Meme manyetik rezonans görüntülemede malign ve benign lezyonların ayrımında histogram analizi: ön çalışma
Öz
Amaç: Bu çalışma, yağ baskısız T1 ağırlıklı ve yağ baskılı T2 ağırlıklı meme manyetik rezonans (MR) görüntülerinin histogram analizi ile malign ve benign lezyonların ayırt edilip edilemeyeceğini incelemeyi amaçladı.
Gereç ve Yöntem: 20 malign, 20 benign hastanın MR görüntüleri retrospektif tarandı. Görüntülerin histogram analizi için Osirix V.4.9 yazılımı kullanıldı. İlgi alanları (ROI) elle lezyonun tamamına yakınını kapsayacak şekilde çizildi. ROI değerlerinden gri seviye yoğunluğu, ortalama, standart sapma, entropi, tekdüzelik, çarpıklık, basıklık, boyut % alt, boyut % üst, boyut % ortalama hesaplandı. Tüm görüntü analizi MATLAB’da kurum içi program kullanılarak sağlandı.
Bulgular: Yağ baskısız T1 ağırlıklı görüntülerde, Minimum, %1, %3, %5, %10 ve %25’inci değerleri; malign lezyonlarda benign lezyonlara göre istatistiksel anlamlı olarak daha düşük izlendi. Minimum değer için sensitivite %70, spesifite %63.2 olarak saptandı. Yağ baskılı T2 ağırlıklı görüntülerde Skewness değeri malign lezyonlarda benign lezyonlara göre istatistiksel anlamlı olarak daha yüksek, Uniformity değeri; malign lezyonlarda benign lezyonlara göre istatistiksel anlamlı olarak daha düşük izlendi. Skewness değer için sensitivite %68.4, spesifite %60 olarak saptandı. Uniformity değer için sensitivite %65, spesifite %68.4 olarak saptandı.
Sonuç: Bu çalışma, yağ baskısız T1 ağırlıklı ve yağ baskılı T2 ağırlıklı görüntülerin histogram analizinin meme MR görüntülemede malign ve benign lezyonları ayırt etmek için kullanılabileceğini göstermektedir.
Anahtar Kelimeler
Kaynakça
- Torre LA, Siegel RL, Ward EM, Jemal A. Global cancer incidence and mortality rates and trends–an update. Cancer Epidemiol Biomarkers Prev. 2016;25:16–27.
- DeSantis CE, Ma J, Goding Sauer A, Newman LA, Jemal A. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 2017;67:439–48. Hulka CA, Slanetz PJ, Halpern EF, Hall DA, McCarthy KA, Moore R et al. Patients' opinion of mammography screening services: immediate results versus delayed results due to interpretation by two observers. AJR Am J Roentgenol. 1997;168:1085-9
- Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD et al. American cancer society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin. 2007;57:75-89.
- Peters NH, Borel Rinkes IH, Zuithoff NP, Mali WP, Moons KG, Peeters PH. Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology. 2008;246:116-24.
- Liu C, Liang C, Liu Z, Zhang S, Huang B. Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol. 2013;82:e782-9.
- Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights imaging. 2012 ;3:573-89. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004 ;59:1061-9. Baykara S, Baykara M, Mermi O, Yildirim H, Atmaca M. Magnetic resonance imaging histogram analysis of corpus callosum in a functional neurological disorder. Turk J Med Sci. 2021;51:140-7.
- Yildirim M, Baykara M. Differentiation of multiple myeloma and lytic bone metastases: Histogram analysis. J Comput Assist Tomogr. 2020;44:953-5.
- De Robertis R, Maris B, Cardobi N, Tinazzi Martini P, Gobbo S, Capelli P et al. Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol. 2018;28:2582-91.
- Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol. 2012 ;22:796-802
- Westra C, Dialani V, Mehta TS, Eisenberg RL. Using T2-weighted sequences to more accurately characterize breast masses seen on MRI. AJR Am J Roentgenol. 2014;202:W183-90.
- Damadian R. Tumor detection by nuclear magnetic resonance. Science. 1971;171:1151-3.
- Bitar R, Leung G, Perng R, Tadros S, Moody AR, Sarrazin J et al. MR pulse sequences: what every radiologist wants to know but is afraid to ask. Radiographics. 2006;26:513-37.
- Heller SL, Moy L, Lavianlivi S, Moccaldi M, Kim S. Differentiation of malignant and benign breast lesions using magnetization transfer imaging and dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2013;37:138-45.
- Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749-62.
- Bittner RC, Felix R. Magnetic resonance (MR) imaging of the chest: state-of-the-art. Eur Respir J. 1998 ;11:1392-404.
- Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med. 2003 ;50:92-8.
- Xu F, Liang YY, Guo Y, Liang ZP, Wu M, Chen S et al. Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis. Acta Radiol. 2020 ;61:1165-75.
- Liu HL, Zong M, Wei H, Lou JJ, Wang SQ, Zou QG et al. Differentiation between malignant and benign breast masses: combination of semi-quantitative analysis on DCE-MRI and histogram analysis of ADC maps. Clin Radiol. 2018;73:460-6.
- Liu C, Wang K, Li X, Zhang J, Ding J, Spuhler K et al. Breast lesion characterization using whole-lesion histogram analysis with stretched-exponential diffusion model. J Magn Reson Imaging. 2018 ;47:1701-10.
- Bougias H, Ghiatas A, Priovolos D, Veliou K, Christou A. Whole-lesion histogram analysis metrics of the apparent diffusion coefficient as a marker of breast lesions characterization at 1.5 T. Radiography (Lond). 2017;23:e41-e6.
- Suo S, Zhang K, Cao M, Suo X, Hua J, Geng X 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.
- Ko ES, Kim JH, Lim Y, Han BK, Cho EY, Nam SJ. Assessment of invasive breast cancer heterogeneity using whole-tumor magnetic resonance imaging texture analysis: Correlations with detailed pathological findings. Medicine (Baltimore). 2016;95:e2453.
- Kim JH, Ko ES, Lim Y, Lee KS, Han BK, Ko EY et al. Breast Cancer Heterogeneity: MR imaging texture analysis and survival outcomes. Radiology. 2017;282:665-75.
Histogram analysis for the differentiation of malignant and benign lesions in breast magnetic resonance imaging: preliminary study
Öz
Purpose: The present study assesses whether malignant and benign lesions can be distinguished through histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted breast magnetic resonance images (MRIs).
Materials and Methods: MRIs of 20 malignant and 20 benign breast lesions were reviewed retrospectively by histogram analysis performed using Osirix V.4.9 software. The regions of interest (ROIs) were drawn manually to include almost the entire lesion, and values from these ROIs were used to calculate gray-level intensity mean, standard deviation, entropy, uniformity, skewness, kurtosis, and percentile values.
Results: In non-fat-suppressed T1-weighted images, the minimum, 1st, 3rd, 5th, 10th and 25th percentile values were significantly lower in the malignant lesions than in the benign lesions. The minimum value had sensitivity of 70% and specificity of 63.2%. On the fat-suppressed T2-weighted images, skewness was significantly higher while uniformity was significantly lower in malignant lesions than benign lesions. Skewness had 68.4% sensitivity and 60% specificity, and uniformity had 65% sensitivity and 68.4% specificity.
Conclusion: The results of this study demonstrated that histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted images can be used to differentiate malignant and benign lesions in breast MRI.
Anahtar Kelimeler
Breast malignant benign magnetic resonance imaging histogram
Kaynakça
- Torre LA, Siegel RL, Ward EM, Jemal A. Global cancer incidence and mortality rates and trends–an update. Cancer Epidemiol Biomarkers Prev. 2016;25:16–27.
- DeSantis CE, Ma J, Goding Sauer A, Newman LA, Jemal A. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 2017;67:439–48. Hulka CA, Slanetz PJ, Halpern EF, Hall DA, McCarthy KA, Moore R et al. Patients' opinion of mammography screening services: immediate results versus delayed results due to interpretation by two observers. AJR Am J Roentgenol. 1997;168:1085-9
- Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD et al. American cancer society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin. 2007;57:75-89.
- Peters NH, Borel Rinkes IH, Zuithoff NP, Mali WP, Moons KG, Peeters PH. Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology. 2008;246:116-24.
- Liu C, Liang C, Liu Z, Zhang S, Huang B. Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol. 2013;82:e782-9.
- Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights imaging. 2012 ;3:573-89. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004 ;59:1061-9. Baykara S, Baykara M, Mermi O, Yildirim H, Atmaca M. Magnetic resonance imaging histogram analysis of corpus callosum in a functional neurological disorder. Turk J Med Sci. 2021;51:140-7.
- Yildirim M, Baykara M. Differentiation of multiple myeloma and lytic bone metastases: Histogram analysis. J Comput Assist Tomogr. 2020;44:953-5.
- De Robertis R, Maris B, Cardobi N, Tinazzi Martini P, Gobbo S, Capelli P et al. Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol. 2018;28:2582-91.
- Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol. 2012 ;22:796-802
- Westra C, Dialani V, Mehta TS, Eisenberg RL. Using T2-weighted sequences to more accurately characterize breast masses seen on MRI. AJR Am J Roentgenol. 2014;202:W183-90.
- Damadian R. Tumor detection by nuclear magnetic resonance. Science. 1971;171:1151-3.
- Bitar R, Leung G, Perng R, Tadros S, Moody AR, Sarrazin J et al. MR pulse sequences: what every radiologist wants to know but is afraid to ask. Radiographics. 2006;26:513-37.
- Heller SL, Moy L, Lavianlivi S, Moccaldi M, Kim S. Differentiation of malignant and benign breast lesions using magnetization transfer imaging and dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2013;37:138-45.
- Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749-62.
- Bittner RC, Felix R. Magnetic resonance (MR) imaging of the chest: state-of-the-art. Eur Respir J. 1998 ;11:1392-404.
- Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med. 2003 ;50:92-8.
- Xu F, Liang YY, Guo Y, Liang ZP, Wu M, Chen S et al. Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis. Acta Radiol. 2020 ;61:1165-75.
- Liu HL, Zong M, Wei H, Lou JJ, Wang SQ, Zou QG et al. Differentiation between malignant and benign breast masses: combination of semi-quantitative analysis on DCE-MRI and histogram analysis of ADC maps. Clin Radiol. 2018;73:460-6.
- Liu C, Wang K, Li X, Zhang J, Ding J, Spuhler K et al. Breast lesion characterization using whole-lesion histogram analysis with stretched-exponential diffusion model. J Magn Reson Imaging. 2018 ;47:1701-10.
- Bougias H, Ghiatas A, Priovolos D, Veliou K, Christou A. Whole-lesion histogram analysis metrics of the apparent diffusion coefficient as a marker of breast lesions characterization at 1.5 T. Radiography (Lond). 2017;23:e41-e6.
- Suo S, Zhang K, Cao M, Suo X, Hua J, Geng X 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.
- Ko ES, Kim JH, Lim Y, Han BK, Cho EY, Nam SJ. Assessment of invasive breast cancer heterogeneity using whole-tumor magnetic resonance imaging texture analysis: Correlations with detailed pathological findings. Medicine (Baltimore). 2016;95:e2453.
- Kim JH, Ko ES, Lim Y, Lee KS, Han BK, Ko EY et al. Breast Cancer Heterogeneity: MR imaging texture analysis and survival outcomes. Radiology. 2017;282:665-75.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Klinik Tıp Bilimleri |
| Bölüm | Araştırma |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Eylül 2022 |
| Kabul Tarihi | 3 Haziran 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 47 Sayı: 3 |
