MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases
- 259 Downloads
The purpose of the study was to determine whether the pre-treated MR texture features of colorectal liver metastases (CRLMs) are predictive of therapeutic response after chemotherapy.
The study included twenty-six consecutive patients (a total of 193 liver metastasis) with unrespectable CRLMs at our institution from August 2014 to February 2016. Lesions were categorized into either responding group or non-responding group according to changes in size. Texture analysis was quantified on T2-weighted images by two radiologists with consensus on regions of interest which were manually drawn on the largest cross-sectional area of the lesions. Five histogram features (mean, variance, skewness, kurtosis, and entropy1) and five gray level co-occurrence matrix features (GLCM; angular second moment (ASM), entropy2, contrast, correlation, and inverse difference moment (IDM)) were extracted. The texture parameters were statistically analyzed to identify the differences between the two groups, and the potential predictive parameters to differentiate the responding group from the non-responding group were subsequently tested using multivariable logistic regression analysis.
A total of 107 responding and 86 non-responding lesions were evaluated. A higher variance, entropy1, contrast, entropy2 and a lower ASM, correlation, IDM were independently (P < 0.05) associated with a good response to chemotherapy with the areas under the ROC curves (AUCs) of 0.602–0.784. Variance (P < 0.001) and ASM (P = 0.001) remained potential predictive values to discriminate responding lesions from non-responding lesions when tested using multivariable logistic regression analysis. The highest AUC of the predictors from the association of variance and ASM was 0.814.
MR texture features on pre-treated T2 images have the potential to predict the therapeutic response of colorectal liver metastases.
KeywordsTexture analysis Histogram Gray level co-occurrence matrix features Magnetic resonance imaging Colorectal liver metastases
The authors declare that there is no conflict of interest regarding the publication of this paper. This study was supported by the National Natural Science Foundation of China (Grant No. 81501437).
- 1.Torre LA, Bray F, Siegel RL, et al. (2015) Global cancer statistics, 2012. CA 65:87–108Google Scholar
- 2.Kemeny N (2006) Management of liver metastases from colorectal cancer. Oncology (Williston Park) 20:1185–1186Google Scholar
- 3.Leporrier J, Maurel J, Chiche L, et al. (2006) A population-based study of the incidence, management and prognosis of hepatic metastases from colorectal cancer. Br J Surg 93:465–474Google Scholar
- 4.Asselin M, O’Connor JPB, Boellaard R, Thacker NA, Jackson A (2012) Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer 48:447–455Google Scholar
- 5.Cook GJR, Yip C, Siddique M, et al. (2013) Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med 54:19–26Google Scholar
- 6.Liu J, Mao Y, Li Z, et al. (2016) Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging 44:445–455Google Scholar
- 7.Michoux N, Van den Broeck S, Lacoste L, et al. (2015) Texture analysis on MR images helps predicting non-response to NAC in breast cancer. BMC Cancer 15:574Google Scholar
- 8.De Cecco CN, Ganeshan B, Ciolina M, et al. (2015) Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Investig Radiol 50:239–245Google Scholar
- 9.De Cecco CN, Ciolina M, Caruso D, et al. (2016) Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol 41:1728–1735Google Scholar
- 10.Miles KA, Ganeshan B, Griffiths MR, Young RCD, Chatwin CR (2009) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 250:444–452Google Scholar
- 11.Lubner MG, Stabo N, Lubner SJ, et al. (2015) CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging 40:2331–2337Google Scholar
- 12.Rao S-X, Lambregts DM, Schnerr RS, et al. (2016) CT texture analysis in colorectal liver metastases: a better way than size and volume measurements to assess response to chemotherapy? United Eur Gastroenterol J 4:257–263Google Scholar
- 13.Ahn SJ, Kim JH, Park SJ, Han JK (2016) Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol 85:1867–1874Google Scholar
- 14.Zhang X, Gao X, Liu BJ, et al. (2015) Effective staging of fibrosis by the selected texture features of liver: which one is better, CT or MR imaging? Comput Med Imaging Graph 46:227–236Google Scholar
- 15.Ng F, Kozarski R, Ganeshan B, Goh V (2013) Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 82:342–348Google Scholar
- 16.Cui Y, Zhang X-P, Sun Y-S, Tang L, Shen L (2008) Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. Radiology 248:894–900Google Scholar
- 17.Ganeshan B, Goh V, Mandeville HC, et al. (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336Google Scholar
- 18.Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184Google Scholar
- 19.Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802Google Scholar
- 20.Oh JS, Kang BC, Roh J-L, et al. (2015) Intratumor textural heterogeneity on pretreatment (18)F-FDG PET images predicts response and survival after chemoradiotherapy for hypopharyngeal cancer. Ann Surg Oncol 22:2746–2754Google Scholar
- 21.Liang HY, Huang YQ, Yang ZX, Ying-Ding Zeng MS, Rao SX (2016) Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases. Eur Radiol 26:2009–2018Google Scholar
- 22.Tournigand C, André T, Achille E, et al. (2004) FOLFIRI followed by FOLFOX6 or the reverse sequence in advanced colorectal cancer: a randomized GERCOR study. J Clin Oncol 22:229–237Google Scholar
- 23.Colucci G, Gebbia V, Paoletti G, et al. (2005) Phase III randomized trial of FOLFIRI versus FOLFOX4 in the treatment of advanced colorectal cancer: a multicenter study of the Gruppo Oncologico Dell’Italia Meridionale. J Clin Oncol 23:4866–4875Google Scholar
- 24.Kabbinavar FF, Schulz J, McCleod M, et al. (2005) Addition of bevacizumab to bolus fluorouracil and leucovorin in first-line metastatic colorectal cancer: results of a randomized phase II trial. J Clin Oncol 23:3697–3705Google Scholar
- 25.Egger ME, Cannon RM, Metzger TL, et al. (2013) Assessment of chemotherapy response in colorectal liver metastases in patients undergoing hepatic resection and the correlation to pathologic residual viable tumor. J Am Coll Surg 216:845–857Google Scholar
- 26.Chun YS, Vauthey J-N, Boonsirikamchai P, et al. (2009) Association of computed tomography morphologic criteria with pathologic response and survival in patients treated with bevacizumab for colorectal liver metastases. JAMA 302:2338–2344Google Scholar
- 27.Shindoh J, Loyer EM, Kopetz S, et al. (2012) Optimal morphologic response to preoperative chemotherapy: an alternate outcome end point before resection of hepatic colorectal metastases. J Clin Oncol 30:4566–4572Google Scholar
- 28.Chung WS, Park MS, Shin SJ, et al. (2012) Response evaluation in patients with colorectal liver metastases: RECIST version 1.1 versus modified CT criteria. Am J Roentgenol 199:809–815Google Scholar