Advertisement

Apparent Diffusion Coefficient Histogram Analysis for Assessing Tumor Staging and Detection of Lymph Node Metastasis in Epithelial Ovarian Cancer: Correlation with p53 and Ki-67 Expression

  • Feng Wang
  • Yuxiang Wang
  • Yan Zhou
  • Congrong Liu
  • Dong Liang
  • Lizhi Xie
  • Zhihang Yao
  • Jianyu LiuEmail author
Research Article

Abstract

Purpose

To investigate the potential of apparent diffusion coefficient (ADC) histogram parameters in epithelial ovarian cancer (EOC) for distinguishing different tumor stages and determining lymph node status and correlations between ADC values and p53 and Ki-67 expression.

Procedures

Forty-nine EOC patients underwent preoperative magnetic resonance imaging. Staging and lymph node status were determined postoperatively. ADC values were measured using histogram analysis and compared between groups. Relationships between ADCs and Ki-67 and p53 expression were explored.

Results

DC parameters differed significantly between stage I vs II, I vs III, and I vs IV. The parameters were significantly lower in the lymph node-positive group than in the lymph node-negative group, were significantly negatively correlated with Ki-67 labeling index, and were all significantly lower in the mutation-type p53 group than in the wild-type p53 group.

Conclusions

ADC histogram analysis can help discriminate stage I from advanced-stage EOC and predict lymph node metastasis. ADC parameters were correlated with Ki-67 labeling index; the parameters may help indicate p53 expression.

Key words

Lymph nodes Magnetic resonance imaging Neoplasm metastasis Neoplasm staging Ovarian cancer 

Notes

Funding

This study has received funding from the Capital Characteristic Clinic Project of China (Z131107002213049) and the Beijing Municipal Natural Science Foundation (7162102).

Compliance with Ethical Standards

Ethical Approval

All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Ramalingam P (2016) Morphologic, immunophenotypic, and molecular features of epithelial ovarian cancer. Oncology (Williston Park) 30:166–176Google Scholar
  2. 2.
    Booth SJ, Turnbull LW, Poole DR, Richmond I (2008) The accurate staging of ovarian cancer using 3T magnetic resonance imaging—a realistic option. BJOG 115:894–901CrossRefGoogle Scholar
  3. 3.
    Pu T, Xiong L, Liu Q, Zhang M, Cai Q, Liu H, Sood AK, Li G, Kang Y, Xu C (2017) Delineation of retroperitoneal metastatic lymph nodes in ovarian cancer with near-infrared fluorescence imaging. Oncol Lett 14:2869–2877CrossRefGoogle Scholar
  4. 4.
    Gomez-Hidalgo NR, Martinez-Cannon BA, Nick AM et al (2015) Predictors of optimal cytoreduction in patients with newly diagnosed advanced-stage epithelial ovarian cancer: time to incorporate laparoscopic assessment into the standard of care. Gynecol Oncol 137:553–558CrossRefGoogle Scholar
  5. 5.
    Satoh T, Hatae M, Watanabe Y, Yaegashi N, Ishiko O, Kodama S, Yamaguchi S, Ochiai K, Takano M, Yokota H, Kawakami Y, Nishimura S, Ogishima D, Nakagawa S, Kobayashi H, Shiozawa T, Nakanishi T, Kamura T, Konishi I, Yoshikawa H (2010) Outcomes of fertility-sparing surgery for stage I epithelial ovarian cancer: a proposal for patient selection. J Clin Oncol 28:1727–1732CrossRefGoogle Scholar
  6. 6.
    Yuan Y, Gu ZX, Tao XF, Liu SY (2012) Computer tomography, magnetic resonance imaging, and positron emission tomography or positron emission tomography/computer tomography for detection of metastatic lymph nodes in patients with ovarian cancer: a meta-analysis. Eur J Radiol 81:1002–1006CrossRefGoogle Scholar
  7. 7.
    Michielsen K, Vergote I, Op de Beeck K et al (2014) Whole-body MRI with diffusion-weighted sequence for staging of patients with suspected ovarian cancer: a clinical feasibility study in comparison to CT and FDG-PET/CT. Eur Radiol 24:889–901CrossRefGoogle Scholar
  8. 8.
    Liu S, Zhang Y, Chen L, Guan W, Guan Y, Ge Y, He J, Zhou Z (2017) Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers. BMC Cancer 17:665CrossRefGoogle Scholar
  9. 9.
    Schob S, Meyer HJ, Pazaitis N, Schramm D, Bremicker K, Exner M, Höhn AK, Garnov N, Surov A (2017) ADC histogram analysis of cervical cancer aids detecting lymphatic metastases—a preliminary study. Mol Imaging Biol 19:953–962CrossRefGoogle Scholar
  10. 10.
    De Robertis R, Maris B, Cardobi N et al (2018) Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol 28:2582–2591.  https://doi.org/10.1007/s00330-017-5236-7 CrossRefPubMedGoogle Scholar
  11. 11.
    Ayhan A, Gultekin M, Taskiran C, Celik NY, Usubutun A, Kucukali T, Yuce K (2005) Lymphatic metastasis in epithelial ovarian carcinoma with respect to clinicopathological variables. Gynecol Oncol 97:400–404CrossRefGoogle Scholar
  12. 12.
    Xu YY, Huang BJ, Sun Z, Lu C, Liu YP (2007) Risk factors for lymph node metastasis and evaluation of reasonable surgery for early gastric cancer. World J Gastroenterol 13:5133–5138CrossRefGoogle Scholar
  13. 13.
    Barral M, Taouli B, Guiu B, Koh DM, Luciani A, Manfredi R, Vilgrain V, Hoeffel C, Kanematsu M, Soyer P (2015) Diffusion-weighted MR imaging of the pancreas: current status and recommendations. Radiology 274:45–63CrossRefGoogle Scholar
  14. 14.
    Rockall AG (2014) Diffusion weighted MRI in ovarian cancer. Curr Opin Oncol 26:529–535CrossRefGoogle Scholar
  15. 15.
    Kang Y, Choi SH, Kim YJ, Kim KG, Sohn CH, Kim JH, Yun TJ, Chang KH (2011) Gliomas: histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging—correlation with tumor grade. Radiology 261:882–890CrossRefGoogle Scholar
  16. 16.
    Zhang YD, Wang Q, Wu CJ, Wang XN, Zhang J, Liu H, Liu XS, Shi HB (2015) The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the Gleason grade of prostate cancer. Eur Radiol 25:994–1004CrossRefGoogle Scholar
  17. 17.
    Li HM, Qiang JW, Xia GL, Zhao SH, Ma FH, Cai SQ, Feng F, Fu AY (2015) MRI for differentiating ovarian endometrioid adenocarcinoma from high-grade serous adenocarcinoma. J Ovarian Res 8:26CrossRefGoogle Scholar
  18. 18.
    Oh JW, Rha SE, Oh SN, Park MY, Byun JY, Lee A (2015) Diffusion-weighted MRI of epithelial ovarian cancers: correlation of apparent diffusion coefficient values with histologic grade and surgical stage. Eur J Radiol 84:590–595CrossRefGoogle Scholar
  19. 19.
    Wang F, Wang Y, Zhou Y, Liu C, Xie L, Zhou Z, Liang D, Shen Y, Yao Z, Liu J (2017) Comparison between types I and II epithelial ovarian cancer using histogram analysis of monoexponential, biexponential, and stretched-exponential diffusion models. J Magn Reson Imaging 46:1797–1809CrossRefGoogle Scholar
  20. 20.
    Kyriazi S, Collins DJ, Messiou C, Pennert K, Davidson RL, Giles SL, Kaye SB, deSouza NM (2011) Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging—value of histogram analysis of apparent diffusion coefficients. Radiology 261:182–192CrossRefGoogle Scholar
  21. 21.
    Woo S, Lee JM, Yoon JH, Joo I et al (2014) Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade. Radiology 270:758–767CrossRefGoogle Scholar
  22. 22.
    Nakayama K, Nakayama N, Katagiri H, Miyazaki K (2012) Mechanisms of ovarian cancer metastasis: biochemical pathways. Int J Mol Sci 13:11705–11717CrossRefGoogle Scholar
  23. 23.
    van Baal J, van Noorden CJF, Nieuwland R et al (2018) Development of peritoneal carcinomatosis in epithelial ovarian cancer: a review. J Histochem Cytochem 66:67–83CrossRefGoogle Scholar
  24. 24.
    Zaal A, Peyrot WJ, Berns PM et al (2012) Genomic aberrations relate early and advanced stage ovarian cancer. Cell Oncol (Dordr) 35:181–188CrossRefGoogle Scholar
  25. 25.
    Shridhar V, Lee J, Pandita A, Iturria S, Avula R, Staub J, Morrissey M, Calhoun E, Sen A, Kalli K, Keeney G, Roche P, Cliby W, Lu K, Schmandt R, Mills GB, Bast RC Jr, James CD, Couch FJ, Hartmann LC, Lillie J, Smith DI (2001) Genetic analysis of early- versus late-stage ovarian tumors. Cancer Res 61:5895–5904PubMedGoogle Scholar
  26. 26.
    Chen YW, Pan HB, Tseng HH, Chu HC, Hung YT, Yen YC, Chou CP (2013) Differentiated epithelial- and mesenchymal-like phenotypes in subcutaneous mouse xenografts using diffusion weighted-magnetic resonance imaging. Int J Mol Sci 14:21943–21959CrossRefGoogle Scholar
  27. 27.
    Bogani G, Tagliabue E, Ditto A, Signorelli M, Martinelli F, Casarin J, Chiappa V, Dondi G, Leone Roberti Maggiore U, Scaffa C, Borghi C, Montanelli L, Lorusso D, Raspagliesi F (2017) Assessing the risk of pelvic and para-aortic nodal involvement in apparent early-stage ovarian cancer: a predictors- and nomogram-based analyses. Gynecol Oncol 147:61–65CrossRefGoogle Scholar
  28. 28.
    Powless CA, Aletti GD, Bakkum-Gamez JN, Cliby WA (2011) Risk factors for lymph node metastasis in apparent early-stage epithelial ovarian cancer: implications for surgical staging. Gynecol Oncol 122:536–540CrossRefGoogle Scholar
  29. 29.
    Karlsson MC, Gonzalez SF, Welin J, Fuxe J (2017) Epithelial-mesenchymal transition in cancer metastasis through the lymphatic system. Mol Oncol 11:781–791CrossRefGoogle Scholar
  30. 30.
    Fan L, Liu Y, Zhang X, Kang Y, Xu C (2014) Establishment of Fischer 344 rat model of ovarian cancer with lymphatic metastasis. Arch Gynecol Obstet 289:149–154CrossRefGoogle Scholar
  31. 31.
    Heijmen L, Ter Voert EE, Nagtegaal ID et al (2013) Diffusion-weighted MR imaging in liver metastases of colorectal cancer: reproducibility and biological validation. Eur Radiol 23:748–756CrossRefGoogle Scholar
  32. 32.
    Sevcenco S, Haitel A, Ponhold L, Susani M, Fajkovic H, Shariat SF, Hiess M, Spick C, Szarvas T, Baltzer PAT (2014) Quantitative apparent diffusion coefficient measurements obtained by 3-tesla MRI are correlated with biomarkers of bladder cancer proliferative activity. PLoS One 9:e106866CrossRefGoogle Scholar
  33. 33.
    Schob S, Meyer HJ, Dieckow J et al (2017) Histogram analysis of diffusion weighted imaging at 3T is useful for prediction of lymphatic metastatic spread, proliferative activity, and cellularity in thyroid cancer. Int J Mol Sci 18CrossRefGoogle Scholar
  34. 34.
    Meyer HJ, Hohn A, Surov A (2018) Histogram analysis of ADC in rectal cancer: associations with different histopathological findings including expression of EGFR, Hif1-alpha, VEGF, p53, PD1, and KI 67. A preliminary study. Oncotarget 9:18510–18517PubMedPubMedCentralGoogle Scholar
  35. 35.
    Meyer HJ, Leifels L, Hamerla G, Höhn AK, Surov A (2018) ADC-histogram analysis in head and neck squamous cell carcinoma. Associations with different histopathological features including expression of EGFR, VEGF, HIF-1alpha, Her 2 and p53. A preliminary study. Magn Reson Imaging 54:214–217CrossRefGoogle Scholar
  36. 36.
    Meyer HJ, Pazaitis N, Surov A (2018) ADC histogram analysis of muscle lymphoma-correlation with histopathology in a rare entity. Br J Radiol 91:20180291CrossRefGoogle Scholar
  37. 37.
    Shen L, Zhou G, Tong T, Tang F, Lin Y, Zhou J, Wang Y, Zong G, Zhang L (2018) ADC at 3.0T as a noninvasive biomarker for preoperative prediction of Ki67 expression in invasive ductal carcinoma of breast. Clin Imaging 52:16–22CrossRefGoogle Scholar
  38. 38.
    Surov A, Meyer HJ, Winter K, Richter C, Hoehn AK (2018) Histogram analysis parameters of apparent diffusion coefficient reflect tumor cellularity and proliferation activity in head and neck squamous cell carcinoma. Oncotarget 9:23599–23607PubMedPubMedCentralGoogle Scholar
  39. 39.
    Kobel M, Piskorz AM, Lee S et al (2016) Optimized p53 immunohistochemistry is an accurate predictor of TP53 mutation in ovarian carcinoma. J Pathol Clin Res 2:247–258CrossRefGoogle Scholar
  40. 40.
    Casey L, Kobel M, Ganesan R et al (2017) A comparison of p53 and WT1 immunohistochemical expression patterns in tubo-ovarian high-grade serous carcinoma before and after neoadjuvant chemotherapy. Histopathology 71:736–742CrossRefGoogle Scholar
  41. 41.
    Sallum LF, Andrade L, Ramalho S, Ferracini AC, de Andrade Natal R, Brito ABC, Sarian LO, Derchain S (2018) WT1, p53 and p16 expression in the diagnosis of low- and high-grade serous ovarian carcinomas and their relation to prognosis. Oncotarget 9:15818–15827CrossRefGoogle Scholar
  42. 42.
    Kobel M, Ronnett BM, Singh N et al (2018) Interpretation of P53 immunohistochemistry in endometrial carcinomas: toward increased reproducibility. Int J Gynecol Pathol.  https://doi.org/10.1097/PGP.0000000000000488
  43. 43.
    Lindgren A, Anttila M, Rautiainen S, Arponen O, Kivelä A, Mäkinen P, Härmä K, Hämäläinen K, Kosma VM, Ylä-Herttuala S, Vanninen R, Sallinen H (2017) Primary and metastatic ovarian cancer: characterization by 3.0T diffusion-weighted MRI. Eur Radiol 27:4002–4012CrossRefGoogle Scholar
  44. 44.
    Surov A, Meyer HJ, Wienke A (2017) Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean. Oncotarget 8:75434–75444PubMedPubMedCentralGoogle Scholar
  45. 45.
    Li HM, Zhao SH, Qiang JW, Zhang GF, Feng F, Ma FH, Li YA, Gu WY (2017) Diffusion kurtosis imaging for differentiating borderline from malignant epithelial ovarian tumors: a correlation with Ki-67 expression. J Magn Reson Imaging 46:1499–1506CrossRefGoogle Scholar
  46. 46.
    Bilyk O, Coatham M, Jewer M, Postovit LM (2017) Epithelial-to-mesenchymal transition in the female reproductive tract: from normal functioning to disease pathology. Front Oncol 7:145CrossRefGoogle Scholar
  47. 47.
    Hao Y, Pan C, Chen W, Li T, Zhu WZ, Qi JP (2016) Differentiation between malignant and benign thyroid nodules and stratification of papillary thyroid cancer with aggressive histological features: whole-lesion diffusion-weighted imaging histogram analysis. J Magn Reson Imaging 44:1546–1555.  https://doi.org/10.1002/jmri.25290 CrossRefPubMedGoogle Scholar
  48. 48.
    Suo S, Zhang K, Cao M, Suo X, Hua J, Geng X, Chen J, Zhuang Z, Ji X, Lu Q, Wang H, Xu J (2016) Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J Magn Reson Imaging 43:894–902CrossRefGoogle Scholar
  49. 49.
    Li X, Yuan Y, Ren J, Shi Y, Tao X (2018) Incremental prognostic value of apparent diffusion coefficient histogram analysis in head and neck squamous cell carcinoma. Acad Radiol 25:1433–1438.  https://doi.org/10.1016/j.acra.2018.02.017 CrossRefPubMedGoogle Scholar
  50. 50.
    Maolake A, Izumi K, Natsagdorj A, Iwamoto H, Kadomoto S, Makino T, Naito R, Shigehara K, Kadono Y, Hiratsuka K, Wufuer G, Nastiuk KL, Mizokami A (2018) Tumor necrosis factor-alpha induces prostate cancer cell migration in lymphatic metastasis through CCR7 upregulation. Cancer Sci 109:1524–1531.  https://doi.org/10.1111/cas.13586 CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Liang Y, Jiao J, Liang L, Zhang J, Lu Y, Xie H, Liang Q, Wan D, Duan L, Wu Y, Zhang B (2018) Tumor necrosis factor receptor-associated factor 6 mediated the promotion of salivary adenoid cystic carcinoma progression through Smad-p38-JNK signaling pathway induced by TGF-beta. J Oral Pathol Med 47:583–589.  https://doi.org/10.1111/jop.12709 CrossRefPubMedGoogle Scholar

Copyright information

© World Molecular Imaging Society 2018

Authors and Affiliations

  • Feng Wang
    • 1
  • Yuxiang Wang
    • 2
  • Yan Zhou
    • 1
  • Congrong Liu
    • 2
  • Dong Liang
    • 3
  • Lizhi Xie
    • 4
  • Zhihang Yao
    • 1
  • Jianyu Liu
    • 1
    Email author
  1. 1.Department of RadiologyPeking University Third HospitalBeijingChina
  2. 2.Department of Pathology, School of Basic Medical SciencePeking University Third Hospital, Peking University Health Science CenterBeijingChina
  3. 3.Siemens Ltd., ChinaBeijingChina
  4. 4.GE Healthcare ChinaBeijingChina

Personalised recommendations