Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67

Abstract

Purpose

A fast, reliable and non-invasive method is required in differentiating brain metastases (BMs) originating from lung cancer (LC) and breast cancer (BC). The aims of this study were to assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating BMs originated from LC and BC, and then to investigate further the association of ADC histogram parameters with Ki-67 index in BMs.

Methods

A total of 55 patients (LC, N = 40; BC, N = 15) with BMs histopathologically confirmed were enrolled in the study. The LC group was divided into small-cell lung cancer (SCLC; N = 15) and non-small-cell lung cancer (NSCLC; N = 25) groups. ADC histogram parameters (ADC_max, ADC_mean, ADC_min, ADC_median, ADC₁₀, ADC₂₅, ADC₇₅ and ADC₉₀, skewness, kurtosis and entropy) were derived from ADC maps. Mann–Whitney U-test, independent samples t-test, receiver operating characteristic (ROC) analysis and Spearman correlation analysis were used for statistical assessment.

Results

ADC histogram parameters did not show significant differences between LC and BC groups (p > 0.05). Subgroup analysis showed that various ADC histogram parameters were found to be statistically lower in the SCLC group compared to the NSCLC and BC groups (p < 0.05). ROC analysis showed that ADC_mean and ADC₁₀ for differentiating SCLC BMs from NSCLC, and ADC₂₅ for differentiating SCLC BMs from BC achieved optimal diagnostic performances. Various histogram parameters were found to be significantly correlated with Ki-67 (p < 0.05).

Conclusion

Histogram analysis of ADC maps may reflect tumoural proliferation potential in BMs and can be useful in differentiating SCLC BMs from NSCLC and BC BMs.

Keywords

Brain metastases lung cancer breast cancer apparent diffusion coefficient histogram analysis

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References

Gavrilovic

Posner

JB.

Brain metastases: epidemiology and pathophysiology. J Neurooncol 2005; 75: 5–14.

Soffietti

Cornu

Delattre

, et al. EFNS guidelines on diagnosis and treatment of brain metastases: report of an EFNS Task Force. Eur J Neurol 2006; 13: 674–681.

Nayak

Lee

Wen

PY.

Epidemiology of brain metastases. Curr Oncol Rep 2012; 14: 48–54.

Stelzer

KJ.

Epidemiology and prognosis of brain metastases. Surg Neurol Int 2013; 4: 192–202.

Scholzen

Gerdes

The Ki-67 protein: From the known and the unknown. J Cell Physiol 2000; 182: 311–322.

Castaneda

Castillo

Bernabe

, et al. Impact of pathological features of brain metastases in prognosis. Biomark Med 2018; 12: 475–485.

Surov

Meyer

Wienke

Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. Oncotarget 2017; 8: 59492–59499.

Meyer

Fiedler

Kornhuber

, et al. Comparison of diffusion-weighted imaging findings in brain metastases of different origin. Clin Imaging 2015; 39; 965–969.

Duygulu

Ovali

Çalli

, et al. Intracerebral metastasis showing restricted diffusion: correlation with histopathologic findings. Eur J Radiol 2010; 74: 117–120.

10.

Jung

Park

Hong

C-K

, et al. Diffusion-weighted imaging of brain metastasis from lung cancer: correlation of MRI parameters with the histologic type and gene mutation status. AJNR Am J Neuroradiol 2018; 39: 273–279.

11.

Hayashida

Hirai

Morishita

, et al. Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. AJNR Am J Neuroradiol 2006; 27: 1419–1425.

12.

Haghighatkhah

Yousefi

The diagnostic value of the apparent diffusion coefficient values derived from magnetic resonance imaging and diffusion-weighted imaging in differentiating the types of metastatic brain tumors. Int J Cancer Manag 2020; 13: e95813.

13.

, et al. Utility of histogram analysis of ADC maps for differentiating orbital tumors. Diagn Interv Radiol 2015; 22: 161–167.

14.

Wang

Y-J

X-Q

, et al. Histogram analysis of apparent diffusion coefficient maps for the differentiation between lymphoma and metastatic lymph nodes of squamous cell carcinoma in head and neck region. Acta Radiol 2018; 59: 672–680.

15.

Tsuchiya

Doai

Usuda

, et al. Non-small cell lung cancer: whole-lesion histogram analysis of the apparent diffusion coefficient for assessment of tumor grade, lymphovascular invasion and pleural invasion. Plos One 2017; 12: e0172433.

16.

Kang

Choi

Kim

, et al. Gliomas: histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging – correlation with tumor grade. Radiology 2011; 261: 882–890.

17.

Bozdağ M, Er A, Çinkooğlu A. Histogram analysis of ADC maps for differentiating brain metastases from different histological types of lung cancers. Can Assoc Radiol J 2020; 72: 271–278.

18.

Gihr

Horvath-Rizea

Garnov

, et al. Diffusion profiling via histogram approach distinguishes low-grade from high-grade meningiomas, can reflect the respective proliferative potential and progesterone receptor status. Mol Imaging Biol 2018; 20: 632–640.

19.

Meyer

Höhn

Surov

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 2018; 9: 18510–18517.

20.

Schob

Münch

Dieckow

, et al. Whole tumor histogram-profiling of diffusion-weighted magnetic resonance images reflects tumorbiological features of primary central nervous system lymphoma. Transl Oncol 2018; 11: 504–510.

21.

Surov

Meyer

Winter

, et al. Histogram analysis parameters of apparent diffusion coefficient reflect tumor cellularity and proliferation activity in head and neck squamous cell carcinoma. Oncotarget 2018; 9: 23599–23607.

22.

Meyer

Gundermann

Höhn

, et al. Associations between whole tumor histogram analysis parameters derived from ADC maps and expression of EGFR, VEGF, Hif 1-alpha, Her-2 and Histone 3 in uterine cervical cancer. Magn Reson Imaging 2019; 57: 68–74.

23.

Suh

Kotecha

Chao

, et al. Current approaches to the management of brain metastases. Nat Rev Clin Oncol 2020; 17: 279–299.

24.

Zhang

Chen

Liu

, et al. Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps. J Magn Reson Imaging 2017; 45: 440–449.

25.

Hirano

Satake

Ishigaki

, et al. Diffusion-weighted imaging of breast masses: comparison of diagnostic performance using various apparent diffusion coefficient parameters. AJR Am J Roentgenol 2012; 198: 717–722.

26.

Suo

Zhang

Cao

, et al. 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 2016; 43: 894–902.

27.

Suo

Zhang

Cheng

, et al. Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging. Eur Radiol 2019; 29: 1425–1434.

28.

Mori

Ota

Mugikura

, et al. Luminal-type breast cancer: correlation of apparent diffusion coefficients with the Ki-67 labeling index. Radiology 2015; 274: 66–73.

29.

Ortiz-Ramón

Larroza

Ruiz-España

, et al. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 2018; 28: 4514–4523.