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Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN-amplification in neuroblastomas: preliminary results

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Abstract

Objectives

To determine the role of apparent diffusion coefficient (ADC) histogram analysis in the identification of MYCN-amplification status in neuroblastomas.

Methods

We retrospectively evaluated imaging records from 62 patients with neuroblastomas (median age: 15 months (interquartile range (IQR): 7–24 months); 38 females) who underwent magnetic resonance imaging at our institution before the initiation of any therapy or biopsy. Fourteen patients had MYCN-amplified (MYCNA) neuroblastoma. Histogram parameters of ADC maps from the entire tumour was obtained from the baseline images and the normalised images. The Mann-Whitney U test was used to compare the absolute and normalised histogram parameters amongst neuroblastomas with and without MYCN-amplification. Receiver operating characteristic (ROC) curves and area under the curves (AUC) were generated for the statistically significant histogram parameters. Cut-offs obtained from the ROC curves were evaluated on an external validation set (n-15, MYCNA-6, F-7, age 24 months (10–60)). A logistic regression model was trained to predict MYCNA by combining statistically significant histogram parameters and was evaluated on the validation set.

Results

MYCN-amplified neuroblastomas had statistically significant higher maximum ADC and lower minimum ADC than non-amplified neuroblastomas. They also demonstrated higher entropy, variance, energy, and lower uniformity than non-amplified neoplasms (p > 0.05). Energy, entropy, and maximum ADC had AUC of 0.85, 0.79, and 0.82, respectively.

Conclusions

Whole tumour ADC histogram analysis of neuroblastomas can differentiate between tumours with and without MYCN-amplification. These parameters can help identify areas for targeted biopsies or can be used to predict subtypes of these high-risk tumours before biopsy results are available.

Key Points

• MYCN-amplification significantly affects treatment decisions in neuroblastomas.

• MYCN-amplified neuroblastomas had significantly different ADC histogram metrics as compared to tumours without amplification.

• ADC histogram metrics can be used to predict MYCN-amplification status based on imaging.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

MRI:

Magnetic resonance imaging

MYCNA:

MYCN-amplified

ROC:

Receiver operating characteristics curve

References

  1. Van Arendonk KJ, Chung DH (2019) Neuroblastoma: tumor biology and its implications for staging and treatment. Children (Basel). https://doi.org/10.3390/children6010012

  2. Brodeur GM (2003) Neuroblastoma: biological insights into a clinical enigma. Nat Rev Cancer 3:203–216. https://doi.org/10.1038/nrc1014

    Article  CAS  PubMed  Google Scholar 

  3. Maris JM, Weiss MJ, Guo C et al (2000) Loss of heterozygosity at 1p36 independently predicts for disease progression but not decreased overall survival probability in neuroblastoma patients: a children’s cancer group study. J Clin Oncol 18:1888–1899. https://doi.org/10.1200/JCO.2000.18.9.1888

    Article  CAS  PubMed  Google Scholar 

  4. Komuro H, Valentine MB, Rowe ST et al (1998) Fluorescence in situ hybridisation analysis of chromosome 1p36 deletions in human MYCN amplified neuroblastoma. J Pediatr Surg 33:1695–1698. https://doi.org/10.1016/s0022-3468(98)90612-1

  5. Bagatell R, Beck-Popovic M, London WB et al (2009) Significance of MYCN amplification in international neuroblastoma staging system stage 1 and 2 neuroblastoma: a report from the International Neuroblastoma Risk Group database. J Clin Oncol 27:365–370. https://doi.org/10.1200/JCO.2008.17.9184

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Schmidt ML, Lal A, Seeger RC et al (2005) Favorable prognosis for patients 12 to 18 months of age with stage 4 nonamplified MYCN neuroblastoma: a Children’s Cancer Group Study. J Clin Oncol 23:6474–6480. https://doi.org/10.1200/JCO.2005.05.183

    Article  CAS  PubMed  Google Scholar 

  7. Katzenstein HM, Bowman LC, Brodeur GM, et al (1998) Prognostic significance of age, MYCN oncogene amplification, tumor cell ploidy, and histology in 110 infants with stage D(S) neuroblastoma: the pediatric oncology group experience--a pediatric oncology group study. J Clin Oncol 16:2007–2017. https://doi.org/10.1200/JCO.1998.16.6.2007

  8. Ambros PF, Ambros IM, Brodeur GM et al (2009) International consensus for neuroblastoma molecular diagnostics: report from the International Neuroblastoma Risk Group (INRG) Biology Committee. Br J Cancer 100:1471–1482. https://doi.org/10.1038/sj.bjc.6605014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Marrano P, Irwin MS, Thorner PS (2017) Heterogeneity of MYCN amplification in neuroblastoma at diagnosis, treatment, relapse, and metastasis. Genes Chromosomes Cancer 56:28–41. https://doi.org/10.1002/gcc.22398

    Article  CAS  PubMed  Google Scholar 

  10. Ambros IM, Benard J, Boavida M et al (2003) Quality assessment of genetic markers used for therapy stratification. J Clin Oncol 21:2077–2084. https://doi.org/10.1200/JCO.2003.03.025

    Article  CAS  PubMed  Google Scholar 

  11. Koh D-M, Collins DJ (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635. https://doi.org/10.2214/AJR.06.1403

    Article  PubMed  Google Scholar 

  12. Enkhbaatar N-E, Inoue S, Yamamuro H et al (2018) MR imaging with apparent diffusion coefficient histogram analysis: evaluation of locally advanced rectal cancer after chemotherapy and radiation therapy. Radiology 288:129–137. https://doi.org/10.1148/radiol.2018171804

    Article  PubMed  Google Scholar 

  13. Wang K, Cheng J, Wang Y, Wu G (2019) Renal cell carcinoma: preoperative evaluate the grade of histological malignancy using volumetric histogram analysis derived from magnetic resonance diffusion kurtosis imaging. Quant Imaging Med Surg 9:671–680. https://doi.org/10.21037/qims.2019.04.14

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhang Y-D, Wu C-J, Wang Q et al (2015) Comparison of utility of histogram apparent diffusion coefficient and R2* for differentiation of low-grade from high-grade clear cell renal cell carcinoma. AJR Am J Roentgenol 205:W193–W201. https://doi.org/10.2214/AJR.14.13802

    Article  PubMed  Google Scholar 

  15. Xing P, Chen L, Yang Q et al (2021) Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging. Cancer Imaging 21:54. https://doi.org/10.1186/s40644-021-00423-5

    Article  PubMed  PubMed Central  Google Scholar 

  16. Umanodan T, Fukukura Y, Kumagae Y et al (2017) ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma. J Magn Reson Imaging 45:1195–1203. https://doi.org/10.1002/jmri.25452

    Article  PubMed  Google Scholar 

  17. Ren J, Yuan Y, Tao X (2021) Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma. Eur Radiol. https://doi.org/10.1007/s00330-021-08310-0

  18. Li D, Cui Y, Hou L et al (2021) Diffusion kurtosis imaging-derived histogram metrics for prediction of resistance to neoadjuvant chemoradiotherapy in rectal adenocarcinoma: preliminary findings. Eur J Radiol 144:109963. https://doi.org/10.1016/j.ejrad.2021.109963

    Article  PubMed  Google Scholar 

  19. Lasso A, Heffter T, Rankin A et al (2014) PLUS: open-source toolkit for ultrasound-guided intervention systems. IEEE Trans Biomed Eng 61:2527–2537. https://doi.org/10.1109/TBME.2014.2322864

    Article  PubMed  PubMed Central  Google Scholar 

  20. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163. https://doi.org/10.1016/j.jcm.2016.02.012

    Article  PubMed  PubMed Central  Google Scholar 

  22. Barrett T, Lawrence EM, Priest AN et al (2019) Repeatability of diffusion-weighted MRI of the prostate using whole lesion ADC values, skew and histogram analysis. Eur J Radiol 110:22–29. https://doi.org/10.1016/j.ejrad.2018.11.014

    Article  PubMed  Google Scholar 

  23. Huo J, Alger J, Kim H et al (2016) Between-scanner and between-visit variation in normal white matter apparent diffusion coefficient values in the setting of a multi-center clinical trial. Clin Neuroradiol 26:423–430. https://doi.org/10.1007/s00062-015-0381-3

    Article  CAS  PubMed  Google Scholar 

  24. Schmeel FC (2018) Variability in quantitative diffusion-weighted MR imaging (DWI) across different scanners and imaging sites: is there a potential consensus that can help reducing the limits of expected bias? Eur Radiol 29:1–3. https://doi.org/10.1007/s00330-018-5866-4

    Article  Google Scholar 

  25. Ghosh A, Singh T, Singla V et al (2017) Comparison of absolute apparent diffusion coefficient (ADC) values in ADC maps generated across different postprocessing software: reproducibility in endometrial carcinoma. AJR Am J Roentgenol 209:1312–1320. https://doi.org/10.2214/AJR.17.18002

    Article  PubMed  Google Scholar 

  26. Huang M, Weiss WA (2013) Neuroblastoma and MYCN. Cold Spring Harb Perspect Med 3:a014415. https://doi.org/10.1101/cshperspect.a014415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chen X, Wang H, Huang K et al (2021) CT-based radiomics signature with machine learning predicts MYCN amplification in pediatric abdominal neuroblastoma. Front Oncol 11:687884. https://doi.org/10.3389/fonc.2021.687884

    Article  PubMed  PubMed Central  Google Scholar 

  28. Dijkstra H, Sijens PE, van der Hoorn A, van Laar PJ (2020) Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas. Acta Radiol 61:76–84. https://doi.org/10.1177/0284185119852729

    Article  PubMed  Google Scholar 

  29. Zeilinger MG, Lell M, Baltzer PAT et al (2017) Impact of post-processing methods on apparent diffusion coefficient values. Eur Radiol 27:946–955. https://doi.org/10.1007/s00330-016-4403-6

    Article  PubMed  Google Scholar 

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Funding

The authors state that this work has not received any funding.

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Correspondence to Adarsh Ghosh.

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Guarantor

The scientific guarantor of this publication is Dr Lisa States.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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• diagnostic

• performed at one institution

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Ghosh, A., Yekeler, E., Dalal, D. et al. Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN-amplification in neuroblastomas: preliminary results. Eur Radiol 32, 8453–8462 (2022). https://doi.org/10.1007/s00330-022-08750-2

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