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Diffusion-weighted imaging does not seem to be a predictor of consistency in pituitary adenomas

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Abstract

Purpose

To prospectively evaluate the usefulness of T1-weighted imaging (T1WI) and diffusion-weighted imaging (DWI) sequences in predicting the consistency of macroadenomas. In addition, to determine their values ​​as prognostic factors of surgical outcomes.

Methods

Patients with pituitary macroadenoma and surgical indication were included. All patients underwent pre-surgical magnetic resonance imaging (MRI) that included the sequences T1WI before and after contrast administration and DWI with the apparent diffusion coefficient (ADC) map. Post-surgical MRI was performed at least 3 months after surgery. The consistency of the macroadenomas was evaluated at surgery, and they were grouped into soft and intermediate/hard adenomas. Mean ADC values, signal on T1WI and the ratio of tumor ADC values ​​to pons (ADCR) were compared with tumor consistency and grade of surgical resection.

Results

A total of 80 patients were included. A softened consistency was found at surgery in 53 patients and hardened in 27 patients. The median ADC in the soft consistency group was 0.532 × 10–3 mm2/sec (0.306 – 1.096 × 10–3 mm2/sec), and in the intermediate/hard consistency group was 0.509 × 10–3 mm2/sec (0.308 − 0.818 × 10–3 mm2/sec). There was no significant difference between the median values ​​of ADC, ADCR and signal on T1W between the soft and hard tumor groups, or between patients with and without tumor residue.

Conclusion

Our results did not show usefulness of the DWI and T1WI for assessing the consistency of pituitary macroadenomas, nor as a predictor of the degree of surgical resection.

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Data availability

No datasets were generated or analysed during the current study.

References

  1. Theodros D, Patel M, Ruzevick J, Lim M, Bettegowda C (2015) Pituitary adenomas: historical perspective, surgical management and future directions. CNS Oncol 4(6):411–429. https://doi.org/10.2217/cns.15.21. (in eng)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Rumboldt Z (2005) Pituitary adenomas. Top Magn Reson Imaging 16(4):277–288. https://doi.org/10.1097/01.rmr.0000224684.76006.cf. (in eng)

    Article  PubMed  Google Scholar 

  3. Vieira L et al (2016) A review on the diagnosis and treatment of patients with clinically nonfunctioning pituitary adenoma by the Neuroendocrinology Department of the Brazilian Society of Endocrinology and Metabolism. Arch Endocrinol Metab 60(4):374–390. https://doi.org/10.1590/2359-3997000000179. (in eng)

    Article  PubMed  Google Scholar 

  4. Lake MG, Krook LS, Cruz SV (2013) Pituitary adenomas: an overview. Am Fam Physician 88(5):319–327 (in eng)

    PubMed  Google Scholar 

  5. Bashari WA et al (2019) Modern imaging of pituitary adenomas. Best Pract Res Clin Endocrinol Metab 33(2):101278. https://doi.org/10.1016/j.beem.2019.05.002. (in eng)

    Article  PubMed  Google Scholar 

  6. Buchfelder M, Schlaffer SM, Zhao Y (2019) The optimal surgical techniques for pituitary tumors. Best Pract Res Clin Endocrinol Metab 33(2):101299. https://doi.org/10.1016/j.beem.2019.101299. (in eng)

    Article  CAS  PubMed  Google Scholar 

  7. Tampourlou M et al (2017) Outcome of nonfunctioning pituitary adenomas that regrow after primary treatment: a study from two large UK Centers. J Clin Endocrinol Metab 102(6):1889–1897. https://doi.org/10.1210/jc.2016-4061. (in eng)

    Article  PubMed  Google Scholar 

  8. Vargas G et al (2015) Clinical characteristics and treatment outcome of 485 patients with nonfunctioning pituitary macroadenomas. Int J Endocrinol 2015:756069. https://doi.org/10.1155/2015/756069. (in eng)

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ramírez C et al (2012) Expression of Ki-67, PTTG1, FGFR4, and SSTR 2, 3, and 5 in nonfunctioning pituitary adenomas: a high throughput TMA, immunohistochemical study. J Clin Endocrinol Metab 97(5):1745–1751. https://doi.org/10.1210/jc.2011-3163. (in eng)

    Article  CAS  PubMed  Google Scholar 

  10. Castinetti F et al (2015) Non-functioning pituitary adenoma: when and how to operate? What pathologic criteria for typing? Ann Endocrinol (Paris) 76(3):220–227. https://doi.org/10.1016/j.ando.2015.04.007. (in eng)

    Article  PubMed  Google Scholar 

  11. Fiore G et al (2023) Predicting tumor consistency and extent of resection in non-functioning pituitary tumors. Pituitary 26(2):209–220. https://doi.org/10.1007/s11102-023-01302-x. (in eng)

    Article  PubMed  Google Scholar 

  12. AcitoresCancela A, Rodríguez Berrocal V, Pian H, Martínez San Millán JS, Díez JJ, Iglesias P (2021) Clinical relevance of tumor consistency in pituitary adenoma. Hormones (Athens) 20(3):463–473. https://doi.org/10.1007/s42000-021-00302-5. (in eng)

    Article  Google Scholar 

  13. Alimohamadi M et al (2014) Predictive value of diffusion-weighted MRI for tumor consistency and resection rate of nonfunctional pituitary macroadenomas. Acta Neurochir (Wien) 156(12):2245–2252. https://doi.org/10.1007/s00701-014-2259-6. (in eng)

    Article  PubMed  Google Scholar 

  14. AcitoresCancela A, Rodríguez Berrocal V, Pian Arias H, Díez JJ, Iglesias P (2022) Effect of pituitary adenoma consistency on surgical outcomes in patients undergoing endonasal endoscopic transsphenoidal surgery. Endocrine 78(3):559–569. https://doi.org/10.1007/s12020-022-03161-1. (in eng)

    Article  CAS  Google Scholar 

  15. Pierallini A et al (2006) Pituitary macroadenomas: preoperative evaluation of consistency with diffusion-weighted MR imaging–initial experience. Radiology 239(1):223–231. https://doi.org/10.1148/radiol.2383042204. (in eng)

    Article  PubMed  Google Scholar 

  16. Mohamed FF, Abouhashem S (2013) Diagnostic value of apparent diffusion coefficient (ADC) in assessment of pituitary macroadenoma consistency. Egypt J Radiol Nucl Med 44(3):617–624

    Article  Google Scholar 

  17. Yiping L, Ji X, Daoying G, Bo Y (2016) Prediction of the consistency of pituitary adenoma: a comparative study on diffusion-weighted imaging and pathological results. J Neuroradiol 43(3):186–194. https://doi.org/10.1016/j.neurad.2015.09.003. (in eng)

    Article  PubMed  Google Scholar 

  18. Bahuleyan B, Raghuram L, Rajshekhar V, Chacko AG (2006) To assess the ability of MRI to predict consistency of pituitary macroadenomas. Br J Neurosurg 20(5):324–326. https://doi.org/10.1080/02688690601000717. (in eng)

    Article  CAS  PubMed  Google Scholar 

  19. Iuchi T, Saeki N, Tanaka M, Sunami K, Yamaura A (1998) MRI prediction of fibrous pituitary adenomas. Acta Neurochir (Wien) 140(8):779–786. https://doi.org/10.1007/s007010050179. (in eng)

    Article  CAS  PubMed  Google Scholar 

  20. Thotakura AK, Patibandla MR, Panigrahi MK, Mahadevan A (2017) Is it really possible to predict the consistency of a pituitary adenoma preoperatively? Neurochirurgie 63(6):453–457. https://doi.org/10.1016/j.neuchi.2017.06.003. (in eng)

    Article  CAS  PubMed  Google Scholar 

  21. Smith KA, Leever JD, Chamoun RB (2015) Prediction of consistency of pituitary adenomas by magnetic resonance imaging. J Neurol Surg B Skull Base 76(5):340–343. https://doi.org/10.1055/s-0035-1549005. (in eng)

    Article  PubMed  PubMed Central  Google Scholar 

  22. Suzuki C et al (2007) Apparent diffusion coefficient of pituitary macroadenoma evaluated with line-scan diffusion-weighted imaging. J Neuroradiol 34(4):228–235. https://doi.org/10.1016/j.neurad.2007.06.007. (in eng)

    Article  CAS  PubMed  Google Scholar 

  23. Wei L, Lin SA, Fan K, Xiao D, Hong J, Wang S (2015) Relationship between pituitary adenoma texture and collagen content revealed by comparative study of MRI and pathology analysis. Int J Clin Exp Med 8(8):12898–12905 (in eng)

    PubMed  PubMed Central  Google Scholar 

  24. Mahmoud OM et al (2010) Role of PROPELLER diffusion weighted imaging and apparent diffusion coefficient in the diagnosis of sellar and parasellar lesions. Eur J Radiol 74(3):420–427. https://doi.org/10.1016/j.ejrad.2009.03.031. (in eng)

    Article  PubMed  Google Scholar 

  25. Su CQ et al (2020) Texture analysis of high b-value diffusion-weighted imaging for evaluating consistency of pituitary macroadenomas. J Magn Reson Imaging 51(5):1507–1513. https://doi.org/10.1002/jmri.26941. (in eng)

    Article  PubMed  Google Scholar 

  26. Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R (2006) Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics 26(Suppl 1):S205–S223. https://doi.org/10.1148/rg.26si065510. (in eng)

    Article  PubMed  Google Scholar 

  27. Drake-Pérez M, Boto J, Fitsiori A, Lovblad K, Vargas MI (2018) Clinical applications of diffusion weighted imaging in neuroradiology. Insights Imaging 9(4):535–547. https://doi.org/10.1007/s13244-018-0624-3. (in eng)

    Article  PubMed  PubMed Central  Google Scholar 

  28. Provenzale JM, Engelter ST, Petrella JR, Smith JS, MacFall JR (1999) Use of MR exponential diffusion-weighted images to eradicate T2 shine-through effect. AJR Am J Roentgenol 172(2):537–539. https://doi.org/10.2214/ajr.172.2.9930819. (in eng)

    Article  CAS  PubMed  Google Scholar 

  29. Minosse S, Marzi S, Piludu F, Vidiri A (2017) Correlation study between DKI and conventional DWI in brain and head and neck tumors. Magn Reson Imaging 42:114–122. https://doi.org/10.1016/j.mri.2017.06.006. (in eng)

    Article  PubMed  Google Scholar 

  30. Schaefer PW, Grant PE, Gonzalez RG (2000) Diffusion-weighted MR imaging of the brain. Radiology 217(2):331–345. https://doi.org/10.1148/radiology.217.2.r00nv24331. (in eng)

    Article  CAS  PubMed  Google Scholar 

  31. Lu L, Wan X, Xu Y, Chen J, Shu K, Lei T (2022) Prognostic factors for recurrence in pituitary adenomas: recent progress and future directions. Diagnostics (Basel). https://doi.org/10.3390/diagnostics12040977. (in eng)

    Article  PubMed  PubMed Central  Google Scholar 

  32. Fleseriu M et al (2021) Consensus on diagnosis and management of Cushing’s disease: a guideline update. Lancet Diab Endocrinol 9(12):847–875. https://doi.org/10.1016/S2213-8587(21)00235-7. (in eng)

    Article  Google Scholar 

  33. Giustina A et al (2020) Multidisciplinary management of acromegaly: a consensus. Rev Endocr Metab Disord 21(4):667–678. https://doi.org/10.1007/s11154-020-09588-z. (in eng)

    Article  PubMed  PubMed Central  Google Scholar 

  34. Melmed S et al (2011) Diagnosis and treatment of hyperprolactinemia: an endocrine society clinical practice guideline. J Clin Endocrinol Metab 96(2):273–288. https://doi.org/10.1210/jc.2010-1692. (in eng)

    Article  CAS  PubMed  Google Scholar 

  35. Knosp E, Steiner E, Kitz K, Matula C (1993) Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings. Neurosurgery 33(4):610–617. https://doi.org/10.1227/00006123-199310000-00008. (in eng)

    Article  CAS  PubMed  Google Scholar 

  36. Micko AS, Wöhrer A, Wolfsberger S, Knosp E (2015) Invasion of the cavernous sinus space in pituitary adenomas: endoscopic verification and its correlation with an MRI-based classification. J Neurosurg 122(4):803–811. https://doi.org/10.3171/2014.12.JNS141083. (in eng)

    Article  PubMed  Google Scholar 

  37. Edal AL, Skjödt K, Nepper-Rasmussen HJ (1997) SIPAP—a new MR classification for pituitary adenomas. Suprasellar, infrasellar, parasellar, anterior and posterior. Acta Radiol 38(1):30–36. https://doi.org/10.1080/02841859709171238. (in eng)

    Article  CAS  PubMed  Google Scholar 

  38. Bonneville JF (2016) Magnetic resonance imaging of pituitary tumors. Front Horm Res 45:97–120. https://doi.org/10.1159/000442327. (in eng)

    Article  PubMed  Google Scholar 

  39. Romano A et al (2017) Predictive role of dynamic contrast enhanced T1-weighted MR sequences in pre-surgical evaluation of macroadenomas consistency. Pituitary 20(2):201–209. https://doi.org/10.1007/s11102-016-0760-z. (in eng)

    Article  PubMed  Google Scholar 

  40. Gadelha MR, Barbosa MA, Lamback EB, Wildemberg LE, Kasuki L, Ventura N (2022) Pituitary MRI standard and advanced sequences: role in the diagnosis and characterization of pituitary adenomas. J Clin Endocrinol Metab 107(5):1431–1440. https://doi.org/10.1210/clinem/dgab901. (in eng)

    Article  PubMed  Google Scholar 

  41. Ma Z et al (2016) Predictive value of PWI for blood supply and T1-spin echo MRI for consistency of pituitary adenoma. Neuroradiology 58(1):51–57. https://doi.org/10.1007/s00234-015-1591-8. (in eng)

    Article  CAS  PubMed  Google Scholar 

  42. Conficoni A et al (2020) Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study. Br J Radiol 93(1113):20200321. https://doi.org/10.1259/bjr.20200321. (in eng)

    Article  PubMed  PubMed Central  Google Scholar 

  43. Boxerman JL, Rogg JM, Donahue JE, Machan JT, Goldman MA, Doberstein CE (2010) Preoperative MRI evaluation of pituitary macroadenoma: imaging features predictive of successful transsphenoidal surgery. AJR Am J Roentgenol 195(3):720–728. https://doi.org/10.2214/AJR.09.4128

    Article  PubMed  Google Scholar 

  44. Sanei Taheri M et al (2019) Accuracy of diffusion-weighted imaging-magnetic resonance in differentiating functional from non-functional pituitary macro-adenoma and classification of tumor consistency. Neuroradiol J 32(2):74–85. https://doi.org/10.1177/1971400918809825

    Article  PubMed  Google Scholar 

  45. Antunes X et al (2018) Predictors of surgical outcome and early criteria of remission in acromegaly. Endocrine 60(3):415–422. https://doi.org/10.1007/s12020-018-1590-8. (in eng)

    Article  CAS  PubMed  Google Scholar 

  46. Ko CC, Chen TY, Lim SW, Kuo YT, Wu TC, Chen JH (2019) Prediction of recurrence in solid nonfunctioning pituitary macroadenomas: additional benefits of diffusion-weighted MR imaging. J Neurosurg 132(2):351–359. https://doi.org/10.3171/2018.10.JNS181783. (in eng)

    Article  PubMed  Google Scholar 

  47. Zeynalova A et al (2019) Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI. Neuroradiology 61(7):767–774. https://doi.org/10.1007/s00234-019-02211-2. (in eng)

    Article  PubMed  Google Scholar 

  48. Hughes JD, Fattahi N, Van Gompel J, Arani A, Ehman R, Huston J (2016) Magnetic resonance elastography detects tumoral consistency in pituitary macroadenomas. Pituitary 19(3):286–292. https://doi.org/10.1007/s11102-016-0706-5. (in eng)

    Article  PubMed  PubMed Central  Google Scholar 

  49. Hughes JD et al (2016) Adenoid cystic carcinoma metastatic to the pituitary: a case report and discussion of potential diagnostic value of magnetic resonance elastography in pituitary tumors. World Neurosurg 91:669.e11–4. https://doi.org/10.1016/j.wneu.2016.03.044. (in eng)

    Article  Google Scholar 

  50. Yao A et al (2020) Pituitary adenoma consistency: direct correlation of ultrahigh field 7T MRI with histopathological analysis. Eur J Radiol 126:108931. https://doi.org/10.1016/j.ejrad.2020.108931. (in eng)

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Radiology Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil

    Monique Alvares Barbosa & Nina Ventura

  2. MRI Unit, Clínica de Diagnóstico por Imagem, DASA, Rio de Janeiro, Brazil

    Monique Alvares Barbosa

  3. Neurosurgery Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil

    Eduardo Giardini Rodovalhe Pereira, Paulo José da Mata Pereira & André Accioly Guasti

  4. Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil

    Felipe Andreiuolo, Leila Chimelli & Monica R. Gadelha

  5. Neuroendocrinology Research Center/Endocrinology Division, Medical School and Hospital Universitário Clementino Fraga Filho, Rio de Janeiro, Brazil

    Leandro Kasuki & Monica R. Gadelha

  6. Neuroendocrine Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil

    Leandro Kasuki & Monica R. Gadelha

  7. Endocrinology Division, Hospital Federal de Bonsucesso, Rio de Janeiro, Brazil

    Leandro Kasuki

  8. Neuroradiology Division, Medical School and Hospital Universitário Clementino Fraga Filho, Rio de Janeiro, Brazil

    Nina Ventura

  9. Neuroradiology Unit, Samaritano Hospital, Grupo Fleury, Rio de Janeiro, Brazil

    Nina Ventura

  10. Serviço de Radiologia, Instituto Estadual do Cérebro Paulo Niemeyer, Rua do Rezende, 156, Centro, Rio de Janeiro, 20231-092, Brazil

    Monique Alvares Barbosa

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  1. Monique Alvares Barbosa
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  2. Eduardo Giardini Rodovalhe Pereira
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Contributions

MAB collected the MRI data, wrote the main manuscript text and prepared tables. NV prepared figures. LK performed statistical analysis. LK and MRG performed the clinical and laboratory evaluation of the patients. EGRP, AAG and PJMP operated on patients and recorded tumor consistency. LC and FA evaluated the histopathology data. All authors reviewed the manuscript.

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Correspondence to Monique Alvares Barbosa.

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Barbosa, M.A., Pereira, E.G.R., da Mata Pereira, P.J. et al. Diffusion-weighted imaging does not seem to be a predictor of consistency in pituitary adenomas. Pituitary 27, 187–196 (2024). https://doi.org/10.1007/s11102-023-01377-6

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