Skip to main content

Advertisement

SpringerLink
Log in

Alkaline phosphatase combines with CT factors for differentiating small (≤ 4 cm) fat-poor angiomyolipoma from renal cell carcinoma: a multiple quantitative tool

  • Original Article
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

This study aimed to evaluate the diagnostic value of serum and CT factors to establish a convenient diagnostic method for differentiating small (≤ 4 cm) fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC).

Materials and methods

This study analyzed the preoperative serum laboratory data and CT data of 32 fat-poor AML patients and 133 RCC patients. The CT attenuation value of tumor (AVT), relative enhancement ratio (RER), and heterogeneous degree of tumor were detected using region of interest on precontrast phase (PCP) and the corticomedullary phase. Multivariate regression was performed to filter the main factors. The main factors were selected to establish the prediction models. The area under the curve (AUC) was measured to evaluate the diagnostic efficacy.

Results

Fat-poor AML was more common found in younger (47.91 ± 2.09 years vs 53.63 ± 1.17 years, P = 0.02) and female (70.68 vs 28.13%, P < 0.001) patients. Alkaline phosphatase (ALP) was higher in RCC patients (81.80 ± 1.75 vs 63.25 ± 2.95 U/L, P < 0.01). For CT factors, fat-poor AML was higher in PCP_AVT (40.30 ± 1.49 vs 32.98 ± 0.69Hu, P < 0.01) but lower in RER (67.17 ± 3.17 vs 84.64 ± 2.73, P < 0.01). Gender, ALP, PCP_AVT and RER was found valuable for the differentiation. When compared with laboratory-based or CT-based diagnostic models, the combination model integrating gender, ALP, PCP_AVT and RER shows the best diagnostic performance (AUC = 0.922).

Conclusion

ALP was found higher in RCC patients. Female patients with ALP < 70.50U/L, PCP_AVT > 35.97Hu and RER < 82.66 are more likely to be diagnose as fat-poor AML.

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

Similar content being viewed by others

Data availability

For data availability, please contact the corresponding author.

References

  1. Sanchez A, Feldman AS, Hakimi AA (2018) Current management of small renal masses, including patient selection, renal tumor biopsy, active surveillance, and thermal ablation. J Clin Oncol 36(36):3591–3600. https://doi.org/10.1200/JCO.2018.79.2341

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hakim SW, Schieda N, Hodgdon T, McInnes MD, Dilauro M, Flood TA (2016) Angiomyolipoma (AML) without visible fat: ultrasound, CT and MR imaging features with pathological correlation. Eur Radiol 26(2):592–600. https://doi.org/10.1007/s00330-015-3851-8

    Article  PubMed  Google Scholar 

  3. Carbonara U, Simone G, Minervini A et al (2021) Robotic-assisted partial nephrectomy for “very small” (<2 cm) renal mass: results of a multicenter contemporary cohort. Eur Urol Focus 7(5):1115–1120. https://doi.org/10.1016/j.euf.2020.10.001

    Article  PubMed  Google Scholar 

  4. Okhunov Z, Gorin MA, Jefferson FA et al (2021) Can preoperative renal mass biopsy change clinical practice and reduce surgical intervention for small renal masses? Urol Oncol 39(10):735 e17-735 e23. https://doi.org/10.1016/j.urolonc.2021.05.024

    Article  PubMed  Google Scholar 

  5. Su ZT, Patel HD, Huang MM et al (2022) Active surveillance versus immediate intervention for small renal masses: a cost-effectiveness and clinical decision analysis. J Urol 208(4):794–803. https://doi.org/10.1097/JU.0000000000002812

    Article  PubMed  Google Scholar 

  6. Wang X, Song G, Jiang H (2021) Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools. Cancer Imaging 21(1):47. https://doi.org/10.1186/s40644-021-00417-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kuusk T, Neves JB, Tran M, Bex A (2021) Radiomics to better characterize small renal masses. World J Urol 39(8):2861–2868. https://doi.org/10.1007/s00345-021-03602-y

    Article  PubMed  Google Scholar 

  8. Gong P, Liu Y, Gong Y et al (2021) The association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and lymphocyte to monocyte ratio with post-thrombolysis early neurological outcomes in patients with acute ischemic stroke. J Neuroinflammation 18(1):51. https://doi.org/10.1186/s12974-021-02090-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Razik A, Das CJ, Sharma S (2019) Angiomyolipoma of the kidneys: current perspectives and challenges in diagnostic imaging and image-guided therapy. Curr Probl Diagn Radiol 48(3):251–261. https://doi.org/10.1067/j.cpradiol.2018.03.006

    Article  PubMed  Google Scholar 

  10. Verma SK, Mitchell DG, Yang R et al (2010) Exophytic renal masses: angular interface with renal parenchyma for distinguishing benign from malignant lesions at MR imaging. Radiology 255(2):501–507. https://doi.org/10.1148/radiol.09091109

    Article  PubMed  Google Scholar 

  11. Tang Z, Yu D, Ni T, Zhao T, Jin Y, Dong E (2020) Quantitative analysis of multiphase contrast-enhanced CT Images: a pilot study of preoperative prediction of fat-poor angiomyolipoma and renal cell carcinoma. AJR Am J Roentgenol 214(2):370–382. https://doi.org/10.2214/AJR.19.21625

    Article  PubMed  Google Scholar 

  12. Li A, Xing W, Li H et al (2018) Subtype differentiation of small (</= 4 cm) solid renal mass using volumetric histogram analysis of DWI at 3-T MRI. AJR Am J Roentgenol 211(3):614–623. https://doi.org/10.2214/AJR.17.19278

    Article  PubMed  Google Scholar 

  13. Sagreiya H, Akhbardeh A, Li D et al (2019) Point shear wave elastography using machine learning to differentiate renal cell carcinoma and angiomyolipoma. Ultrasound Med Biol 45(8):1944–1954. https://doi.org/10.1016/j.ultrasmedbio.2019.04.009

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zaher DM, El-Gamal MI, Omar HA et al (2020) Recent advances with alkaline phosphatase isoenzymes and their inhibitors. Arch Pharm (Weinheim) 353(5):2000011. https://doi.org/10.1002/ardp.202000011

    Article  CAS  Google Scholar 

  15. Lou Z, Lin W, Zhao H et al (2021) Alkaline phosphatase downregulation promotes lung adenocarcinoma metastasis via the c-Myc/RhoA axis. Cancer Cell Int. https://doi.org/10.1186/s12935-021-01919-7

    Article  PubMed  PubMed Central  Google Scholar 

  16. Huang CW, Wu TH, Hsu HY et al (2022) Reappraisal of the role of alkaline phosphatase in hepatocellular carcinoma. J Pers Med. https://doi.org/10.3390/jpm12040518

    Article  PubMed  PubMed Central  Google Scholar 

  17. Rao SR, Snaith AE, Marino D et al (2017) Tumour-derived alkaline phosphatase regulates tumour growth, epithelial plasticity and disease-free survival in metastatic prostate cancer. Br J Cancer 116(2):227–236. https://doi.org/10.1038/bjc.2016.402

    Article  CAS  PubMed  Google Scholar 

  18. Yu J, Zheng Q, Ding X et al (2019) Systematic re-analysis strategy of serum indices identifies alkaline phosphatase as a potential predictive factor for cervical cancer. Oncol Lett 18(3):2356–2365. https://doi.org/10.3892/ol.2019.10527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Nam HK, Sharma M, Liu J, Hatch NE (2017) Tissue nonspecific alkaline phosphatase (TNAP) regulates cranial base growth and synchondrosis maturation. Front Physiol 8:161. https://doi.org/10.3389/fphys.2017.00161

    Article  PubMed  PubMed Central  Google Scholar 

  20. Choueiri TK, Kaelin WG Jr (2020) Targeting the HIF2-VEGF axis in renal cell carcinoma. Nat Med 26(10):1519–1530. https://doi.org/10.1038/s41591-020-1093-z

    Article  CAS  PubMed  Google Scholar 

  21. Liu Y, Lv H, Li X et al (2021) Cyclovirobuxine inhibits the progression of clear cell renal cell carcinoma by suppressing the IGFBP3-AKT/STAT3/MAPK-Snail signalling pathway. Int J Biol Sci 17(13):3522–3537. https://doi.org/10.7150/ijbs.62114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. van der Doelen MJ, Stockhaus A, Ma Y et al (2021) Early alkaline phosphatase dynamics as biomarker of survival in metastatic castration-resistant prostate cancer patients treated with radium-223. Eur J Nucl Med Mol Imaging 48(10):3325–3334. https://doi.org/10.1007/s00259-021-05283-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li SJ, Lv WY, Du H et al (2019) Albumin-to-alkaline phosphatase ratio as a novel prognostic indicator for patients undergoing minimally invasive lung cancer surgery: propensity score matching analysis using a prospective database. Int J Surg 69:32–42. https://doi.org/10.1016/j.ijsu.2019.07.008

    Article  PubMed  Google Scholar 

  24. Tan P, Xie N, Ai J et al (2018) The prognostic significance of albumin-to-alkaline phosphatase ratio in upper tract urothelial carcinoma. Sci Rep 8(1):12311. https://doi.org/10.1038/s41598-018-29833-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Brozovich A, Garmezy B, Pan T, Wang L, Farach-Carson MC, Satcher RL (2021) All bone metastases are not created equal: revisiting treatment resistance in renal cell carcinoma. J Bone Oncol 31:100399. https://doi.org/10.1016/j.jbo.2021.100399

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chen XY, Lan M, Zhou Y et al (2017) Risk factors for bone metastasis from renal cell cancer. J Bone Oncol 9:29–33. https://doi.org/10.1016/j.jbo.2017.10.004

    Article  PubMed  PubMed Central  Google Scholar 

  27. Pecoraro A, Palumbo C, Knipper S et al (2021) Synchronous metastasis rates in T1 renal cell carcinoma: a surveillance, epidemiology, and end results database-based study. Eur Urol Focus 7(4):818–826. https://doi.org/10.1016/j.euf.2020.02.011

    Article  PubMed  Google Scholar 

  28. Kang HS, Park JJ (2021) Circularity index on contrast-enhanced computed tomography helps distinguish fat-poor angiomyolipoma from renal cell carcinoma: retrospective analyses of histologically proven 257 small renal tumors less than 4 cm. Korean J Radiol 22(5):735–741. https://doi.org/10.3348/kjr.2020.0865

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all of the members of the team. We are grateful to the financial support of the Department of Finance of Guangdong Province and Guangdong Provincial People’s Hospital.

Funding

This work was supported by Special Funding of Department of Finance of Guangdong Province (No. KS012022267) and NSFC Incubation Project of Guangdong Provincial People’s Hospital (No.KY0120220029).

Author information

Authors and Affiliations

  1. Department of Urology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People’s Republic of China

    Tianming Peng, Junhong Fan, Qianqian Wang, Yuchun Chen, Yong Li, Kunlin Wu, Chunxiang Feng, Teng Li, Hanzhong Chen, Xiaoyong Pu & Jiumin Liu

  2. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China

    Tianming Peng, Xiaoyong Pu & Jiumin Liu

  3. Department of Medical Ultrasonics, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China

    Binyang Xie

Authors
  1. Tianming Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junhong Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Binyang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qianqian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuchun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kunlin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chunxiang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Teng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hanzhong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xiaoyong Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jiumin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JL: Protocol development, Manuscript editing; TP: Data collection, Data analysis, Manuscript writing; JF: Data analysis, Manuscript writing; BX: Data collection, Data analysis; QW: Data collection; YC: Data collection; YL: Data collection; KW: Data collection; CF: Data collection; TL: Data collection; HC: Data collection, Data analysis; XP: Data management, Manuscript editing.

Corresponding authors

Correspondence to Xiaoyong Pu or Jiumin Liu.

Ethics declarations

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval and consent to participate

The study has been approved by the Research Ethics Committee of Guangdong Provincial People’s Hospital (No. KY-Q-2022-122-01). As a retrospective study, the informed consent was waived by the committee.

Consent for publication

Not applicable.

Additional information

Publisher's Note

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

Supplementary Information

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

Peng, T., Fan, J., Xie, B. et al. Alkaline phosphatase combines with CT factors for differentiating small (≤ 4 cm) fat-poor angiomyolipoma from renal cell carcinoma: a multiple quantitative tool. World J Urol 41, 1345–1351 (2023). https://doi.org/10.1007/s00345-023-04367-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00345-023-04367-2

Keywords

Search

Navigation