, Volume 57, Issue 1, pp 18–34 | Cite as

Systematic reviews of diagnostic tests in endocrinology: an audit of methods, reporting, and performance

  • Gabriela Spencer-Bonilla
  • Naykky Singh Ospina
  • Rene Rodriguez-Gutierrez
  • Juan P. Brito
  • Nicole Iñiguez-Ariza
  • Shrikant Tamhane
  • Patricia J. Erwin
  • M. Hassan Murad
  • Victor M. MontoriEmail author



Systematic reviews provide clinicians and policymakers estimates of diagnostic test accuracy and their usefulness in clinical practice. We identified all available systematic reviews of diagnosis in endocrinology, summarized the diagnostic accuracy of the tests included, and assessed the credibility and clinical usefulness of the methods and reporting.


We searched Ovid MEDLINE, EMBASE, and Cochrane CENTRAL from inception to December 2015 for systematic reviews and meta-analyses reporting accuracy measures of diagnostic tests in endocrinology. Experienced reviewers independently screened for eligible studies and collected data. We summarized the results, methods, and reporting of the reviews. We performed subgroup analyses to categorize diagnostic tests as most useful based on their accuracy.


We identified 84 systematic reviews; half of the tests included were classified as helpful when positive, one-fourth as helpful when negative. Most authors adequately reported how studies were identified and selected and how their trustworthiness (risk of bias) was judged. Only one in three reviews, however, reported an overall judgment about trustworthiness and one in five reported using adequate meta-analytic methods. One in four reported contacting authors for further information and about half included only patients with diagnostic uncertainty.


Up to half of the diagnostic endocrine tests in which the likelihood ratio was calculated or provided are likely to be helpful in practice when positive as are one-quarter when negative. Most diagnostic systematic reviews in endocrine lack methodological rigor, protection against bias, and offer limited credibility. Substantial efforts, therefore, seem necessary to improve the quality of diagnostic systematic reviews in endocrinology.


Diagnostic accuracy Diagnostic systematic review Systematic review methodology 



G.S.B. was supported by CTSA Grant Number TL1TR000137 from the National Center for Advancing Translational Science (NCATS) and Grant Number 3R01HL131535-01S1 from the National Heart Lung and Blood Institute (NHLBI). V.M.M. was partially supported by Grant Number UL1TR000135 from the NCATS, a component of the National Institutes of Health (NIH). Its contents are solely the responsibility of the author and do not necessarily represent the official view of the NIH.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

12020_2017_1298_MOESM1_ESM.docx (63 kb)
Supplementary Information


  1. 1.
    T.C. Hoffmann, C. Del Mar: Clinicians’ expectations of the benefits and harms of treatments, screening, and tests: a systematic review. JAMA Intern. Med. (2017). doi: 10.1001/jamainternmed.2016.8254 CrossRefGoogle Scholar
  2. 2.
    J.R. Ball, E. Balogh, Improving diagnosis in health care: highlights of a report from the national academies of sciences, engineering, and medicine. Ann. Intern. Med. 164(1), 59–61 (2016). doi: 10.7326/m15-2256 CrossRefGoogle Scholar
  3. 3.
    P. Tricoci, J.M. Allen, J.M. Kramer, R.M. Califf, S.C. Smith Jr., Scientific evidence underlying the ACC/AHA clinical practice guidelines. J. Am. Med. Assoc. 301(8), 831–841 (2009). doi: 10.1001/jama.2009.205 CrossRefGoogle Scholar
  4. 4.
    N. Singh Ospina, R. Rodriguez-Gutierrez, J.P. Brito, W.F. Young Jr., V.M. Montori, Is the endocrine research pipeline broken? A systematic evaluation of the endocrine society clinical practice guidelines and trial registration. BMC Med. 13, 187 (2015). doi: 10.1186/s12916-015-0435-z CrossRefPubMedCentralPubMedGoogle Scholar
  5. 5.
    L. Ge, J.C. Wang, J.L. Li, L. Liang, N. An, X.T. Shi, Y.C. Liu, J.H. Tian, The assessment of the quality of reporting of systematic reviews/meta-analyses in diagnostic tests published by authors in China. PLoS ONE 9(1), e85908 (2014). doi: 10.1371/journal.pone.0085908 CrossRefPubMedCentralPubMedGoogle Scholar
  6. 6.
    S. Mallett, J.J. Deeks, S. Halligan, S. Hopewell, V. Cornelius, D.G. Altman, Systematic reviews of diagnostic tests in cancer: review of methods and reporting. Br. Med. J. 333(7565), 413 (2006). doi: 10.1136/bmj.38895.467130.55 CrossRefGoogle Scholar
  7. 7.
    T. McGinn, P.C. Wyer, T.B. Newman, S. Keitz, R. Leipzig, G.G. For, Tips for learners of evidence-based medicine: 3. Measures of observer variability (kappa statistic). Can. Med. Assoc. J. 171(11), 1369–1373 (2004). doi: 10.1503/cmaj.1031981 CrossRefGoogle Scholar
  8. 8.
    L.M. Bachmann, D.B. Bischof, S.A. Bischofberger, M.G. Bonani, F.M. Osann, J. Steurer, Systematic quantitative overviews of the literature to determine the value of diagnostic tests for predicting acute appendicitis: study protocol. BMC Surg. 2, 2 (2002)CrossRefPubMedGoogle Scholar
  9. 9.
    P. Whiting, A.W. Rutjes, J. Dinnes, J.B. Reitsma, P.M. Bossuyt, J. Kleijnen, A systematic review finds that diagnostic reviews fail to incorporate quality despite available tools. J. Clin. Epidemiol. 58(1), 1–12 (2005). doi: 10.1016/j.jclinepi.2004.04.008 CrossRefGoogle Scholar
  10. 10.
    R.M. Harbord, P. Whiting, J.A. Sterne, M. Egger, J.J. Deeks, A. Shang, L.M. Bachmann, An empirical comparison of methods for meta-analysis of diagnostic accuracy showed hierarchical models are necessary. J. Clin. Epidemiol. 61(11), 1095–1103 (2008). doi: 10.1016/j.jclinepi.2007.09.013 CrossRefGoogle Scholar
  11. 11.
    M. Gotthardt, B. Lohmann, T.M. Behr, A. Bauhofer, C. Franzius, M.L. Schipper, M. Wagner, H. Hoffken, H. Sitter, M. Rothmund, K. Joseph, C. Nies, Clinical value of parathyroid scintigraphy with technetium-99m methoxyisobutylisonitrile: discrepancies in clinical data and a systematic metaanalysis of the literature. World J. Surg. 28(1), 100–107 (2004)CrossRefGoogle Scholar
  12. 12.
    S. Nayak, I. Olkin, H. Liu, M. Grabe, M.K. Gould, I.E. Allen, D.K. Owens, D.M. Bravata, Meta-analysis: accuracy of quantitative ultrasound for identifying patients with osteoporosis. Ann. Intern. Med. 144(11), 832–841 (2006)CrossRefGoogle Scholar
  13. 13.
    C. Caldarella, G. Treglia, M.A. Isgro, A. Giordano, Diagnostic performance of positron emission tomography using 11C-methionine in patients with suspected parathyroid adenoma: a meta-analysis. Endocrine 43(1), 78–83 (2013). doi: 10.1007/s12020-012-9746-4 CrossRefGoogle Scholar
  14. 14.
    C. Caldarella, G. Treglia, A. Pontecorvi, A. Giordano, Diagnostic performance of planar scintigraphy using 99mTc-MIBI in patients with secondary hyperparathyroidism: a meta-analysis. Ann. Nucl. Med. 26(10), 794–803 (2012). doi: 10.1007/s12149-012-0643-y CrossRefGoogle Scholar
  15. 15.
    K. Cheung, T.S. Wang, F. Farrokhyar, S.A. Roman, J.A. Sosa, A meta-analysis of preoperative localization techniques for patients with primary hyperparathyroidism. Ann. Surg. Oncol. 19(2), 577–583 (2012). doi: 10.1245/s10434-011-1870-5 CrossRefGoogle Scholar
  16. 16.
    W.J. Wei, C.T. Shen, H.J. Song, Z.L. Qiu, Q.Y. Luo, Comparison of SPET/CT, SPET and planar imaging using 99mTc-MIBI as independent techniques to support minimally invasive parathyroidectomy in primary hyperparathyroidism: a meta-analysis. Hell. J. Nucl. Med. 18(2), 127–135 (2015). doi: 10.1967/s002449910207 CrossRefGoogle Scholar
  17. 17.
    K.K. Wong, L.M. Fig, M.D. Gross, B.A. Dwamena, Parathyroid adenoma localization with 99mTc-sestamibi SPECT/CT: a meta-analysis. Nucl. Med. Commun. 36(4), 363–375 (2015). doi: 10.1097/MNM.0000000000000262 CrossRefGoogle Scholar
  18. 18.
    J. Windeler, J. Kobberling, The fructosamine assay in diagnosis and control of diabetes mellitus scientific evidence for its clinical usefulness? J. Clin. Chem. Clin. Biochem. 28(3), 129–138 (1990)Google Scholar
  19. 19.
    J.E. Jensen, S.H. Nielsen, L. Foged, S.N. Holmegaard, E. Magid, The MICRAL test for diabetic microalbuminuria: predictive values as a function of prevalence. Scand. J. Clin. Lab. Invest. 56(2), 117–122 (1996)CrossRefGoogle Scholar
  20. 20.
    A.L. Peters, M.B. Davidson, D.L. Schriger, V. Hasselblad, A clinical approach for the diagnosis of diabetes mellitus: an analysis using glycosylated hemoglobin levels. Metaanalysis Research Group on the Diagnosis of Diabetes Using Glycated Hemoglobin Levels. [Erratum appears in JAMA 1997 Apr 9;277(14):1125]. J. Am. Med. Assoc. 276(15), 1246–1252 (1996)CrossRefGoogle Scholar
  21. 21.
    E.A. Whitsel, E.J. Boyko, D.S. Siscovick, Reassessing the role of QTc in the diagnosis of autonomic failure among patients with diabetes: a meta-analysis. Diabetes Care 23(2), 241–247 (2000)CrossRefGoogle Scholar
  22. 22.
    B. Ewald, J. Attia, Which test to detect microalbuminuria in diabetic patients? A systematic review. Aust. Fam. Physician 33(7), 565–567, 571 (2004).Google Scholar
  23. 23.
    G. Virgili, F. Menchini, A.F. Dimastrogiovanni, E. Rapizzi, U. Menchini, F. Bandello, R.G. Chiodini, Optical coherence tomography versus stereoscopic fundus photography or biomicroscopy for diagnosing diabetic macular edema: a systematic review. Invest. Ophthalmol. Vis. Sci. 48(11), 4963–4973 (2007)CrossRefGoogle Scholar
  24. 24.
    M.T. Dinh, C.L. Abad, N. Safdar, Diagnostic accuracy of the physical examination and imaging tests for osteomyelitis underlying diabetic foot ulcers: meta-analysis. Clin. Infect. Dis. 47(4), 519–527 (2008). doi: 10.1086/590011 CrossRefGoogle Scholar
  25. 25.
    Y. Feng, F.J. Schlosser, B.E. Sumpio, The Semmes Weinstein monofilament examination as a screening tool for diabetic peripheral neuropathy. J. Vasc. Surg. 50(3), 675–682, 682.e671 (2009). doi: 10.1016/j.jvs.2009.05.017.CrossRefGoogle Scholar
  26. 26.
    B.A. Blomberg, M.C. Moghbel, B. Saboury, C.A. Stanley, A. Alavi, The value of radiologic interventions and 18F-DOPA PET in diagnosing and localizing focal congenital hyperinsulinism: systematic review and meta-analysis. Mol. Imaging Biol. 15(1), 97–105 (2013). doi: 10.1007/s11307-012-0572-0 CrossRefGoogle Scholar
  27. 27.
    Y. Ye, H. Xie, X. Zhao, S. Zhang, The oral glucose tolerance test for the diagnosis of diabetes mellitus in patients during acute coronary syndrome hospitalization: a meta-analysis of diagnostic test accuracy. Cardiovasc. Diabetol. 11, 155 (2012). doi: 10.1186/1475-2840-11-155 CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    S.T. Tang, Q. Zhang, C.J. Wang, H.Q. Tang, T.X. Wu, [Glycosylated hemoglobin A1c for the diagnosis of diabetes mellitus: a meta-analysis]. Chung-Hua Nei Ko Tsa Chih Chinese. J. Intern. Med. 52(1), 21–25 (2013)CrossRefGoogle Scholar
  29. 29.
    Q.W. Tian, C. Xuan, H.W. Wang, J.X. Zhao, W.L. Yu, G. Gao, B.B. Zhang, L.M. Lun, Diagnostic accuracy of glycosylated hemoglobin in Chinese patients with gestational diabetes mellitus: a meta-analysis based on 2,812 patients and 5,918 controls. Genet. Test. Mol. Biomarkers 17(9), 687–695 (2013). doi: 10.1089/gtmb.2013.0099 CrossRefGoogle Scholar
  30. 30.
    S. Yan, S. Liu, Y. Zhao, W. Zhang, X. Sun, J. Li, F. Jiang, J. Ju, N. Lang, Y. Zhang, W. Zhou, Q. Li, Diagnostic accuracy of HbA1c in diabetes between Eastern and Western. Eur. J. Clin. Invest. 43(7), 716–726 (2013). doi: 10.1111/eci.12098 CrossRefPubMedGoogle Scholar
  31. 31.
    J. Yang, R. Hao, X. Zhu, Diagnostic role of 18F-dihydroxyphenylalanine positron emission tomography in patients with congenital hyperinsulinism: a meta-analysis. Nucl. Med. Commun. 34(4), 347–353 (2013). doi: 10.1097/MNM.0b013e32835e6ac6 CrossRefPubMedGoogle Scholar
  32. 32.
    G. Hirschfeld, Mv Glischinski, M. Blankenburg, B. Zernikow, Screening for peripheral neuropathies in children with diabetes: a systematic review. Pediatrics 133(5), e1324–e1330 (2014). doi: 10.1542/peds.2013-3645 CrossRefPubMedGoogle Scholar
  33. 33.
    X. Su, Z. Zhang, X. Qu, Y. Tian, G. Zhang, Hemoglobin A1c for diagnosis of postpartum abnormal glucose tolerance among women with gestational diabetes mellitus: diagnostic meta-analysis. PLoS ONE 9(7), e102144 (2014). doi: 10.1371/journal.pone.0102144 CrossRefPubMedCentralPubMedGoogle Scholar
  34. 34.
    A. Tsapas, A. Liakos, P. Paschos, T. Karagiannis, E. Bekiari, N. Tentolouris, P. Boura, A simple plaster for screening for diabetic neuropathy: a diagnostic test accuracy systematic review and meta-analysis. Metabolism 63(4), 584–592 (2014). doi: 10.1016/j.metabol.2013.11.019 CrossRefPubMedGoogle Scholar
  35. 35.
    H.Y. Wu, Y.S. Peng, C.K. Chiang, J.W. Huang, K.Y. Hung, K.D. Wu, Y.K. Tu, K.L. Chien, Diagnostic performance of random urine samples using albumin concentration vs ratio of albumin to creatinine for microalbuminuria screening in patients with diabetes mellitus: a systematic review and meta-analysis. JAMA Intern. Med. 174(7), 1108–1115 (2014). doi: 10.1001/jamainternmed.2014.1363 CrossRefPubMedGoogle Scholar
  36. 36.
    N. Xu, H. Wu, D. Li, J. Wang, Diagnostic accuracy of glycated hemoglobin compared with oral glucose tolerance test for diagnosing diabetes mellitus in Chinese adults: a meta-analysis. Diabetes Res. Clin. Pract. 106(1), 11–18 (2014). doi: 10.1016/j.diabres.2014.04.010 CrossRefPubMedGoogle Scholar
  37. 37.
    L. Shi, H. Wu, J. Dong, K. Jiang, X. Lu, J. Shi, Telemedicine for detecting diabetic retinopathy: a systematic review and meta-analysis. Br. J. Ophthalmol. 99(6), 823–831 (2015). doi: 10.1136/bjophthalmol-2014-305631 CrossRefPubMedCentralPubMedGoogle Scholar
  38. 38.
    G. Treglia, F. Bertagna, R. Sadeghi, F.A. Verburg, L. Ceriani, L. Giovanella, Focal thyroid incidental uptake detected by 18F-fluorodeoxyglucose positron emission tomography. Meta-analysis on prevalence and malignancy risk. Nucl. Med. 52(4), 130–136 (2013). doi: 10.3413/Nukmed-0568-13-03 CrossRefGoogle Scholar
  39. 39.
    S.R. Puli, N. Kalva, M.L. Bechtold, S.R. Pamulaparthy, M.D. Cashman, N.C. Estes, R.H. Pearl, F.H. Volmar, S. Dillon, M.F. Shekleton, D. Forcione, Diagnostic accuracy of endoscopic ultrasound in pancreatic neuroendocrine tumors: a systematic review and meta analysis. World J. Gastroenterol. 19(23), 3678–3684 (2013). doi: 10.3748/wjg.v19.i23.3678 CrossRefPubMedCentralPubMedGoogle Scholar
  40. 40.
    X. Yang, Y. Yang, Z. Li, C. Cheng, T. Yang, C. Wang, L. Liu, S. Liu, Diagnostic value of circulating chromogranin a for neuroendocrine tumors: a systematic review and meta-analysis. PLoS ONE 10(4), e124884 (2015). doi: 10.1371/journal.pone.0124884 CrossRefGoogle Scholar
  41. 41.
    G.W. Boland, M.J. Lee, G.S. Gazelle, E.F. Halpern, M.M. McNicholas, P.R. Mueller, Characterization of adrenal masses using unenhanced CT: an analysis of the CT literature. Am. J. Roentgenol. 171(1), 201–204 (1998)CrossRefGoogle Scholar
  42. 42.
    R.I. Dorin, C.R. Qualls, L.M. Crapo, Diagnosis of adrenal insufficiency. Ann. Intern. Med. 139(3), 194–204 (2003)CrossRefPubMedGoogle Scholar
  43. 43.
    M.B. Elamin, M.H. Murad, R. Mullan, D. Erickson, K. Harris, S. Nadeem, R. Ennis, P.J. Erwin, V.M. Montori, Accuracy of diagnostic tests for Cushing’s syndrome: a systematic review and metaanalyses. J. Clin. Endocrinol. Metab. 93(5), 1553–1562 (2008). doi: 10.1210/jc.2008-0139 CrossRefGoogle Scholar
  44. 44.
    R. Kazlauskaite, A.T. Evans, C.V. Villabona, T.A. Abdu, B. Ambrosi, A.B. Atkinson, C.H. Choi, R.N. Clayton, C.H. Courtney, E.N. Gonc, M. Maghnie, S.R. Rose, S.G. Soule, K. Tordjman; Adrenal, I.C.f.E.o.C.T.i.H.-P., Corticotropin tests for hypothalamic-pituitary-adrenal insufficiency: a metaanalysis. J. Clin. Endocrinol. Metab. 93(11), 4245–4253 (2008). doi: 10.1210/jc.2008-0710 CrossRefPubMedCentralPubMedGoogle Scholar
  45. 45.
    T. Carroll, H. Raff, J.W. Findling, Late-night salivary cortisol for the diagnosis of Cushing syndrome: a meta-analysis. Endocr. Pract. 15(4), 335–342 (2009). doi: 10.4158/EP09023OR CrossRefGoogle Scholar
  46. 46.
    A.F. Jacobson, H. Deng, J. Lombard, H.J. Lessig, R.R. Black, 123I-meta-iodobenzylguanidine scintigraphy for the detection of neuroblastoma and pheochromocytoma: results of a meta-analysis. J. Clin. Endocrinol. Metab. 95(6), 2596–2606 (2010). doi: 10.1210/jc.2009-2604 CrossRefGoogle Scholar
  47. 47.
    G.W. Boland, B.A. Dwamena, M.J. Sangwaiya, A.G. Goehler, M.A. Blake, P.F. Hahn, J.A. Scott, M.K. Kalra, Characterization of adrenal masses by using FDG PET: a systematic review and meta-analysis of diagnostic test performance. Radiology 259(1), 117–126 (2011). doi: 10.1148/radiol.11100569 CrossRefPubMedGoogle Scholar
  48. 48.
    A. Hazem, M.B. Elamin, G. Malaga, I. Bancos, Y. Prevost, C. Zeballos-Palacios, E.R. Velasquez, P.J. Erwin, N. Natt, V.M. Montori, M.H. Murad, The accuracy of diagnostic tests for GH deficiency in adults: a systematic review and meta-analysis. Eur. J. Endocrinol. 165(6), 841–849 (2011). doi: 10.1530/EJE-11-0476 CrossRefGoogle Scholar
  49. 49.
    S. Iliodromiti, T.W. Kelsey, R.A. Anderson, S.M. Nelson, Can anti-Mullerian hormone predict the diagnosis of polycystic ovary syndrome? A systematic review and meta-analysis of extracted data. J. Clin. Endocrinol. Metab. 98(8), 3332–3340 (2013). doi: 10.1210/jc.2013-1393 CrossRefPubMedGoogle Scholar
  50. 50.
    V. Rufini, G. Treglia, P. Castaldi, G. Perotti, A. Giordano, Comparison of metaiodobenzylguanidine scintigraphy with positron emission tomography in the diagnostic work-up of pheochromocytoma and paraganglioma: a systematic review. Q. J. Nucl. Med. Mol. Imaging 57(2), 122–133 (2013)Google Scholar
  51. 51.
    Y. Shen, J. Zhang, Y. Zhao, Y. Yan, Y. Liu, J. Cai, Diagnostic value of serum IGF-1 and IGFBP-3 in growth hormone deficiency: a systematic review with meta-analysis. Eur. J. Pediatr. 174(4), 419–427 (2015). doi: 10.1007/s00431-014-2406-3 CrossRefGoogle Scholar
  52. 52.
    C.F. Eustatia-Rutten, J.W. Smit, J.A. Romijn, E.Pvd Kleij-Corssmit, A.M. Pereira, M.P. Stokkel, J. Kievit, Diagnostic value of serum thyroglobulin measurements in the follow-up of differentiated thyroid carcinoma, a structured meta-analysis. Clin. Endocrinol. 61(1), 61–74 (2004)CrossRefGoogle Scholar
  53. 53.
    V. Rufini, G. Treglia, F. Montravers, A. Giordano, Diagnostic accuracy of [18F]DOPA PET and PET/CT in patients with neuroendocrine tumors: a meta-analysis. Clin. Transl. Imaging 1(2), 111–122 (2013). doi: 10.1007/s40336-013-0005-3 CrossRefGoogle Scholar
  54. 54.
    L. Peng, M.J. Gu, Diagnostic value of conventional and ultrasound-guided fine-needle aspiration biopsy for thyroid nodules: a meta-analysis. [Chinese]. Acad. J. Second Mil. Med. Univ. 28(9), 968–972 (2007)Google Scholar
  55. 55.
    Y. Peng, H.H. Wang, A meta-analysis of comparing fine-needle aspiration and frozen section for evaluating thyroid nodules. Diagn. Cytopathol. 36(12), 916–920 (2008). doi: 10.1002/dc.20943 CrossRefGoogle Scholar
  56. 56.
    P.G. Raijmakers, M.A. Paul, P. Lips, Sentinel node detection in patients with thyroid carcinoma: a meta-analysis. World J. Surg. 32(9), 1961–1967 (2008). doi: 10.1007/s00268-008-9657-y CrossRefPubMedCentralPubMedGoogle Scholar
  57. 57.
    M.J. Dong, Z.F. Liu, K. Zhao, L.X. Ruan, G.L. Wang, S.Y. Yang, F. Sun, X.G. Luo, Value of 18F-FDG-PET/PET-CT in differentiated thyroid carcinoma with radioiodine-negative whole-body scan: a meta-analysis. Nucl. Med. Commun. 30(8), 639–650 (2009). doi: 10.1097/MNM.0b013e32832dcfa7 CrossRefGoogle Scholar
  58. 58.
    C. Stevens, J.K. Lee, M. Sadatsafavi, G.K. Blair, Pediatric thyroid fine-needle aspiration cytology: a meta-analysis. J. Pediatr. Surg. 44(11), 2184–2191 (2009). doi: 10.1016/j.jpedsurg.2009.07.022 CrossRefGoogle Scholar
  59. 59.
    J. Bojunga, E. Herrmann, G. Meyer, S. Weber, S. Zeuzem, M. Friedrich-Rust, Real-time elastography for the differentiation of benign and malignant thyroid nodules: a meta-analysis. Thyroid 20(10), 1145–1150 (2010). doi: 10.1089/thy.2010.0079 CrossRefGoogle Scholar
  60. 60.
    W. Iared, D.C. Shigueoka, J.C. Cristofoli, R. Andriolo, A.N. Atallah, S.A. Ajzen, O. Valente, Use of color Doppler ultrasonography for the prediction of malignancy in follicular thyroid neoplasms: systematic review and meta-analysis. J. Ultrasound Med. 29(3), 419–425 (2010)CrossRefGoogle Scholar
  61. 61.
    M.E. Miller, Q. Chen, D. Elashoff, E. Abemayor, M.S. John, Positron emission tomography and positron emission tomography-CT evaluation for recurrent papillary thyroid carcinoma: meta-analysis and literature review. Head Neck 33(4), 562–565 (2011). doi: 10.1002/hed.21492 CrossRefGoogle Scholar
  62. 62.
    D. Vriens, J.Hd Wilt, G.Jvd Wilt, R.T. Netea-Maier, W.J. Oyen, L.Fd Geus-Oei, The role of [18F]-2-fluoro-2-deoxy-d-glucose-positron emission tomography in thyroid nodules with indeterminate fine-needle aspiration biopsy: systematic review and meta-analysis of the literature. Cancer 117(20), 4582–4594 (2011). doi: 10.1002/cncr.26085 CrossRefGoogle Scholar
  63. 63.
    M. Bongiovanni, A. Spitale, W.C. Faquin, L. Mazzucchelli, Z.W. Baloch, The Bethesda system for reporting thyroid cytopathology: a meta-analysis. Acta Cytol. 56(4), 333–339 (2012). doi: 10.1159/000339959 CrossRefGoogle Scholar
  64. 64.
    X. Cheng, L. Bao, Z. Xu, D. Li, J. Wang, Y. Li, 18F-FDG-PET and 18F-FDG-PET/CT in the detection of recurrent or metastatic medullary thyroid carcinoma: a systematic review and meta-analysis. J. Med. Imaging Radiat. Oncol. 56(2), 136–142 (2012). doi: 10.1111/j.1754-9485.2012.02344.x CrossRefGoogle Scholar
  65. 65.
    L.Ld Matos, A.B.D. Giglio, C.O. Matsubayashi, Md.L. Farah, A.D. Giglio, M.Ad.S. Pinhal, Expression of CK-19, galectin-3 and HBME-1 in the differentiation of thyroid lesions: systematic review and diagnostic meta-analysis. Diagn. Pathol. 7, 97 (2012). doi: 10.1186/1746-1596-7-97 CrossRefPubMedCentralPubMedGoogle Scholar
  66. 66.
    R. Tozzoli, M. Bagnasco, D. Giavarina, N. Bizzaro, TSH receptor autoantibody immunoassay in patients with Graves’ disease: improvement of diagnostic accuracy over different generations of methods. Systematic review and meta-analysis. Autoimmun. Rev. 12(2), 107–113 (2012). doi: 10.1016/j.autrev.2012.07.003 CrossRefGoogle Scholar
  67. 67.
    L.M. Wu, H.Y. Gu, X.H. Qu, J. Zheng, W. Zhang, Y. Yin, J.R. Xu, 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. 81(8), 1798–1805 (2012). doi: 10.1016/j.ejrad.2011.04.028 CrossRefGoogle Scholar
  68. 68.
    S.A. Razavi, T.A. Hadduck, G. Sadigh, B.A. Dwamena, Comparative effectiveness of elastographic and B-mode ultrasound criteria for diagnostic discrimination of thyroid nodules: a meta-analysis. Am. J. Roentgenol. 200(6), 1317–1326 (2013). doi: 10.2214/AJR.12.9215 CrossRefGoogle Scholar
  69. 69.
    G. Treglia, C. Caldarella, E. Saggiorato, L. Ceriani, F. Orlandi, M. Salvatori, L. Giovanella, Diagnostic performance of (99m)Tc-MIBI scan in predicting the malignancy of thyroid nodules: a meta-analysis. Endocrine 44(1), 70–78 (2013). doi: 10.1007/s12020-013-9932-z CrossRefGoogle Scholar
  70. 70.
    N. Wang, H. Zhai, Y. Lu, Is fluorine-18 fluorodeoxyglucose positron emission tomography useful for the thyroid nodules with indeterminate fine needle aspiration biopsy? A meta-analysis of the literature. [Erratum appears in J Otolaryngol Head Neck Surg.2014;43():43]. J. Otolaryngol. Head Neck Surg. 42, 38 (2013). doi: 10.1186/1916-0216-42-38 CrossRefPubMedCentralPubMedGoogle Scholar
  71. 71.
    B. Zhang, X. Ma, N. Wu, L. Liu, X. Liu, J. Zhang, J. Yang, T. Niu, Shear wave elastography for differentiation of benign and malignant thyroid nodules: a meta-analysis. J. Ultrasound Med. 32(12), 2163–2169 (2013). doi: 10.7863/ultra.32.12.2163 CrossRefGoogle Scholar
  72. 72.
    J.P. Brito, M.R. Gionfriddo, A.A. Nofal, K.R. Boehmer, A.L. Leppin, C. Reading, M. Callstrom, T.A. Elraiyah, L.J. Prokop, M.N. Stan, M.H. Murad, J.C. Morris, V.M. Montori, The accuracy of thyroid nodule ultrasound to predict thyroid cancer: systematic review and meta-analysis. J. Clin. Endocrinol. Metab. 99(4), 1253–1263 (2014). doi: 10.1210/jc.2013-2928 CrossRefGoogle Scholar
  73. 73.
    M. Ghajarzadeh, F. Sodagari, M. Shakiba, Diagnostic accuracy of sonoelastography in detecting malignant thyroid nodules: a systematic review and meta-analysis. Am. J. Roentgenol. 202(4), W379–W389 (2014). doi: 10.2214/AJR.12.9785 CrossRefGoogle Scholar
  74. 74.
    L. Giovanella, G. Treglia, R. Sadeghi, P. Trimboli, L. Ceriani, F.A. Verburg, Unstimulated highly sensitive thyroglobulin in follow-up of differentiated thyroid cancer patients: a meta-analysis. J. Clin. Endocrinol. Metab. 99(2), 440–447 (2014). doi: 10.1210/jc.2013-3156 CrossRefGoogle Scholar
  75. 75.
    G. Grani, A. Fumarola, Thyroglobulin in lymph node fine-needle aspiration washout: a systematic review and meta-analysis of diagnostic accuracy. J. Clin. Endocrinol. Metab. 99(6), 1970–1982 (2014). doi: 10.1210/jc.2014-1098 CrossRefGoogle Scholar
  76. 76.
    Y. Jia, Y. Yu, X. Li, S. Wei, X. Zheng, X. Yang, J. Zhao, T. Xia, M. Gao, Diagnostic value of B-RAF(V600E) in difficult-to-diagnose thyroid nodules using fine-needle aspiration: systematic review and meta-analysis. Diagn. Cytopathol. 42(1), 94–101 (2014). doi: 10.1002/dc.23044 CrossRefGoogle Scholar
  77. 77.
    L. Li, B.D. Chen, H.F. Zhu, S. Wu, D. Wei, J.Q. Zhang, L. Yu, Comparison of pre-operation diagnosis of thyroid cancer with fine needle aspiration and core-needle biopsy: a meta-analysis. Asian Pac. J. Cancer Prev. 15(17), 7187–7193 (2014)CrossRefGoogle Scholar
  78. 78.
    Y.Y. Ma, X.G. Zhang, P. Paerhati, Y. Mu, N. Tieliewuhan, Value of ultrasonographic elastography in differential diagnosis of benign/malignant thyroid nodules in China: a meta-analysis. [Chinese]. Chin. J. Evid. Based Med. 14(5), 584–591 (2014). doi: 10.7507/1672-2531.20140097 CrossRefGoogle Scholar
  79. 79.
    N. Qu, L. Zhang, Z.W. Lu, W.J. Wei, Y. Zhang, Q.H. Ji, Risk of malignancy in focal thyroid lesions identified by (18)F-fluorodeoxyglucose positron emission tomography or positron emission tomography/computed tomography: evidence from a large series of studies. Tumour Biol. 35(6), 6139–6147 (2014). doi: 10.1007/s13277-014-1813-4 CrossRefGoogle Scholar
  80. 80.
    B.S. Sheffield, H. Masoudi, B. Walker, S.M. Wiseman, Preoperative diagnosis of thyroid nodules using the Bethesda system for reporting thyroid cytopathology: a comprehensive review and meta-analysis. Expert Rev. Endocrinol. Metab. 9(2), 97–110 (2014). doi: 10.1586/17446651.2014.887435 CrossRefGoogle Scholar
  81. 81.
    J. Sun, J. Cai, X. Wang, Real-time ultrasound elastography for differentiation of benign and malignant thyroid nodules: a meta-analysis. J. Ultrasound Med. 33(3), 495–502 (2014). doi: 10.7863/ultra.33.3.495 CrossRefGoogle Scholar
  82. 82.
    A. Wale, K.A. Miles, B. Young, C. Zammit, A. Williams, J. Quin, S. Dizdarevic, Combined (99m)Tc-methoxyisobutylisonitrile scintigraphy and fine-needle aspiration cytology offers an accurate and potentially cost-effective investigative strategy for the assessment of solitary or dominant thyroid nodules. Eur. J. Nuclear Med. Mol. Imaging 41(1), 105–115 (2014). doi: 10.1007/s00259-013-2546-0 CrossRefGoogle Scholar
  83. 83.
    X. Wei, Y. Li, S. Zhang, M. Gao, Thyroid imaging reporting and data system (TI-RADS) in the diagnostic value of thyroid nodules: a systematic review. Tumour Biol. 35(7), 6769–6776 (2014). doi: 10.1007/s13277-014-1837-9 CrossRefGoogle Scholar
  84. 84.
    K. Wolinski, M. Szkudlarek, E. Szczepanek-Parulska, M. Ruchala, Usefulness of different ultrasound features of malignancy in predicting the type of thyroid lesions: a meta-analysis of prospective studies. Pol. Arch. Med. Wewn. 124(3), 97–104 (2014)Google Scholar
  85. 85.
    D. Yu, Y. Han, T. Chen, Contrast-enhanced ultrasound for differentiation of benign and malignant thyroid lesions: meta-analysis. Otolaryngol. Head Neck Surg. 151(6), 909–915 (2014). doi: 10.1177/0194599814555838 CrossRefGoogle Scholar
  86. 86.
    Y. Zhang, Q. Zhong, X. Chen, J. Fang, Z. Huang, Diagnostic value of microRNAs in discriminating malignant thyroid nodules from benign ones on fine-needle aspiration samples. Tumour Biol. 35(9), 9343–9353 (2014). doi: 10.1007/s13277-014-2209-1 CrossRefGoogle Scholar
  87. 87.
    F.J. Dong, M. Li, Y. Jiao, J.F. Xu, Y. Xiong, L. Zhang, H. Luo, Z.M. Ding, Acoustic radiation force impulse imaging for detecting thyroid nodules: a systematic review and pooled meta-analysis. Med. Ultrason. 17(2), 192–199 (2015). doi: 10.11152/mu.2013.2066.172.hyr CrossRefGoogle Scholar
  88. 88.
    S. Nell, J.W. Kist, T.P. Debray, Bd Keizer, T.Jv Oostenbrugge, I.H.B. Rinkes, G.D. Valk, M.R. Vriens, Qualitative elastography can replace thyroid nodule fine-needle aspiration in patients with soft thyroid nodules. A systematic review and meta-analysis. Eur. J. Radiol. 84(4), 652–661 (2015). doi: 10.1016/j.ejrad.2015.01.003 CrossRefGoogle Scholar
  89. 89.
    J.S. Pyo, J.H. Sohn, G. Kang, BRAF immunohistochemistry using clone VE1 is strongly concordant with BRAF(V600E) mutation test in papillary thyroid carcinoma. Endocr. Pathol. 26(3), 211–217 (2015). doi: 10.1007/s12022-015-9374-7 CrossRefGoogle Scholar
  90. 90.
    L.R. Remonti, C.K. Kramer, C.B. Leitao, L.C. Pinto, J.L. Gross, Thyroid ultrasound features and risk of carcinoma: a systematic review and meta-analysis of observational studies. Thyroid 25(5), 538–550 (2015). doi: 10.1089/thy.2014.0353 CrossRefPubMedCentralPubMedGoogle Scholar
  91. 91.
    V. Veer, S. Puttagunta, The role of elastography in evaluating thyroid nodules: a literature review and meta-analysis. Eur. Arch. Otorhinolaryngol. 272(8), 1845–1855 (2015). doi: 10.1007/s00405-014-3155-7 CrossRefGoogle Scholar
  92. 92.
    G.J. Zhou, M. Xiao, L.N. Zhao, J.G. Tang, L. Zhang, MicroRNAs as novel biomarkers for the differentiation of malignant versus benign thyroid lesions: a meta-analysis. Genet. Mol. Res. 14(3), 7279–7289 (2015). doi: 10.4238/2015.July.3.3 CrossRefGoogle Scholar
  93. 93.
    G. Treglia, P. Castaldi, G. Rindi, A. Giordano, V. Rufini, Diagnostic performance of Gallium-68 somatostatin receptor PET and PET/CT in patients with thoracic and gastroenteropancreatic neuroendocrine tumours: a meta-analysis. Endocrine 42(1), 80–87 (2012). doi: 10.1007/s12020-012-9631-1 CrossRefGoogle Scholar
  94. 94.
    P. Lin, M. Chen, B. Liu, S. Wang, X. Li, Diagnostic performance of shear wave elastography in the identification of malignant thyroid nodules: a meta-analysis. Eur. Radiol. 24(11), 2729–2738 (2014). doi: 10.1007/s00330-014-3320-9 CrossRefGoogle Scholar
  95. 95.
    L.M. Wu, X.X. Chen, Y.L. Li, J. Hua, J. Chen, J. Hu, J.R. Xu, On the utility of quantitative diffusion-weighted MR imaging as a tool in differentiation between malignant and benign thyroid nodules. Acad. Radiol. 21(3), 355–363 (2014). doi: 10.1016/j.acra.2013.10.008 CrossRefGoogle Scholar
  96. 96.
    H. Honest, K.S. Khan, Reporting of measures of accuracy in systematic reviews of diagnostic literature. BMC Health Serv. Res. 2, 4 (2002)CrossRefPubMedGoogle Scholar
  97. 97.
    Y. Ben-Shlomo, S.M. Collin, J. Quekett, J.A. Sterne, P. Whiting, Presentation of diagnostic information to doctors may change their interpretation and clinical management: a web-based randomised controlled trial. PLoS ONE 10(7), e0128637 (2015). doi: 10.1371/journal.pone.0128637 CrossRefPubMedCentralPubMedGoogle Scholar
  98. 98.
    J.G. Lijmer, B.W. Mol, S. Heisterkamp, G.J. Bonsel, M.H. Prins, J.H. van der Meulen, P.M. Bossuyt, Empirical evidence of design-related bias in studies of diagnostic tests. J. Am. Med. Assoc. 282(11), 1061–1066 (1999)CrossRefGoogle Scholar
  99. 99.
    A.W. Rutjes, J.B. Reitsma, M. Di Nisio, N. Smidt, J.C. van Rijn, P.M. Bossuyt, Evidence of bias and variation in diagnostic accuracy studies. Can. Med. Assoc. J. 174(4), 469–476 (2006). doi: 10.1503/cmaj.050090 CrossRefGoogle Scholar
  100. 100.
    R.J. Mullan, D.N. Flynn, B. Carlberg, I.M. Tleyjeh, C.C. Kamath, M.L. LaBella, P.J. Erwin, G.H. Guyatt, V.M. Montori, Systematic reviewers commonly contact study authors but do so with limited rigor. J. Clin. Epidemiol. 62(2), 138–142 (2009). doi: 10.1016/j.jclinepi.2008.08.002 CrossRefGoogle Scholar
  101. 101.
    S.S. Selph, A.D. Ginsburg, R. Chou, Impact of contacting study authors to obtain additional data for systematic reviews: diagnostic accuracy studies for hepatic fibrosis. Syst. Rev. 3, 107 (2014). doi: 10.1186/2046-4053-3-107 CrossRefPubMedCentralPubMedGoogle Scholar
  102. 102.
    M.M.G. Leeflang, A.W.S. Rutjes, J.B. Reitsma, L. Hooft, P.M.M. Bossuyt, Variation of a test’s sensitivity and specificity with disease prevalence. Can. Med. Assoc. J. 185(11), E537–E544 (2013). doi: 10.1503/cmaj.121286 CrossRefGoogle Scholar
  103. 103.
    Y. Takwoingi, B. Guo, R.D. Riley, J.J. Deeks Performance of methods for meta-analysis of diagnostic test accuracy with few studies or sparse data. Stat. Methods Med. Res. (2015). doi: 10.1177/0962280215592269 CrossRefPubMedGoogle Scholar
  104. 104.
    E.A. Ochodo, J.B. Reitsma, P.M. Bossuyt, M.M.G. Leeflang, Survey revealed a lack of clarity about recommended methods for meta-analysis of diagnostic accuracy data. J. Clin. Epidemiol. 66(11), 1281–1288 (2013). doi: 10.1016/j.jclinepi.2013.05.015 CrossRefGoogle Scholar
  105. 105.
    H.J. Schunemann, A.D. Oxman, J. Brozek, P. Glasziou, R. Jaeschke, G.E. Vist, J.W. Williams Jr., R. Kunz, J. Craig, V.M. Montori, P. Bossuyt, G.H. Guyatt, G.W. Group, Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. Br. Med. J. 336(7653), 1106–1110 (2008). doi: 10.1136/bmj.39500.677199.AE CrossRefGoogle Scholar
  106. 106.
    A. Liberati, D.G. Altman, J. Tetzlaff, C. Mulrow, P.C. Gotzsche, J.P. Ioannidis, M. Clarke, P.J. Devereaux, J. Kleijnen, D. Moher, The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J. Clin. Epidemiol. 62(10), e1–e34 (2009). doi: 10.1016/j.jclinepi.2009.06.006 CrossRefGoogle Scholar
  107. 107.
    W.L. Deville, F. Buntinx, L.M. Bouter, V.M. Montori, H.C. de Vet, D.A. van der Windt, P.D. Bezemer, Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med. Res. Methodol. 2, 9 (2002)CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Gabriela Spencer-Bonilla
    • 1
    • 2
  • Naykky Singh Ospina
    • 1
    • 3
  • Rene Rodriguez-Gutierrez
    • 1
    • 4
  • Juan P. Brito
    • 1
    • 5
  • Nicole Iñiguez-Ariza
    • 5
  • Shrikant Tamhane
    • 1
    • 5
  • Patricia J. Erwin
    • 6
  • M. Hassan Murad
    • 1
    • 7
  • Victor M. Montori
    • 1
    • 5
    Email author
  1. 1.Knowledge and Evaluation Research UnitMayo ClinicRochesterUSA
  2. 2.School of MedicineUniversity of Puerto Rico Medical Sciences CampusSan JuanUSA
  3. 3.Division of Endocrinology, Diabetes and Metabolism, Department of MedicineUniversity of FloridaGainesvilleUSA
  4. 4.Division of Endocrinology, Department of Internal MedicineUniversity Hospital “Dr. Jose E. Gonzalez”, Autonomous University of Nuevo LeonMonterreyUSA
  5. 5.Division of Endocrinology, Diabetes, Metabolism, and NutritionMayo ClinicRochesterUSA
  6. 6.Mayo Medical LibraryMayo ClinicRochesterUSA
  7. 7.Division of Preventive, Occupational, and Aerospace MedicineMayo ClinicRochesterUSA

Personalised recommendations