Skip to main content
SpringerLink
Log in

Morphometric Analysis of Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Digital Pathology

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

Abstract

Digital pathology uses digitized images for cancer research. We aimed to assess morphometric parameters using digital pathology for predicting recurrence in patients with papillary thyroid carcinoma (PTC) and lateral cervical lymph node (LN) metastasis. We analyzed 316 PTC patients and assessed the longest diameter and largest area of metastatic focus in LNs using a whole slide imaging scanner. In digital pathology assessment, the longest diameters and largest areas of metastatic foci in LNs were positively correlated with traditional optically measured diameters (R = 0.928 and R2 = 0.727, p < 0.001 and p < 0.001, respectively). The optimal cutoff diameter was 8.0 mm in both traditional microscopic (p = 0.009) and digital pathology (p = 0.016) evaluations, with significant differences in progression-free survival (PFS) observed at this cutoff (p = 0.006 and p = 0.002, respectively). The predictive area’s cutoff was 35.6 mm2 (p = 0.005), which significantly affected PFS (p = 0.015). Using an 8.0-mm cutoff in traditional microscopic evaluation and a 35.6-mm2 cutoff in digital pathology showed comparable predictive results using the proportion of variation explained (PVE) methods (2.6% vs. 2.4%). Excluding cases with predominant cystic changes in LNs, the largest metastatic areas by digital pathology had the highest PVE at 3.9%. Furthermore, high volume of LN metastasis (p = 0.001), extranodal extension (p = 0.047), and high ratio of metastatic LNs (p = 0.006) were associated with poor prognosis. Both traditional microscopic and digital pathology evaluations effectively measured the longest diameter of metastatic foci in LNs. Moreover, digital pathology offers limited advantages in predicting PFS of patients with lateral cervical LN metastasis of PTC, especially those without predominant cystic changes in LNs.

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

Similar content being viewed by others

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Baxi V, Edwards R, Montalto M, Saha S (2022) Digital pathology and artificial intelligence in translational medicine and clinical practice. Modern Pathology 35:23–32.https://doi.org/10.1038/s41379-021-00919-2

  2. Pell R, Oien K, Robinson M, et al. (2019) The use of digital pathology and image analysis in clinical trials. J Pathol Clin Res 5:81–90. https://doi.org/10.1002/cjp2.127

  3. Kumar N, Gupta R, Gupta S (2020) Whole Slide Imaging (WSI) in pathology: current perspectives and future directions. Journal of Digital Imaging 33:1034–1040. https://doi.org/10.1007/s10278-020-00351-z

  4. Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights into Imaging 9:611–629. https://doi.org/10.1007/s13244-018-0639-9

  5. Abele N, Tiemann K, Krech T, et al. (2023) Noninferiority of artificial intelligence-assisted analysis of Ki-67 and estrogen/progesterone receptor in breast cancer routine diagnostics. Mod Pathol 36:100033. https://doi.org/10.1016/j.modpat.2022.100033

  6. Vyas NS, Markow M, Prieto-Granada C, et al. (2016) Comparing whole slide digital images versus traditional glass slides in the detection of common microscopic features seen in dermatitis. Journal of Pathology Informatics 7:30. https://doi.org/10.4103/2153-3539.186909

  7. Mukhopadhyay S, Feldman MD, Abels E, et al. (2018) Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (Pivotal Study). Am J Surg Pathol 42:39–52. https://doi.org/10.1097/pas.0000000000000948

  8. Litjens G, Sánchez CI, Timofeeva N, et al. (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6:26286. https://doi.org/10.1038/srep26286

  9. Collins BT, Collins LE (2013) Assessment of malignancy for atypia of undetermined significance in thyroid fine-needle aspiration biopsy evaluated by whole-slide image analysis. Am J Clin Pathol 139:736–745. https://doi.org/10.1309/AJCPQU29GHXYSZRR

  10. Takamatsu M, Yamamoto N, Kawachi H, et al. (2019) Prediction of early colorectal cancer metastasis by machine learning using digital slide images. Comput Methods Programs Biomed 178:155–161. https://doi.org/10.1016/j.cmpb.2019.06.022

  11. Steiner DF, MacDonald R, Liu Y, et al. (2018) Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am J Surg Pathol 42:1636–1646. https://doi.org/10.1097/PAS.0000000000001151

  12. Lin S, Samsoondar JP, Bandari E, et al. (2023) Digital quantification of tumor cellularity as a novel prognostic feature of non-small cell lung carcinoma. Mod Pathol 36:100055. https://doi.org/10.1016/j.modpat.2022.100055

  13. Bouzin C, Saini ML, Khaing KK, et al. (2016) Digital pathology: elementary, rapid and reliable automated image analysis. Histopathology 68:888–896. https://doi.org/10.1111/his.12867

  14. Hsieh AM, Polyakova O, Fu G, et al. (2018) Programmed death-ligand 1 expression by digital image analysis advances thyroid cancer diagnosis among encapsulated follicular lesions. Oncotarget 9:19767–19782. https://doi.org/10.18632/oncotarget.24833

  15. J AAJ, V MAR (2017) Automatic classification of thyroid histopathology images using multi-classifier system. Multimedia Tools and Applications 76:18711–18730. https://doi.org/10.1007/s11042-017-4363-0

  16. Gopinath B, Shanthi N (2013) Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images. Australas Phys Eng Sci Med 36:219–230. https://doi.org/10.1007/s13246-013-0199-8

  17. Tsou P, Wu CJ (2019) Mapping driver mutations to histopathological subtypes in papillary thyroid carcinoma: applying a deep convolutional neural network. J Clin Med 8:1675. https://doi.org/10.3390/jcm8101675

  18. Eftimie LG, Glogojeanu RR, Tejaswee A, et al. (2022) Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features. Scientific Reports 12:21636. https://doi.org/10.1038/s41598-022-25788-w

  19. Kim M, Jeon MJ, Oh HS, et al. (2018) Prognostic implication of N1b classification in the eighth edition of the tumor-node-metastasis staging system of differentiated thyroid cancer. Thyroid 28:496–503. https://doi.org/10.1089/thy.2017.0473

  20. Contal C, O'Quigley J (1999) An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 30:253–270. https://doi.org/10.1016/S0167-9473(98)00096-6

  21. Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171. https://doi.org/10.1109/rbme.2009.2034865

  22. Arvaniti E, Fricker KS, Moret M, et al. (2018) Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep 8:12054. https://doi.org/10.1038/s41598-018-30535-1

  23. Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK, Mazurowski MA (2019) Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. Breast Cancer Res Treat 173:455–463. https://doi.org/10.1007/s10549-018-4990-9

  24. Lee H, Lee DE, Park S, et al. (2019) Predicting response to neoadjuvant chemotherapy in patients with breast cancer: combined statistical modeling using clinicopathological factors and FDG PET/CT texture parameters. Clin Nucl Med 44:21–29. https://doi.org/10.1097/rlu.0000000000002348

  25. Skrede OJ, De Raedt S, Kleppe A, et al. (2020) Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 395:350–360. https://doi.org/10.1016/s0140-6736(19)32998-8

  26. Kezlarian B, Lin O (2021) Artificial intelligence in thyroid fine needle aspiration biopsies. Acta Cytologica 65:324–329. https://doi.org/10.1159/000512097

  27. Aloqaily A, Polonia A, Campelos S, et al. (2021) Digital versus optical diagnosis of follicular patterned thyroid lesions. Head Neck Pathol 15:537–543. https://doi.org/10.1007/s12105-020-01243-y

  28. Girolami I, Marletta S, Pantanowitz L, et al. (2020) Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects. Cytopathology 31:432–444. https://doi.org/10.1111/cyt.12828

  29. Chain K, Legesse T, Heath JE, Staats PN (2019) Digital image-assisted quantitative nuclear analysis improves diagnostic accuracy of thyroid fine-needle aspiration cytology. Cancer Cytopathol 127:501–513. https://doi.org/10.1002/cncy.22120

  30. Haugen BR, Alexander EK, Bible KC, et al. (2016) 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 26:1–133. https://doi.org/10.1089/thy.2015.0020

  31. Deng Y, Zhu G, Ouyang W, et al. (2019) Size of the largest metastatic focus to the lymph node is associated with incomplete response of Pn1 papillary thyroid carcinoma. Endocrine Practice 25:887–898. https://doi.org/10.4158/EP-2018-0583

Download references

Acknowledgements

A part of this study was presented as an abstract at a spring meeting of the Korean Thyroid Association in 2023.

Funding

Dong Eun Song acknowledges support from the National Research Foundation of Korea Research Grant (2021R1F1A1045552) and a grant (2022IL0016) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea. The remaining authors have no funding to report.

Author information

Authors and Affiliations

  1. Division of Endocrinology and Metabolism, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Chae A Kim, Jungmin Yoo & Won Gu Kim

  2. Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Hyeong Rok An & Dong Eun Song

  3. Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Yu-Mi Lee & Tae-Yon Sung

Authors
  1. Chae A Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyeong Rok An
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jungmin Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu-Mi Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tae-Yon Sung
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Won Gu Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dong Eun Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Chae A Kim: data collection (equal), formal analysis (lead), and writing—original draft and revision (lead). Hyeong Rok An: formal analysis (equal) and visualization of results (lead). Jungmin Yoo: data collection (equal) and data curation (equal). Yu-Mi Lee and Tae-Yon Sung: data collection (equal), interpretation of the results (equal), and writing—editing (equal). Won Gu Kim: conceptualization (supporting), interpretation of the results (lead), writing—review and editing (lead), and revision (supporting). Dong Eun Song: conceptualization (lead), pathology review (lead), writing—review and editing (lead), and revision (supporting). All the authors had full access to the data, took responsibility for the accuracy of the data analysis, and approved the final version of the manuscript.

Corresponding authors

Correspondence to Won Gu Kim or Dong Eun Song.

Ethics declarations

Ethical Approval

This study was performed in line with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board of the Asan Medical Center (IRB no: 2022-0791).

Informed Consent

Informed consent was obtained from all participants and/or their legal guardians.

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.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 140 KB)

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

Kim, C.A., An, H.R., Yoo, J. et al. Morphometric Analysis of Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Digital Pathology. Endocr Pathol (2023). https://doi.org/10.1007/s12022-023-09790-0

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12022-023-09790-0

Keywords

Search

Navigation