Skip to main content

Advertisement

SpringerLink
Log in

Artificial Intelligence Detected the Relationship Between Nuclear Morphological Features and Molecular Abnormalities of Papillary Thyroid Carcinoma

  • Research
  • Published:
Endocrine Pathology Aims and scope Submit manuscript

Abstract

Papillary thyroid carcinoma (PTC) is the most common type of thyroid carcinoma and has characteristic nuclear features. Genetic abnormalities of PTC affect recent molecular target therapeutic strategy towards RET-altered cases, and they affect clinical prognosis and progression. However, there has been insufficient objective analysis of the correlation between genetic abnormalities and nuclear features. Using our newly developed methods, we studied the correlation between nuclear morphology and molecular abnormalities of PTC with the aim of predicting genetic abnormalities of PTC. We studied 72 cases of PTC and performed genetic analysis to detect BRAF p.V600E mutation and RET fusions. Nuclear features of PTC, such as nuclear grooves, pseudo-nuclear inclusions, and glassy nuclei, were also automatically detected by deep learning models. After analyzing the correlation between genetic abnormalities and nuclear features of PTC, logistic regression models could be used to predict gene abnormalities. Nuclear features were accurately detected with over 0.90 of AUCs in every class. The ratio of glassy nuclei to nuclear groove and the ratio of pseudo-nuclear inclusion to glassy nuclei were significantly higher in cases that were positive for RET fusions (p = 0.027, p = 0.043, respectively) than in cases that were negative for RET fusions. RET fusions were significantly predicted by glassy nuclei/nuclear grooves, pseudo-nuclear inclusions/glassy nuclei, and age (p = 0.023). Our deep learning models could accurately detect nuclear features. Genetic abnormalities had a correlation with nuclear features of PTC. Furthermore, our artificial intelligence model could significantly predict RET fusions of classic PTC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The datasets generated and analyzed for this study are available from the corresponding author upon reasonable request.

References

  1. Lloyd RV, Osamura RY, Klöppel G, Rosai J: WHO Classification of Tumours of Endocrine Organs: International Agency for Research on Cancer, 2017.

  2. Baloch ZW, Asa SL, Barletta JA, Ghossein RA, Juhlin CC, Jung CK, LiVolsi VA, Papotti MG, Sobrinho-Simões M, Tallini G, Mete O (2022) Overview of the 2022 WHO Classification of Thyroid Neoplasms. Endocr Pathol 33(1):27-63. https://doi.org/10.1007/s12022-022-09707-3

  3. Niu D, Murata S, Kondo T, Nakazawa T, Kawasaki T, Ma D, Yamane T, Nakamura N, Katoh R (2009) Involvement of centrosomes in nuclear irregularity of thyroid carcinoma cells. Virchows Arch 455(2):149-57. https://doi.org/10.1007/s00428-009-0802-2

  4. Murata S, Nakazawa T, Ohno N, Terada N, Iwashina M, Mochizuki K, Kondo T, Nakamura N, Yamane T, Iwasa S, Ohno S, Katoh R (2007) Conservation and alteration of chromosome territory arrangements in thyroid carcinoma cell nuclei. Thyroid 17(6):489-96. https://doi.org/10.1089/thy.2006.0328

  5. Lewiński A, Adamczewski Z, Zygmunt A, Markuszewski L, Karbownik-Lewińska M, Stasiak M (2019) Correlations between Molecular Landscape and Sonographic Image of Different Variants of Papillary Thyroid Carcinoma. J Clin Med 8(11):1916. https://doi.org/10.3390/jcm8111916

  6. Adeniran AJ, Zhu Z, Gandhi M, Steward DL, Fidler JP, Giordano TJ, Biddinger PW, Nikiforov YE (2006) Correlation between genetic alterations and microscopic features, clinical manifestations, and prognostic characteristics of thyroid papillary carcinomas. Am J Surg Pathol 30(2):216-22. https://doi.org/10.1097/01.pas.0000176432.73455.1b

  7. Tsou P, Wu CJ (2019) Mapping Driver Mutations to Histopathological Subtypes in Papillary Thyroid Carcinoma: Applying a Deep Convolutional Neural Network. J Clin Med 14;8(10):1675. https://doi.org/10.3390/jcm8101675

  8. Anand D, Yashashwi K, Kumar N, Rane S, Gann PH, Sethi A (2021) Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy. J Pathol 255(3):232-242. https://doi.org/10.1002/path.5773

  9. Kim JK, Seong CY, Bae IE, Yi JW, Yu HW, Kim SJ, Won JK, Chai YJ, Choi JY, Lee KE (2018) Comparison of Immunohistochemistry and Direct Sequencing Methods for Identification of the BRAFV600E Mutation in Papillary Thyroid Carcinoma. Ann Surg Oncol 25(6):1775-1781. https://doi.org/10.1245/s10434-018-6460-3

  10. Soares P, Trovisco V, Rocha AS, Lima J, Castro P, Preto A, Máximo V, Botelho T, Seruca R, Sobrinho-Simões M (2003) BRAF mutations and RET/PTC rearrangements are alternative events in the etiopathogenesis of PTC. Oncogene 17;22(29):4578–80. https://doi.org/10.1038/sj.onc.1206706

  11. Caicedo JC, Goodman A, Karhohs KW, Cimini BA, Ackerman J, Haghighi M, Heng C, Becker T, Doan M, McQuin C, Rohban M, Singh S, Carpenter AE (2019) Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat Methods 16(12):1247-1253. https://doi.org/10.1038/s41592-019-0612-7

  12. Tan M, Le QV (2019) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ed.^, eds. 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), 2019; 10691–10700. https://doi.org/10.48550/arXiv.1905.11946

  13. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. ed.^, eds. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. Springer, 2015; 234–241. https://doi.org/10.48550/arXiv.1505.04597

  14. Vaickus LJ, Suriawinata AA, Wei JW, Liu X (2019) Automating the Paris System for urine cytopathology-A hybrid deep-learning and morphometric approach. Cancer Cytopathol 127(2):98-115. https://doi.org/10.1002/cncy.22099

  15. Wang H, Wang Z, Du Met al. (2020) Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks. ed.^, eds. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society 111–119. https://doi.org/10.48550/arXiv.1910.01279

  16. Jung CK, Bychkov A, Kakudo K (2022) Update from the 2022 World Health Organization Classification of Thyroid Tumors: A Standardized Diagnostic Approach. Endocrinol Metab (Seoul) 37(5):703-718. https://doi.org/10.3803/EnM.2022.1553

  17. Pizzimenti C, Fiorentino V, Ieni A, Martini M, Tuccari G, Lentini M, Fadda G (2022) Aggressive variants of follicular cell-derived thyroid carcinoma: an overview. Endocrine78(1):1-12. https://doi.org/10.1007/s12020-022-03146-0

  18. Murata SI, Matsuzaki I, Kishimoto M, Katsuki N, Onishi T, Hirokawa M, Kojima F (2023) Papillary thyroid carcinoma with aggressive fused follicular and solid growth pattern: A unique histological subtype with high-grade malignancy? Pathol Int 73(5):207-211. https://doi.org/10.1111/pin.13323

  19. Jeon MJ, Chun SM, Kim D, Kwon H, Jang EK, Kim TY, Kim WB, Shong YK, Jang SJ, Song DE, Kim WG (2016) Genomic Alterations of Anaplastic Thyroid Carcinoma Detected by Targeted Massive Parallel Sequencing in a BRAF(V600E) Mutation-Prevalent Area. Thyroid 26(5):683-90. https://doi.org/10.1089/thy.2015.0506

  20. Xing M, Alzahrani AS, Carson KA, Shong YK, Kim TY, Viola D, Elisei R, Bendlová B, Yip L, Mian C, Vianello F, Tuttle RM, Robenshtok E, Fagin JA, Puxeddu E, Fugazzola L, Czarniecka A, Jarzab B, O'Neill CJ, Sywak MS, Lam AK, Riesco-Eizaguirre G, Santisteban P, Nakayama H, Clifton-Bligh R, Tallini G, Holt EH, Sýkorová V (2015) Association between BRAF V600E mutation and recurrence of papillary thyroid cancer. J Clin Oncol 33(1):42-50. https://doi.org/10.1200/JCO.2014.56.8253

  21. Wirth LJ, Sherman E, Robinson B, Solomon B, Kang H, Lorch J, Worden F, Brose M, Patel J, Leboulleux S, Godbert Y, Barlesi F, Morris JC, Owonikoko TK, Tan DSW, Gautschi O, Weiss J, de la Fouchardière C, Burkard ME, Laskin J, Taylor MH, Kroiss M, Medioni J, Goldman JW, Bauer TM, Levy B, Zhu VW, Lakhani N, Moreno V, Ebata K, Nguyen M, Heirich D, Zhu EY, Huang X, Yang L, Kherani J, Rothenberg SM, Drilon A, Subbiah V, Shah MH, Cabanillas ME (2020) Efficacy of Selpercatinib in RET-Altered Thyroid Cancers. N Engl J Med 27;383(9):825–835. https://doi.org/10.1056/NEJMoa2005651

  22. Nishikawa T, Iwamoto R, Matsuzaki I, Musangile FY, Takahashi A, Mikasa Y, Takahashi Y, Kojima F, Murata SI (2022) Pathologic Image Classification of Flat Urothelial Lesions Using Pathologic Criteria-Based Deep Learning. Am J Clin Pathol 158(6):759-769. https://doi.org/10.1093/ajcp/aqac117

  23. Murata SI, Kuroda M, Kawamura N, Warigaya K, Musangile FY, Matsuzaki I, Kojima F (2021) Microtubule-organizing center-mediated structural atypia in low- and high-grade urothelial carcinoma. Virchows Arch 478(2):327-334. https://doi.org/10.1007/s00428-020-02895-5

  24. Schwertheim S, Theurer S, Jastrow H, Herold T, Ting S, Westerwick D, Bertram S, Schaefer CM, Kälsch J, Baba HA, Schmid KW (2019) New insights into intranuclear inclusions in thyroid carcinoma: Association with autophagy and with BRAFV600E mutation. PLoS One 16;14(12):e0226199. https://doi.org/10.1371/journal.pone.0226199

  25. Lloyd RV, Erickson LA, Casey MB, Lam KY, Lohse CM, Asa SL, Chan JK, DeLellis RA, Harach HR, Kakudo K, LiVolsi VA, Rosai J, Sebo TJ, Sobrinho-Simoes M, Wenig BM, Lae ME (2004) Observer variation in the diagnosis of follicular variant of papillary thyroid carcinoma. Am J Surg Pathol 28(10):1336-40. https://doi.org/10.1097/01.pas.0000135519.34847.f6

  26. Saxén E, Franssila K, Bjarnason O, Normann T, Ringertz N (1978) Observer variation in histologic classification of thyroid cancer. Acta Pathol Microbiol Scand A. 86A(6):483-6. https://doi.org/10.1111/j.1699-0463.1978.tb02073.x

  27. Elisei R, Romei C, Vorontsova T, Cosci B, Veremeychik V, Kuchinskaya E, Basolo F, Demidchik EP, Miccoli P, Pinchera A, Pacini F (2001) RET/PTC rearrangements in thyroid nodules: studies in irradiated and not irradiated, malignant and benign thyroid lesions in children and adults. J Clin Endocrinol Metab 86(7):3211-6. https://doi.org/10.1210/jcem.86.7.7678

Download references

Acknowledgements

We acknowledge proofreading and editing by Benjamin Phillis at the Clinical Study Support Center at Wakayama Medical University.

Funding

This study was partly supported by Grant-in-Aid for Scientific Research C (19K07466), supported by the Japan Society for the Promotion of Science.

Author information

Authors and Affiliations

  1. Department of Human Pathology, Wakayama Medical University, 811-1 Kimiidera, Wakayama Prefecture, Wakayama 641-8509, Japan

    Toui Nishikawa, Ibu Matsuzaki, Ayata Takahashi, Iwamoto Ryuta, Fidele Yambayamba Musangile, Kanako Sagan, Mizuki Nishikawa, Yurina Mikasa, Yuichi Takahashi, Fumiyoshi Kojima & Shin-ichi Murata

Authors
  1. Toui Nishikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ibu Matsuzaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ayata Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Iwamoto Ryuta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fidele Yambayamba Musangile
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kanako Sagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mizuki Nishikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yurina Mikasa
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yuichi Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Fumiyoshi Kojima
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Shin-ichi Murata
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.N. made substantial contributions to the design and interpretation of the report. I.M. and A.T. significantly contributed to genetic analysis. R.I., F.Y.M., K.S., M.N., Y.M., and Y.T. contributed to pathological analysis. F.K. and S.M. significantly contributed to the validation of the manuscript.

Corresponding author

Correspondence to Shin-ichi Murata.

Ethics declarations

Ethics Approval and Consent to Participate

The Wakayama Medical University Institutional Review Board approved this study (No.3212).

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nishikawa, T., Matsuzaki, I., Takahashi, A. et al. Artificial Intelligence Detected the Relationship Between Nuclear Morphological Features and Molecular Abnormalities of Papillary Thyroid Carcinoma. Endocr Pathol 35, 40–50 (2024). https://doi.org/10.1007/s12022-023-09796-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12022-023-09796-8

Keywords

Search

Navigation