Skip to main content

Advertisement

SpringerLink
Log in

Deep learning approach for differentiating indeterminate adrenal masses using CT imaging

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Distinguishing stage 1–2 adrenocortical carcinoma (ACC) and large, lipid poor adrenal adenoma (LPAA) via imaging is challenging due to overlapping imaging characteristics. This study investigated the ability of deep learning to distinguish ACC and LPAA on single time-point CT images.

Methods

Retrospective cohort study from 1994 to 2022. Imaging studies of patients with adrenal masses who had available adequate CT studies and histology as the reference standard by method of adrenal biopsy and/or adrenalectomy were included as well as four patients with LPAA determined by stability or regression on follow-up imaging. Forty-eight (48) subjects with pathology-proven, stage 1–2 ACC and 43 subjects with adrenal adenoma >3 cm in size demonstrating a mean non-contrast CT attenuation > 20 Hounsfield Units centrally were included. We used annotated single time-point contrast-enhanced CT images of these adrenal masses as input to a 3D Densenet121 model for classifying as ACC or LPAA with five-fold cross-validation. For each fold, two checkpoints were reported, highest accuracy with highest sensitivity (accuracy focused) and highest sensitivity with the highest accuracy (sensitivity focused).

Results

We trained a deep learning model (3D Densenet121) to predict ACC versus LPAA. The sensitivity-focused model achieved mean accuracy: 87.2% and mean sensitivity: 100%. The accuracy-focused model achieved mean accuracy: 91% and mean sensitivity: 96%.

Conclusion

Deep learning demonstrates promising results distinguishing between ACC and large LPAA using single time-point CT images. Before being widely adopted in clinical practice, multicentric and external validation are needed.

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

Similar content being viewed by others

References

  1. Sherlock, M., Scarsbrook, A., Abbas, A., Fraser, S., Limumpornpetch, P., Dineen, R., & Stewart, P. M. (2020). Adrenal incidentaloma. Endocrine Reviews, 41(6), 775-820.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Reimondo, G., Castellano, E., Grosso, M., Priotto, R., Puglisi, S., Pia, A., ... & Terzolo, M. (2020). Adrenal incidentalomas are tied to increased risk of diabetes: findings from a prospective study. The Journal of Clinical Endocrinology & Metabolism, 105(4), e973-e981.

    Article  Google Scholar 

  3. Bovio, S., Cataldi, A., Reimondo, G., Sperone, P., Novello, S., Berruti, A., ... & Terzolo, M. (2006). Prevalence of adrenal incidentaloma in a contemporary computerized tomography series. Journal of endocrinological investigation, 29, 298-302.

    Article  CAS  PubMed  Google Scholar 

  4. Boland, G. W., Lee, M., Gazelle, G. S., Halpern, E. F., McNicholas, M. M., & Mueller, P. R. (1998). Characterization of adrenal masses using unenhanced CT: an analysis of the CT literature. AJR. American journal of roentgenology, 171(1), 201-204.

    Article  CAS  PubMed  Google Scholar 

  5. Boland, G. W., Blake, M. A., Hahn, P. F., & Mayo-Smith, W. W. (2008). Incidental adrenal lesions: principles, techniques, and algorithms for imaging characterization. Radiology, 249(3), 756-775.

    Article  PubMed  Google Scholar 

  6. Seo, J. M., Park, B. K., Park, S. Y., & Kim, C. K. (2014). Characterization of lipid-poor adrenal adenoma: chemical-shift MRI and washout CT. American Journal of Roentgenology, 202(5), 1043-1050.

    Article  PubMed  Google Scholar 

  7. Bancos, I., Taylor, A. E., Chortis, V., Sitch, A. J., Lang, K., Prete, A., ... & Arlt, W. (2020). Urine metabolomic phenotyping for detection of adrenocortical carcinoma: still a long way to go–Authors' reply. The Lancet Diabetes & Endocrinology, 8(11), 877-878.

    Article  Google Scholar 

  8. Fishman, E. K., Deutch, B. M., Hartman, D. S., Goldman, S. M., Zerhouni, E. A., & Siegelman, S. S. (1987). Primary adrenocortical carcinoma: CT evaluation with clinical correlation. American Journal of Roentgenology, 148(3), 531-535.

    Article  CAS  PubMed  Google Scholar 

  9. Bharwani, N., Rockall, A. G., Sahdev, A., Gueorguiev, M., Drake, W., Grossman, A. B., & Reznek, R. H. (2011). Adrenocortical carcinoma: the range of appearances on CT and MRI. American journal of roentgenology, 196(6), W706-W714.

    Article  PubMed  Google Scholar 

  10. Vanbrabant, T., Fassnacht, M., Assie, G., & Dekkers, O. M. (2018). Influence of hormonal functional status on survival in adrenocortical carcinoma: systematic review and meta-analysis. European journal of endocrinology, 179(6), 429-436.

    Article  CAS  PubMed  Google Scholar 

  11. Nader, S., Hickey, R. C., Sellin, R. V., & Samaan, N. A. (1983). Adrenal cortical carcinoma a study of 77 cases. Cancer, 52(4), 707-711.

    Article  CAS  PubMed  Google Scholar 

  12. Newhouse, J. H., Heffess, C. S., Wagner, B. J., Imray, T. J., Adair, C. F., & Davidson, A. J. (1999). Large degenerated adrenal adenomas: radiologic-pathologic correlation. Radiology, 210(2), 385-391.

    Article  CAS  PubMed  Google Scholar 

  13. Fassnacht, M., Arlt, W., Bancos, I., Dralle, H., Newell-Price, J., Sahdev, A., ... & Dekkers, O. M. (2016). Management of adrenal incidentalomas: European society of endocrinology clinical practice guideline in collaboration with the European network for the study of adrenal tumors. European journal of endocrinology, 175(2), G1-G34.

    Article  CAS  PubMed  Google Scholar 

  14. Lau, S. K., & Weiss, L. M. (2009). The Weiss system for evaluating adrenocortical neoplasms: 25 years later. Human pathology, 40(6), 757-768.

    Article  PubMed  Google Scholar 

  15. Erickson, B. J., Korfiatis, P., Kline, T. L., Akkus, Z., Philbrick, K., & Weston, A. D. (2018). Deep learning in radiology: does one size fit all?. Journal of the American College of Radiology, 15(3), 521-526.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Indolia, S., Goswami, A. K., Mishra, S. P., & Asopa, P. (2018). Conceptual understanding of convolutional neural network-a deep learning approach. Procedia computer science, 132, 679-688.

    Article  Google Scholar 

  17. Islam, J., & Zhang, Y. (2019). Understanding 3D CNN behavior for Alzheimer's disease diagnosis from brain PET scan. arXiv preprint arXiv:1912.04563.

    Article  Google Scholar 

  18. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).

  19. Rouzrokh, P., Khosravi, B., Faghani, S., Moassefi, M., Vera Garcia, D. V., Singh, Y., ... & Erickson, B. J. (2022). Mitigating bias in radiology machine learning: 1. Data handling. Radiology: Artificial Intelligence, 4(5), e210290.

    PubMed  PubMed Central  Google Scholar 

  20. Moassefi, M., Faghani, S., Conte, G. M., Kowalchuk, R. O., Vahdati, S., Crompton, D. J., ... & Erickson, B. J. (2022). A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients. Journal of neuro-oncology, 159(2), 447-455.

    Article  CAS  PubMed  Google Scholar 

  21. The MONAI Consortium (2020) Project MONAI. https://zenodo.org/record/4323059

  22. Torresan, F., Crimì, F., Ceccato, F., Zavan, F., Barbot, M., Lacognata, C., ... & Iacobone, M. (2021). Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma. BJS open, 5(1), zraa061.

    Article  Google Scholar 

  23. Elmohr, M. M., Fuentes, D., Habra, M. A., Bhosale, P. R., Qayyum, A. A., Gates, E., ... & Elsayes, K. M. (2019). Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT. Clinical radiology, 74(10), 818-e1.

    Article  Google Scholar 

  24. Bancos, I., & Prete, A. (2021). Approach to the patient with adrenal incidentaloma. The Journal of Clinical Endocrinology & Metabolism, 106(11), 3331-3353.

    Article  Google Scholar 

  25. Dinnes, J., Bancos, I., Ferrante di Ruffano, L., Chortis, V., Davenport, C., Bayliss, S., ... & Arlt, W. (2016). Management of endocrine disease: imaging for the diagnosis of malignancy in incidentally discovered adrenal masses: a systematic review and meta-analysis. European journal of endocrinology, 175(2), R51-R64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bancos, I., Taylor, A. E., Chortis, V., Sitch, A. J., Jenkinson, C., Davidge-Pitts, C. J., ... & Young Jr, W. F. (2020). Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas in the EURINE-ACT study: a prospective test validation study. The lancet Diabetes & endocrinology, 8(9), 773-781.

    Article  CAS  Google Scholar 

  27. Espinasse, M., Pitre-Champagnat, S., Charmettant, B., Bidault, F., Volk, A., Balleyguier, C., ... & Caramella, C. (2020). CT texture analysis challenges: influence of acquisition and reconstruction parameters: a comprehensive review. Diagnostics, 10(5), 258.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors are thankful to Mayo AI lab, Radiology, Mayo clinic, Rochester, MN, USA.

Funding

None

Author information

Authors and Affiliations

  1. Department of Radiology, Mayo Clinic, Rochester, MN, USA

    Yashbir Singh, Zachary S. Kelm, Shahriar Faghani & Bradley J. Erickson

  2. Division of Endocrinology, Metabolism and Nutrition, Mayo Clinic, Rochester, MN, USA

    Dana Erickson & Irina Bancos

  3. Department of General Surgery, Mayo Clinic, La Crosse, WI, USA

    Tal Yalon

Authors
  1. Yashbir Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zachary S. Kelm
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahriar Faghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dana Erickson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tal Yalon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Irina Bancos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bradley J. Erickson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bradley J. Erickson.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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 (DOCX 16 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

Singh, Y., Kelm, Z.S., Faghani, S. et al. Deep learning approach for differentiating indeterminate adrenal masses using CT imaging. Abdom Radiol 48, 3189–3194 (2023). https://doi.org/10.1007/s00261-023-03988-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-023-03988-w

Keywords

Search

Navigation