidental diagnosis (ie, the subclinical reservoir), given the high degree of advanced, symptomatic disease inherent in these subtypes (Figure 2; eTable 2 in the Supplement). As such, while the surge in WDPTC incidence has been met with calls for mitigating overdiagnosis and overtreatment because of largely indolent tumor behavior,37-40 the greater rise in aggressive PTC incidence may require markedly different responses for workup and treatment escalation. It is also unclear if the rise is attributable to a fundamental increase in the likelihood of these cancers developing or stems from increasing awareness by pathologists. In either case, optimizing specific therapeutic strategies will become more worthwhile as these variants grow in prevalence.

    Our survival analysis builds on prior studies assessing the prognostic importance of histology.9,14,41-43 These reports have debated whether histologic subtype is a surrogate for other unfavorable covariates known to be negatively associated with survival, such as older age or distant metastasis.16 Perhaps because of low numbers and limited follow-up, prior findings may be underpowered to discriminate differences in outcome. Our study design differs in several meaningful ways, including a multifold larger cohort size, a broader array of compared histologic subtypes, and adjustment for potential confounders. Indeed, all aggressive PTC variants demographically exhibited a higher incidence of features that confer poorer prognosis (ie, ETE, older age, larger mean size, nodal metastasis, and distant metastasis; Figure 2; eTable 2 in the Supplement), as others have suggested9,41,43; yet even after correcting for these covariates, each histologic subtype upheld a significant, independent, and negative association with survival.

    These PTC subtypes furthermore span a noticeably eclectic spectrum of survival outcomes (Figure 3), which belies current guidelines and clinical management.4,17 Such differences may have noteworthy implications for treatment and counseling. For example, insular and poorly differentiated histologic subtypes have roughly double the risk of death as DSV or TCV, even after adjusting for differences in presentation. Strikingly, the high distant metastasis rate seen on initial presentation (16.1%-24.3%) with PDTC and insular histologic subtypes dwarf that seen in DSV (2.3%) and even TCV subtypes (4.0%): this could alter the management offered to a patient with known insular or PDTC histologic subtypes, whether it be a more aggressive curative approach or scaled-down palliative one if distant metastasis is recognized. In contrast, a patient with DSV or TCV could be comparatively more optimistic regarding prognosis. Knowledge of such histology-specific features and outcomes should enable clinicians to better calibrate management decisions, rather than consider them alike.

    Limitations

    A number of caveats may limit this study’s analysis, including its retrospective nature and the scope of ICD-O-3 methodology. Coding errors are inherent within cancer registries, and while annual audits are undertaken, a centralized review of pathology was not performed. Although a number of covariates were corrected for on multivariable analysis, there is an absence of thyroid cancer-specific treatment details, including radioactive iodine dose, thyrotropin suppression targets, and thyroglobulin levels. The lack of precise histologic definitions may also lead to underrepresentation of PTC subtypes, although the diversity of the 895 institutions involved should mitigate any single center’s reporting bias or pathologists’ discretion. Greater appreciation of rarer thyroid cancer variants and the increase in subspecialty thyroid pathologists over time may also bias true incidence. The evolution of histologic definitions may furthermore have affected reported numbers: the Turin criteria for PDTC,44 for instance, was proposed in 2007 but included in World Health Organization classification after our study’s time window. Such reclassification may increase or decrease incidence in multifactorial ways difficult to adjust for.

    Finally, it is important to note that individual cancers will contain a blend of variants or tumor grade components that may defy categorization. While NCDB and SEER code only the primary histologic subtype or the highest grade assigned by the pathologist, there exists unavoidable ambiguity for certain cases that we cannot verify. Nonetheless, the divergence noted in incidence and prognosis are intuitively appreciable: the breadth and scale of these results represent convincing evidence backing substratification.

    Conclusions

    In summary, we describe a disproportionate rise in aggressive PTC incidence and confirm a diverse range of outcomes for a group aggregated as intermediate risk. Such survival differences naturally coincide with heterogeneous disease variants known to be histologically and genomically distinct.8,45-47 Aggressive PTC subtypes deserve deeper understanding and greater vigilance, because they constitute a rising fraction of incidence and a lopsided share of cancer-associated mortality. Our data suggest that broader integration of unique PTC histological characteristics may improve the dynamic risk stratification supported in current treatment guidelines.4,48,49 Such adjustments should better convey prognosis and ultimately advance patient decision-making.

    Back to top
    Article Information

    Accepted for Publication: December 11, 2019.

    Corresponding Author: Allen S. Ho, MD, Cedars-Sinai Medical Center, 8635 West Third St, Ste 590W, Los Angeles, CA 90048 (allen.ho@cshs.org); Zachary S. Zumsteg, MD, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048 (zachary.zumsteg@cshs.org).

    Published Online: March 5, 2020. doi:10.1001/jamaoncol.2019.6851

    Author Contributions: Drs Ho and Zumsteg had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Ho, Melany, Zumsteg.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Ho, Luu, Barrios, Ali, Y. Chen, Zumsteg.

    Critical revision of the manuscript for important intellectual content: Ho, Luu, I. Chen, Melany, Patio, Bose, Fan, Mallen-St. Clair, Braunstein, Sacks.

    Statistical analysis: Ho, Luu.

    Administrative, technical, or material support: Ho, Barrios, Melany, Patio, Sacks, Zumsteg.

    Supervision: Ho, Bose, Mallen-St. Clair, Zumsteg.

    Conflict of Interest Disclosures: Dr Zumsteg was on the external advisory board for the Scripps Proton Therapy Center and has been a paid consultant for EMD Serono. No other disclosures were reported.

    Funding/Support: This study was supported by the Donna and Jesse Garber Award for Cancer Research (Dr Ho) and the Levy Family Fellowship in Thyroid Cancer (Ms Barrios).

    Role of the Funder/Sponsor: The funders had no role in the 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.
    Cancer Genome Atlas Research Network.  Integrated genomic characterization of papillary thyroid carcinoma.  Cell. 2014;159(3):676-690. doi:10.1016/j.cell.2014.09.050PubMedGoogle ScholarCrossref
    2.
    Cibas  ES, Ali  SZ.  The 2017 Bethesda system for reporting thyroid cytopathology.  Thyroid. 2017;27(11):1341-1346. doi:10.1089/thy.2017.0500PubMedGoogle ScholarCrossref
    3.
    Xing  M, Liu  R, Liu  X,  et al.  BRAF V600E and TERT promoter mutations cooperatively identify the most aggressive papillary thyroid cancer with highest recurrence.  J Clin Oncol. 2014;32(25):2718-2726. doi:10.1200/JCO.2014.55.5094PubMedGoogle ScholarCrossref
    4.
    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
    5.
    Tuttle  RM, Fagin  JA, Minkowitz  G,  et al.  Natural history and tumor volume kinetics of papillary thyroid cancers during active surveillance.  JAMA Otolaryngol Head Neck Surg. 2017;143(10):1015-1020. doi:10.1001/jamaoto.2017.1442PubMedGoogle ScholarCrossref
    6.
    Ho  AS, Luu  M, Zalt  C,  et al.  Mortality risk of nonoperative papillary thyroid carcinoma: a corollary for active surveillance.  Thyroid. 2019;29(10):1409-1417. doi:10.1089/thy.2019.0060PubMedGoogle ScholarCrossref
    7.
    Sywak  M, Pasieka  JL, Ogilvie  T.  A review of thyroid cancer with intermediate differentiation.  J Surg Oncol. 2004;86(1):44-54. doi:10.1002/jso.20044PubMedGoogle ScholarCrossref
    8.
    Landa  I, Ibrahimpasic  T, Boucai  L,  et al.  Genomic and transcriptomic hallmarks of poorly differentiated and anaplastic thyroid cancers.  J Clin Invest. 2016;126(3):1052-1066. doi:10.1172/JCI85271PubMedGoogle ScholarCrossref
    9.
    Regalbuto  C, Malandrino  P, Tumminia  A, Le Moli  R, Vigneri  R, Pezzino  V.  A diffuse sclerosing variant of papillary thyroid carcinoma: clinical and pathologic features and outcomes of 34 consecutive cases.  Thyroid. 2011;21(4):383-389. doi:10.1089/thy.2010.0331PubMedGoogle ScholarCrossref
    10.
    Lam  AK, Lo  CY.  Diffuse sclerosing variant of papillary carcinoma of the thyroid: a 35-year comparative study at a single institution.  Ann Surg Oncol. 2006;13(2):176-181. doi:10.1245/ASO.2006.03.062PubMedGoogle ScholarCrossref
    11.
    Vuong  HG, Kondo  T, Pham  TQ,  et al.  Prognostic significance of diffuse sclerosing variant papillary thyroid carcinoma: a systematic review and meta-analysis.  Eur J Endocrinol. 2017;176(4):433-441. doi:10.1530/EJE-16-0863PubMedGoogle ScholarCrossref
    12.
    Malandrino  P, Russo  M, Regalbuto  C,  et al.  Outcome of the diffuse sclerosing variant of papillary thyroid cancer: a meta-analysis.  Thyroid. 2016;26(9):1285-1292. doi:10.1089/thy.2016.0168PubMedGoogle ScholarCrossref
    13.
    Ghossein  RA, Leboeuf  R, Patel  KN,  et al.  Tall cell variant of papillary thyroid carcinoma without extrathyroid extension: biologic behavior and clinical implications.  Thyroid. 2007;17(7):655-661. doi:10.1089/thy.2007.0061PubMedGoogle ScholarCrossref
    14.
    Morris  LG, Shaha  AR, Tuttle  RM, Sikora  AG, Ganly  I.  Tall-cell variant of papillary thyroid carcinoma: a matched-pair analysis of survival.  Thyroid. 2010;20(2):153-158. doi:10.1089/thy.2009.0352PubMedGoogle ScholarCrossref
    15.
    Ganly  I, Ibrahimpasic  T, Rivera  M,  et al.  Prognostic implications of papillary thyroid carcinoma with tall-cell features.  Thyroid. 2014;24(4):662-670. doi:10.1089/thy.2013.0503PubMedGoogle ScholarCrossref
    16.
    Silver  CE, Owen  RP, Rodrigo  JP, Rinaldo  A, Devaney  KO, Ferlito  A.  Aggressive variants of papillary thyroid carcinoma.  Head Neck. 2011;33(7):1052-1059. doi:10.1002/hed.21494PubMedGoogle ScholarCrossref
    17.
    Haddad  RI, Nasr  C, Bischoff  L,  et al.  NCCN guidelines insights: thyroid carcinoma, version 2.2018.  J Natl Compr Canc Netw. 2018;16(12):1429-1440. doi:10.6004/jnccn.2018.0089PubMedGoogle ScholarCrossref
    18.
    Janz  TA, Graboyes  EM, Nguyen  SA,  et al.  A comparison of the NCDB and SEER database for research involving head and neck cancer.  Otolaryngol Head Neck Surg. 2019;160(2):284-294. doi:10.1177/0194599818792205PubMedGoogle ScholarCrossref
    19.
    Boffa  DJ, Rosen  JE, Mallin  K,  et al.  Using the National Cancer database for outcomes research: a review.  JAMA Oncol. 2017;3(12):1722-1728. doi:10.1001/jamaoncol.2016.6905PubMedGoogle ScholarCrossref
    20.
    Galliano  G, Frishberg  DP. Pathology and classification of thyroid tumors. In: Braunstein  G, ed.  Thyroid Cancer. New York, NY: Springer; 2012. doi:10.1007/978-1-4614-0875-8_1
    21.
    National Cancer Data Base. 2016 PUF. http://www.urlhere.com. Accessed February 4, 2020.
    22.
    Surveillance, Epidemiology, and End Results. Nov 2018 sub (2000-2016) (Katrina/Rita population adjustment), with additional treatment fields. http://www.urlhere.com. Accessed February 4, 2020.
    23.
    US Department of Commerce. Current population reports: population projections of the United States by age, sex, race, and Hispanic origin, 1995 to 2050. https://www.census.gov/prod/1/pop/p25-1130/p251130.pdf. Published February 1996. Accessed February 4, 2020.
    24.
    Tiwari  RC, Clegg  LX, Zou  Z.  Efficient interval estimation for age-adjusted cancer rates.  Stat Methods Med Res. 2006;15(6):547-569. doi:10.1177/0962280206070621PubMedGoogle ScholarCrossref
    25.
    Kim  HJ, Fay  MP, Feuer  EJ, Midthune  DN.  Permutation tests for joinpoint regression with applications to cancer rates.  Stat Med. 2000;19(3):335-351. doi:10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-ZPubMedGoogle ScholarCrossref
    26.
    Venables  WN, Ripley  BD.  Modern Applied Statistics with S. 4th ed. New York, NY: Springer; 2002. doi:10.1007/978-0-387-21706-2
    27.
    Grambsch  PM, Therneau  TM.  Proportional hazards tests and diagnostics based on weighted residuals.  Biometrika. 1994;81(3):515-526. doi:10.1093/biomet/81.3.515Google ScholarCrossref
    28.
    Rosenbaum  PR, Rubin  DB.  The central role of the propensity score in observational studies for causal effects.  Biometrika. 1983;70(1):41-55. doi:10.1093/biomet/70.1.41Google ScholarCrossref
    29.
    Becker  SO, Ichino  A.  Estimation of average treatment effects based on propensity scores.  Stata J. 2002;2(4):358-377. doi:10.1177/1536867X0200200403Google ScholarCrossref
    30.
    Team  RCR. A language and environment for statistical computing. 2018; https://www.R-project.org/.
    31.
    Davies  L, Welch  HG.  Current thyroid cancer trends in the United States.  JAMA Otolaryngol Head Neck Surg. 2014;140(4):317-322. doi:10.1001/jamaoto.2014.1PubMedGoogle ScholarCrossref
    32.
    Ho  AS, Davies  L, Nixon  IJ,  et al.  Increasing diagnosis of subclinical thyroid cancers leads to spurious improvements in survival rates.  Cancer. 2015;121(11):1793-1799. doi:10.1002/cncr.29289PubMedGoogle ScholarCrossref
    33.
    Freeman  M, Jemal  A.  Abstract LB-171: global variation in prostate cancer incidence and mortality rates, 1980-2013.  Cancer Res. 2019;79(13)(suppl):LB-171-LB-171. doi:10.1158/1538-7445.AM2019-LB-171Google Scholar
    34.
    de Groot  PM, Wu  CC, Carter  BW, Munden  RF.  The epidemiology of lung cancer.  Transl Lung Cancer Res. 2018;7(3):220-233. doi:10.21037/tlcr.2018.05.06PubMedGoogle ScholarCrossref
    35.
    Ansa  BE, Coughlin  SS, Alema-Mensah  E, Smith  SA.  Evaluation of colorectal cancer incidence trends in the United States (2000-2014).  J Clin Med. 2018;7(2):E22. doi:10.3390/jcm7020022PubMedGoogle Scholar
    36.
    Siegel  RL, Miller  KD, Jemal  A.  Cancer statistics, 2018.  CA Cancer J Clin. 2018;68(1):7-30. doi:10.3322/caac.21442PubMedGoogle ScholarCrossref
    37.
    Bibbins-Domingo  K, Grossman  DC, Curry  SJ,  et al; US Preventive Services Task Force.  Screening for thyroid cancer: US Preventive Services Task Force Recommendation statement.  JAMA. 2017;317(18):1882-1887. doi:10.1001/jama.2017.4011PubMedGoogle ScholarCrossref
    38.
    Davies  L, Morris  LGT.  The USPSTF recommendation on thyroid cancer screening: don’t “check your neck”.  JAMA Otolaryngol Head Neck Surg. 2017;143(8):755-756. doi:10.1001/jamaoto.2017.0502PubMedGoogle ScholarCrossref
    39.
    Walgama  E, Sacks  WL, Ho  AS.  Papillary thyroid microcarcinoma: optimal management versus overtreatment.  Curr Opin Oncol. 2020;32(1):1-6. doi:10.1097/CCO.0000000000000595PubMedGoogle ScholarCrossref
    40.
    Ho  AS, Chen  I, Melany  M, Sacks  WL.  Evolving management considerations in active surveillance for micropapillary thyroid carcinoma.  Curr Opin Endocrinol Diabetes Obes. 2018;25(5):353-359. doi:10.1097/MED.0000000000000438PubMedGoogle ScholarCrossref
    41.
    Michels  JJ, Jacques  M, Henry-Amar  M, Bardet  S.  Prevalence and prognostic significance of tall cell variant of papillary thyroid carcinoma.  Hum Pathol. 2007;38(2):212-219. doi:10.1016/j.humpath.2006.08.001PubMedGoogle ScholarCrossref
    42.
    Kuo  EJ, Goffredo  P, Sosa  JA, Roman  SA.  Aggressive variants of papillary thyroid microcarcinoma are associated with extrathyroidal spread and lymph-node metastases: a population-level analysis.  Thyroid. 2013;23(10):1305-1311. doi:10.1089/thy.2012.0563PubMedGoogle ScholarCrossref
    43.
    Regalbuto  C, Malandrino  P, Frasca  F,  et al.  The tall cell variant of papillary thyroid carcinoma: clinical and pathological features and outcomes.  J Endocrinol Invest. 2013;36(4):249-254.PubMedGoogle Scholar
    44.
    Akaishi  J, Kondo  T, Sugino  K,  et al.  Prognostic impact of the Turin criteria in poorly differentiated thyroid carcinoma.  World J Surg. 2019;43(9):2235-2244. doi:10.1007/s00268-019-05028-5PubMedGoogle ScholarCrossref
    45.
    Joung  JY, Kim  TH, Jeong  DJ,  et al.  Diffuse sclerosing variant of papillary thyroid carcinoma: major genetic alterations and prognostic implications.  Histopathology. 2016;69(1):45-53. doi:10.1111/his.12902PubMedGoogle ScholarCrossref
    46.
    Xia  F, Jiang  B, Chen  Y,  et al.  Prediction of novel target genes and pathways involved in tall cell variant papillary thyroid carcinoma.  Medicine (Baltimore). 2018;97(51):e13802. doi:10.1097/MD.0000000000013802PubMedGoogle Scholar
    47.
    Pozdeyev  N, Gay  LM, Sokol  ES,  et al.  Genetic analysis of 779 advanced differentiated and anaplastic thyroid cancers.  Clin Cancer Res. 2018;24(13):3059-3068. doi:10.1158/1078-0432.CCR-18-0373PubMedGoogle ScholarCrossref
    48.
    Momesso  DP, Vaisman  F, Yang  SP,  et al.  Dynamic risk stratification in patients with differentiated thyroid cancer treated without radioactive iodine.  J Clin Endocrinol Metab. 2016;101(7):2692-2700. doi:10.1210/jc.2015-4290PubMedGoogle ScholarCrossref
    49.
    Tuttle  RM, Alzahrani  AS.  Risk stratification in differentiated thyroid cancer: from detection to final follow-up.  J Clin Endocrinol Metab. 2019;jc.2019-00177. doi:10.1210/jc.2019-00177PubMedGoogle Scholar
    ×