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Published in: European Radiology 8/2020

01-08-2020 | Magnetic Resonance Imaging | Gastrointestinal

MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer

Authors: Hayeong Park, Kyung Ah Kim, Ji-Han Jung, Jeongbae Rhie, Sun Young Choi

Published in: European Radiology | Issue 8/2020

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Abstract

Objectives

This study aimed to evaluate the efficiency of imaging features and texture analysis (TA) based on baseline rectal MRI for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) and tumor recurrence in patients with locally advanced rectal cancer (LARC).

Methods

Consecutive patients with LARC who underwent rectal MRI between January 2014 and December 2015 and surgical resection after completing nCRT were retrospectively enrolled. Imaging features were analyzed, and TA parameters were extracted from the tumor volume of interest (VOI) from baseline rectal MRI. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the optimal TA parameter cutoff values to stratify the patients. Logistic and Cox regression analyses were performed to assess the efficacy of each imaging feature and texture parameter in predicting tumor response and disease-free survival.

Results

In total, 78 consecutive patients were enrolled. In the logistic regression, good treatment response was associated with lower tumor location (OR = 13.284, p = 0.012), low Conv_Min (OR = 0.300, p = 0.013) and high Conv_Std (OR = 3.174, p = 0.016), Shape_Sphericity (OR = 3.170, p = 0.015), and Shape_Compacity (OR = 2.779, p = 0.032). In the Cox regression, a greater risk of tumor recurrence was related to higher cT stage (HR = 5.374, p = 0.044), pelvic side wall lymph node positivity (HR = 2.721, p = 0.013), and gray-level run length matrix_long-run low gray-level emphasis (HR = 2.268, p = 0.046).

Conclusions

Imaging features and TA based on baseline rectal MRI could be valuable for predicting the treatment response to nCRT for rectal cancer and tumor recurrence.

Key Points

Imaging features and texture parameters of T2-weighted MR images of rectal cancer can help to predict treatment response and the risk for tumor recurrence.
Tumor location as well as conventional and shape indices of texture features can help to predict treatment response for rectal cancer.
Clinical T stage, positive pelvic side wall lymph nodes, and the high-order texture parameter, GLRLM_LRLGE, can help to predict tumor recurrence for rectal cancer.
Literature
1.
go back to reference Cui Y, Yang X, Shi Z et al (2019) Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 29:1211–1220CrossRef Cui Y, Yang X, Shi Z et al (2019) Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 29:1211–1220CrossRef
2.
go back to reference Ryan JE, Warrier SK, Lynch AC, Ramsay RG, Phillips WA, Heriot AG (2016) Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review. Colorectal Dis 18:234–246CrossRef Ryan JE, Warrier SK, Lynch AC, Ramsay RG, Phillips WA, Heriot AG (2016) Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review. Colorectal Dis 18:234–246CrossRef
3.
go back to reference Meng Y, Zhang C, Zou S et al (2018) MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget 9:11999CrossRef Meng Y, Zhang C, Zou S et al (2018) MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget 9:11999CrossRef
4.
go back to reference Prezzi D, Goh V (2016) Rectal cancer magnetic resonance imaging: imaging beyond morphology. Clin Oncol (R Coll Radiol) 28:83–92CrossRef Prezzi D, Goh V (2016) Rectal cancer magnetic resonance imaging: imaging beyond morphology. Clin Oncol (R Coll Radiol) 28:83–92CrossRef
5.
go back to reference Lambregts DMJ, Maas M, Boellaard TN et al (2020) Long-term imaging characteristics of clinical complete responders during watch-and-wait for rectal cancer—an evaluation of over 1500 MRIs. Eur Radiol 30:272–280CrossRef Lambregts DMJ, Maas M, Boellaard TN et al (2020) Long-term imaging characteristics of clinical complete responders during watch-and-wait for rectal cancer—an evaluation of over 1500 MRIs. Eur Radiol 30:272–280CrossRef
6.
go back to reference Lambregts DM, Boellaard TN, Beets-Tan RG (2019) Response evaluation after neoadjuvant treatment for rectal cancer using modern MR imaging: a pictorial review. Insights Imaging 10:15CrossRef Lambregts DM, Boellaard TN, Beets-Tan RG (2019) Response evaluation after neoadjuvant treatment for rectal cancer using modern MR imaging: a pictorial review. Insights Imaging 10:15CrossRef
7.
go back to reference Santiago I, Barata M, Figueiredo N et al (2020) The split scar sign as an indicator of sustained complete response after neoadjuvant therapy in rectal cancer. Eur Radiol 30:224–238CrossRef Santiago I, Barata M, Figueiredo N et al (2020) The split scar sign as an indicator of sustained complete response after neoadjuvant therapy in rectal cancer. Eur Radiol 30:224–238CrossRef
8.
go back to reference Hotker AM, Garcia-Aguilar J, Gollub MJ (2014) Multi-parametric MRI of rectal cancer in the assessment of response to therapy: a systematic review. Dis Colon Rectum 57:790CrossRef Hotker AM, Garcia-Aguilar J, Gollub MJ (2014) Multi-parametric MRI of rectal cancer in the assessment of response to therapy: a systematic review. Dis Colon Rectum 57:790CrossRef
9.
go back to reference Joye I, Deroose CM, Vandecaveye V, Haustermans K (2014) The role of diffusion-weighted MRI and (18)F-FDG PET/CT in the prediction of pathologic complete response after radiochemotherapy for rectal cancer: a systematic review. Radiother Oncol 113:158–165CrossRef Joye I, Deroose CM, Vandecaveye V, Haustermans K (2014) The role of diffusion-weighted MRI and (18)F-FDG PET/CT in the prediction of pathologic complete response after radiochemotherapy for rectal cancer: a systematic review. Radiother Oncol 113:158–165CrossRef
10.
go back to reference Liu Z, Wang S, Dong D et al (2019) The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics 9:1303–1322CrossRef Liu Z, Wang S, Dong D et al (2019) The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics 9:1303–1322CrossRef
11.
go back to reference Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503CrossRef Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503CrossRef
12.
go back to reference Jalil O, Afaq A, Ganeshan B et al (2017) Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis 19:349–362CrossRef Jalil O, Afaq A, Ganeshan B et al (2017) Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis 19:349–362CrossRef
13.
go back to reference Nardone V, Reginelli A, Scala F et al (2019) Magnetic-resonance-imaging texture analysis predicts early progression in rectal cancer patients undergoing neoadjuvant chemoradiation. Gastroenterol Res Pract 2019:8505798CrossRef Nardone V, Reginelli A, Scala F et al (2019) Magnetic-resonance-imaging texture analysis predicts early progression in rectal cancer patients undergoing neoadjuvant chemoradiation. Gastroenterol Res Pract 2019:8505798CrossRef
14.
go back to reference Horvat N, Veeraraghavan H, Khan M et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287:833–843CrossRef Horvat N, Veeraraghavan H, Khan M et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287:833–843CrossRef
15.
go back to reference Shu Z, Fang S, Ye Q et al (2019) Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images. Abdom Radiol (NY) 44:3775–3784CrossRef Shu Z, Fang S, Ye Q et al (2019) Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images. Abdom Radiol (NY) 44:3775–3784CrossRef
16.
go back to reference Vandendorpe B, Durot C, Lebellec L et al (2019) Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer. Radiother Oncol 135:153–160CrossRef Vandendorpe B, Durot C, Lebellec L et al (2019) Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer. Radiother Oncol 135:153–160CrossRef
17.
go back to reference Liu Z, Zhang X-Y, Shi Y-J et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 23:7253–7262CrossRef Liu Z, Zhang X-Y, Shi Y-J et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 23:7253–7262CrossRef
18.
go back to reference Grossmann P, Narayan V, Chang K et al (2017) Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol 19:1688–1697CrossRef Grossmann P, Narayan V, Chang K et al (2017) Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol 19:1688–1697CrossRef
19.
go back to reference Yang L, Qiu M, Xia C et al (2019) Value of high-resolution DWI in combination with texture analysis for the evaluation of tumor response after preoperative chemoradiotherapy for locally advanced rectal cancer. AJR Am J Roentgenol:1–8. https://doi.org/10.2214/AJR.18.20689 Yang L, Qiu M, Xia C et al (2019) Value of high-resolution DWI in combination with texture analysis for the evaluation of tumor response after preoperative chemoradiotherapy for locally advanced rectal cancer. AJR Am J Roentgenol:1–8. https://​doi.​org/​10.​2214/​AJR.​18.​20689
20.
go back to reference Ahmed A, Gibbs P, Pickles M, Turnbull L (2013) Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 38:89–101CrossRef Ahmed A, Gibbs P, Pickles M, Turnbull L (2013) Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 38:89–101CrossRef
21.
go back to reference Dworak O, Keilholz L, Hoffmann A (1997) Pathological features of rectal cancer after preoperative radiochemotherapy. Int J Colorectal Dis 12:19–23CrossRef Dworak O, Keilholz L, Hoffmann A (1997) Pathological features of rectal cancer after preoperative radiochemotherapy. Int J Colorectal Dis 12:19–23CrossRef
22.
go back to reference Liu Y, Xu X, Yin L, Zhang X, Li L, Lu H (2017) Relationship between glioblastoma heterogeneity and survival time: an MR imaging texture analysis. AJNR Am J Neuroradiol 38:1695–1701CrossRef Liu Y, Xu X, Yin L, Zhang X, Li L, Lu H (2017) Relationship between glioblastoma heterogeneity and survival time: an MR imaging texture analysis. AJNR Am J Neuroradiol 38:1695–1701CrossRef
23.
go back to reference Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589CrossRef Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589CrossRef
24.
go back to reference Dasarathy BV, Holder EB (1991) Image characterizations based on joint gray level—run length distributions. Pattern Recogn Lett 12:497–502CrossRef Dasarathy BV, Holder EB (1991) Image characterizations based on joint gray level—run length distributions. Pattern Recogn Lett 12:497–502CrossRef
25.
go back to reference Nielsen B, Albregtsen F, Danielsen HE (2008) Statistical nuclear texture analysis in cancer research: a review of methods and applications. Crit Rev Oncog 14:89–164CrossRef Nielsen B, Albregtsen F, Danielsen HE (2008) Statistical nuclear texture analysis in cancer research: a review of methods and applications. Crit Rev Oncog 14:89–164CrossRef
26.
go back to reference Zlobec I, Gunthert U, Tornillo L et al (2009) Systematic assessment of the prognostic impact of membranous CD44v6 protein expression in colorectal cancer. Histopathology 55:564–575CrossRef Zlobec I, Gunthert U, Tornillo L et al (2009) Systematic assessment of the prognostic impact of membranous CD44v6 protein expression in colorectal cancer. Histopathology 55:564–575CrossRef
27.
go back to reference Gourtsoyianni S, Doumou G, Prezzi D et al (2017) Primary rectal cancer: repeatability of global and local-regional MR imaging texture features. Radiology 284:552–561CrossRef Gourtsoyianni S, Doumou G, Prezzi D et al (2017) Primary rectal cancer: repeatability of global and local-regional MR imaging texture features. Radiology 284:552–561CrossRef
28.
go back to reference Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400CrossRef Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400CrossRef
29.
go back to reference Sieren J, Smith A, Thiesse J et al (2011) Exploration of the volumetric composition of human lung cancer nodules in correlated histopathology and computed tomography. Lung Cancer 74:61–68CrossRef Sieren J, Smith A, Thiesse J et al (2011) Exploration of the volumetric composition of human lung cancer nodules in correlated histopathology and computed tomography. Lung Cancer 74:61–68CrossRef
30.
go back to reference Hocquelet A, Auriac T, Perier C et al (2018) Pre-treatment magnetic resonance-based texture features as potential imaging biomarkers for predicting event free survival in anal cancer treated by chemoradiotherapy. Eur Radiol 28:2801–2811CrossRef Hocquelet A, Auriac T, Perier C et al (2018) Pre-treatment magnetic resonance-based texture features as potential imaging biomarkers for predicting event free survival in anal cancer treated by chemoradiotherapy. Eur Radiol 28:2801–2811CrossRef
31.
go back to reference 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. Invest Radiol 50:239–245CrossRef 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. Invest Radiol 50:239–245CrossRef
32.
go back to reference Monguzzi L, Ippolito D, Bernasconi DP, Trattenero C, Galimberti S, Sironi S (2013) Locally advanced rectal cancer: value of ADC mapping in prediction of tumor response to radiochemotherapy. Eur J Radiol 82:234–240CrossRef Monguzzi L, Ippolito D, Bernasconi DP, Trattenero C, Galimberti S, Sironi S (2013) Locally advanced rectal cancer: value of ADC mapping in prediction of tumor response to radiochemotherapy. Eur J Radiol 82:234–240CrossRef
33.
go back to reference Punt CJ, Buyse M, Kohne CH et al (2007) Endpoints in adjuvant treatment trials: a systematic review of the literature in colon cancer and proposed definitions for future trials. J Natl Cancer Inst 99:998–1003CrossRef Punt CJ, Buyse M, Kohne CH et al (2007) Endpoints in adjuvant treatment trials: a systematic review of the literature in colon cancer and proposed definitions for future trials. J Natl Cancer Inst 99:998–1003CrossRef
34.
go back to reference Beets-Tan RG, Beets GL (2004) Rectal cancer: review with emphasis on MR imaging. Radiology 232:335–346CrossRef Beets-Tan RG, Beets GL (2004) Rectal cancer: review with emphasis on MR imaging. Radiology 232:335–346CrossRef
35.
go back to reference Zhang H, Hung CL, Min G, Guo JP, Liu M, Hu X (2019) GPU-accelerated GLRLM algorithm for feature extraction of MRI. Sci Rep 9:10883CrossRef Zhang H, Hung CL, Min G, Guo JP, Liu M, Hu X (2019) GPU-accelerated GLRLM algorithm for feature extraction of MRI. Sci Rep 9:10883CrossRef
36.
go back to reference Varghese BA, Cen SY, Hwang DH, Duddalwar VA (2019) Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol 212:520–528CrossRef Varghese BA, Cen SY, Hwang DH, Duddalwar VA (2019) Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol 212:520–528CrossRef
37.
go back to reference Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef
38.
go back to reference Mayerhoefer ME, Szomolanyi P, Jirak D, Materka A, Trattnig S (2009) Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study. Med Phys 36:1236–1243CrossRef Mayerhoefer ME, Szomolanyi P, Jirak D, Materka A, Trattnig S (2009) Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study. Med Phys 36:1236–1243CrossRef
Metadata
Title
MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer
Authors
Hayeong Park
Kyung Ah Kim
Ji-Han Jung
Jeongbae Rhie
Sun Young Choi
Publication date
01-08-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06835-4

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