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Quantitative kinetic parameters of primary tumor can be used to predict pelvic lymph node metastasis in early-stage cervical cancer

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Abstract

Purpose

To investigate the role of kinetic parameters of primary tumor derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer.

Methods

66 women with newly diagnosed cervical cancer were included between July 2017 and August 2019. All patients had a FIGO stage IB-IIA cancer and treated with hysterectomy and bilateral lymphadenectomy. Kinetic parameters of the primary tumor were derived from DCE-MRI data. The tumor diameter, ADC value, kinetic parameters, and nodal short-axis diameter were compared between patients with or without PLNM. Logistic regression analysis was used to determine the independent predictors for PLNM and receiver operator characteristic curve was used to evaluate the predictive performance.

Results

There were 20 patients with PLNM and 46 patients without PLNM. Tumor diameter, the efflux rate constant (Kep), and nodal short-axis diameter were significantly higher in patients with PLNM (P < 0.01). Multivariate logistic regression analysis showed that Kep and short-axis diameter were independent predictors for PLNM. Combining Kep and nodal short-axis diameter yielded the highest area under the curve (AUC) of 0.839. Combined with Kep, the sensitivity, specificity, negative predictive value, and positive predictive value of nodal short-axis diameter increased from 0.500, 0.957, 0.815, and 0.833 to 0.600, 0.978, 0.923, and 0.849, respectively. With 1.113 min−1 as threshold, the sensitivity and specificity values of Kep in predicting PLNM in patients with normal-sized lymph nodes were 0.909 and 0.667, respectively.

Conclusions

Kep of primary tumor can be used as a surrogate marker to predict PLNM in cervical cancer.

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Data availability

All relevant data are available from the corresponding author.

References

  1. National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology (NCCN Guidelines): cervical cancer. Version I.2019, 2019.

  2. Noguchi H, Shiozawa I, Sakai Y et al (1987) Pelvic lymph node metastasis of uterine cervical cancer. Gynecol Oncol 27:150–158.

  3. Bentivegna E, Gouy S, Maulard A et al (2016) Oncological outcomes after fertility-sparing surgery for cervical cancer: a systematic review. Lancet Oncol 17:240-253.

  4. Machida H, Iwata T, Okugawa K et al (2019) Fertility-sparing trachelectomy for early-stage cervical cancer: a proposal of an ideal candidate. Gynecol Oncol. pii: S0090-8258(19)31672-5.

  5. Musch M, Klevecka V, Roggenbuck U et al (2008) Complications of pelvic lymphadenectomy in 1,380 patients undergoing radical retropubic prostatectomy between 1993 and 2006. J Urol 179: 923–928.

  6. Cibula D, McCluggage WG (2019) Sentinel lymph node (SLN) concept in cervical cancer: current limitations and unanswered questions. Gynecol Oncol 152:202–207.

  7. Fader AN, Edwards RP, Cost Met et al (2008) Sentinel lymph node biopsy in early-stage cervical cancer: utility of intraoperative versus postoperative assessment. Gynecol Oncol 111:13–17.

  8. Pálsdóttir K, Fischerova D, Franchi D et al (2015) Preoperative prediction of lymph node metastasis and deep stromal invasion in women with invasive cervical cancer: prospective multicenter study using 2D and 3D ultrasound. Ultrasound Obstet Gynecol 45:470-475.

  9. Sironi S, Buda A, Picchio M et al (2006) Lymph node metastasis in patients with clinical early-stage cervical cancer: detection with integrated FDG PET/CT. Radiology 238:272–279.

  10. Kim SH, Kim SC, Choi BI et al (1994) Uterine cervical carcinoma: evaluation of pelvic lymph node metastasis with MR imaging. Radiology 190:807-811.

  11. Liyanage SH, Roberts CA, Rockall AG (2010) MRI and PET scans for primary staging and detection of cervical cancer recurrence. Womens Health (Lond) 6:251-267.

  12. Matsukuma K, Tsukamoto N, Matsuyama T et al (1989) Preoperative CT study of lymph nodes in cervical cancer: its correlation with histological findings. Gynecol Oncol 33:168–171.

  13. Eifel PJ, Berek JS, Thigpen JT (1997) Cancer of the cervix, vagina and vulva. In: deVita VTJ, Hellman S, Rosenberg SA, eds, Cancer: principles and practice of oncology, 5th ed., Pa: Lippincott-Raven, Philadelphia.

  14. Liu Y, Liu H, Bai X et al (2011) Differentiation of metastatic from non-metastatic lymph nodes in patients with uterine cervical cancer using diffusion-weighted imaging. Gynecol Oncol 122:19–24.

  15. Dhanda S, Thakur M, Kerkar R et al (2014) Diffusion weighted imaging of gynecologic tumors: diagnostic pearls and potential pitfalls. Radiographics 34:1393–1416.

  16. Song J, Hu Q, Huang J et al (2019) Combining tumor size and diffusion-weighted imaging to diagnose normal-sized metastatic pelvic lymph nodes in cervical cancers. Acta Radiol 60:388–395.

  17. McVeigh PZ, Syed AM, Milosevic M et al (2008) Diffusion-weighted MRI in cervical cancer. Eur Radiol 18:1058–1064.

  18. Rizzo S, Calareso G, Maccagnoni S et al (2014) Pre-operative MR evaluation of features that indicate the need of adjuvant therapies in early stage cervical cancer patients. a single-centre experience. Eur J Radiol 83:858–864.

  19. Nola Hylton (2006) Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker. J Clin Oncol 24:3293-3298.

  20. Fidler IJ, Ellis LM (1994) The implications of angiogenesis for the biology and therapy of cancer metastasis. Cell 79:85 –188.

  21. Sundfor K, Lyng H, Rofstad EK (1998) Tumour hypoxia and vascular density as predictors of metastasis in squamous cell carcinoma of the uterine cervix. Br J Cancer 78:822–827.

  22. Wu Q, Shi D, Dou S et al (2019) Radiomics analysis of multiparametric MRI evaluates the pathological features of cervical squamous cell carcinoma. J Magn Reson Imaging 49:1141-1148.

  23. Lakhman Y, Akin O, Park KJ (2013) Stage IB1 cervical cancer: role of preoperative MR imaging in selection of patients for fertility-sparing radical trachelectomy. Radiology 269:149-158.

  24. Ziliang Cheng, Zhuo Wu, Guangzi Shi et al (2018) Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging. Eur Radiol 28: 982-991.

  25. Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223-232.

  26. Shrout PE (1998) Measurement reliability and agreement in psychiatry. Stat Methods Med Res 7: 301-317.

  27. Fagotti A, Pedone Anchora L, Conte C et al (2016) Beyond sentinel node algorithm. toward a more tailored surgery for cervical cancer patients. Cancer Med 5:1725–1730.

  28. Macdonald OK, Chen J, Dodson M et al (2009) Prognostic significance of histology and positive lymph node involvement following radical hysterectomy in carcinoma of the cervix. Am J Clin Oncol 32:411–416.

  29. Chen XL, Chen GW, Xu GH et al (2018) Tumor size at magnetic resonance imaging association with lymph node metastasis and lymphovascular space invasion in resectable cervical cancer: a multicenter evaluation of surgical specimens. Int J Gynecol Cancer 28:1545-1552.

  30. Khalifa F, Soliman A, El-Baz A et al (2014) Models and methods for analyzing DCE-MRI: a review. Med Phys 41:124301.

  31. Postema S, Pattynama PM, van Rijswijk CS et al (1999) Cervical carcinoma: can dynamic contrast-enhanced MR imaging help predict tumor aggressiveness. Radiology 210:217–220.

  32. Jain RK (1987) Transport of molecules in the tumor interstitium: a review. Cancer Res 47:3039-3051.

  33. Carmeliet P, Jain RK (2011) Molecular mechanisms and clinical applications of angiogenesis. Nature 473:298-307.

  34. Øvrebø KM, Ellingsen C, Hompland T et al (2013) Dynamic contrast-enhanced magnetic resonance imaging of the metastatic potential of tumors: a preclinical study of cervical carcinoma and melanoma xenografts. Acta Oncol 52:604-611.

  35. Lopata RG, Backes WH, van den Bosch PP et al (2007) On the identifiability of pharmacokinetic parameters in dynamic contrast-enhanced imaging. Magn Reson Med 58:425-429.

  36. Michaely HJ, Morelli JN, Budjan J et al (2013) CAIPIRINHA-Dixon-TWIST (CDT)-volume-interpolated breath-hold examination (VIBE): a new technique for fast time-resolved dynamic 3-dimensional imaging of the abdomen with high spatial resolution. Invest Radiol 48:590-597.

  37. Rafal Panek, Maria A Schmidt, Marco Borri et al (2016) Time-resolved angiography with stochastic trajectories for dynamic contrast-enhanced MRI in head and neck cancer: are pharmacokinetic parameters affected? Med Phys 43:6024-6032.

  38. Luminita A Tudorica, Karen Y Oh, Nicole Roy et al (2012) A feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings. Magn Reson Imaging 30:1257-1267.

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Funding

This work was funded by the Project Supported by Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2017) and the Medical Research Foundation of Guangdong Province of China (Grant Nos. A2017248, A2019384).

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Author notes

  1. Zhiqiang Bai and Jie Shi contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China

    Zhiqiang Bai, Zehong Yang, Weike Zeng, Huijun Hu, Jinglian Zhong, Xiaohui Duan & Jun Shen

  2. Department of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China

    Jie Shi

  3. Department of MRI, Maoming People Hospital, No. 101 Weimin Road, Maoming, 525000, Guangdong, China

    Xinmin Wang

Authors
  1. Zhiqiang Bai
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  2. Jie Shi
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Contributions

Z.B.: Methodology, Writing—Original draft preparation. J.S.: Data curation, Investigation, and Visualization. Z.Y.: Formal analysis. W.Z., H.H.,J.Z.,: Resources. X.D.: Funding acquisition. X.W.: Project administration. J.S.: Conceptualization, Supervision, and Writing—Reviewing and Editing.

Corresponding authors

Correspondence to Xinmin Wang or Jun Shen.

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The authors declare that they have no conflicts of interest.

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The study was approved by the institutional review board of Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University.

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Written informed consent was obtained from all participants.

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Bai, Z., Shi, J., Yang, Z. et al. Quantitative kinetic parameters of primary tumor can be used to predict pelvic lymph node metastasis in early-stage cervical cancer. Abdom Radiol 46, 1129–1136 (2021). https://doi.org/10.1007/s00261-020-02762-6

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  • DOI: https://doi.org/10.1007/s00261-020-02762-6

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