Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients
Tóm tắt
Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection is the standard treatment for locally advanced rectal cancer (LARC). Radiomics can be used as noninvasive biomarker for prediction of response to therapy. The main aim of this study was to evaluate the association of MRI texture features of LARC with nCRT response and the effect of Laplacian of Gaussian (LoG) filter and feature selection algorithm in prediction process improvement. All patients underwent MRI with a 3T clinical scanner, 1 week before nCRT. For each patient, intensity, shape, and texture-based features were derived from MRI images with LoG filter using the IBEX software and without preprocessing. We identified responder from a non-responder group using 9 machine learning classifiers. Then, the effect of preprocessing LoG filters with 0.5, 1 and 1.5 value on these classification algorithms’ performance was investigated. Eventually, classification algorithm’s results were compared in different feature selection methods. Sixty-seven patients with LARC were included in the study. Patients’ nCRT responses included 11 patients with Grade 0, 19 with Grade 1, 26 with Grade 2, and 11 with Grade 3 according to AJCC/CAP pathologic grading. In MR Images which were not preprocessed, the best performance was for Ada boost classifier (AUC = 74.8) with T2W MR Images. In T1W MR Images, the best performance was for aba boost classifier (AUC = 78.1) with a σ = 1 preprocessing LoG filter. In T2W MR Images, the best performance was for naive Bayesian network classifier (AUC = 85.1) with a σ = 0.5 preprocessing LoG filter. Also, performance of machine learning models with CfsSubsetEval (CF SUB E) feature selection algorithm was better than others. Machine learning can be used as a response predictor model in LARC patients, but its performance should be improved. A preprocessing LoG filter can improve the machine learning methods performance and at the end, the effect of feature selection algorithm on model’s performance is clear.
Tài liệu tham khảo
Birkman EM, et al. Protein phosphatase 2A (PP2A) inhibitor CIP2A indicates resistance to radiotherapy in rectal cancer. Cancer Med. 2018.
Cossiolo DC, et al. Polymorphism of the Cox-2 gene and susceptibility to colon and rectal cancer. Arq Bras Cir Dig. 2017;30(2):114–7.
Hathout L, Williams TM, Jabbour SK. The impact of novel radiation treatment techniques on toxicity and clinical outcomes in rectal cancer. Curr Colorectal Cancer Rep. 2017;13(1):61–72.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66(1):7–30.
Martinez-Useros J, Moreno I, Fernandez-Aceñero MJ, Rodriguez-Remirez M, Borrero-Palacios A, Cebrian A, et al. The potential predictive value of DEK expression for neoadjuvant chemoradiotherapy response in locally advanced rectal cancer. BMC Cancer. 2018;18(1):144.
Zhang C, et al. Morphologic predictors of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Oncotarget. 2018;9(4):4862–74.
Sathyakumar K, Chandramohan A, Masih D, Jesudasan MR, Pulimood A, Eapen A. Best MRI predictors of complete response to neoadjuvant chemoradiation in locally advanced rectal cancer. Br J Radiol. 2016;89(1060):20150328.
Noh GT, Kim NK. Genomic predictor of complete response after chemoradiotherapy in rectal cancer. Ann Transl Med. 2016;4(24):493.
Chee CG, Kim YH, Lee KH, Lee YJ, Park JH, Lee HS, et al. CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: a potential imaging biomarker for treatment response and prognosis. PLoS One. 2017;12(8):e0182883.
Sun Y-S, et al. Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: preliminary analysis of diffusion-weighted MR imaging for early detection of tumor histopathologic downstaging. Radiology. 2009;254(1):170–8.
Anand A, Tripathy SS, Kumar RS. An improved edge detection using morphological Laplacian of Gaussian operator. In Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on. 2015. IEEE.
Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40(1):133–40.
Aerts HJ, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
Sacconi B, Anzidei M, Leonardi A, Boni F, Saba L, Scipione R, et al. Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates. Clin Radiol. 2017;72(6):443–50.
Nie K, Chen JH, Yu HJ, Chu Y, Nalcioglu O, Su MY. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol. 2008;15(12):1513–25.
Ailianou A, Mundada P, de Perrot T, Pusztaszieri M, Poletti PA, Becker M. MRI with DWI for the detection of posttreatment head and neck squamous cell carcinoma: why morphologic MRI criteria matter. Am J Neuroradiol. 2018;39:748–55.
Schieda N, Lim CS, Idris M, Lim RS, Morash C, Breau RH, et al. MRI assessment of pathological stage and surgical margins in anterior prostate cancer (APC) using subjective and quantitative analysis. J Magn Reson Imaging. 2017;45(5):1296–303.
Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23(19):2507–17.
Parmar C, et al. Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol. 2015;5:272.
Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Informat. 2006;2:117693510600200030.
Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: a review of classification techniques. Emerging artificial intelligence applications in computer engineering. 2007;160:3–24.
Molina D, Pérez-Beteta J, Martínez-González A, Martino J, Velásquez C, Arana E, et al. Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images. Comput Biol Med. 2016;78:49–57.
Vallières M, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60(14):5471–96.
Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266(1):177–84.
Haddad P, Miraie M, Farhan F, Fazeli MS, Alikhassi A, Maddah-Safaei A, et al. Addition of oxaliplatin to neoadjuvant radiochemotherapy in MRI-defined T3, T4 or N+ rectal cancer: a randomized clinical trial. Asia-Pacific Journal of Clinical Oncology. 2017;13(6):416–22.
Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17(6):1471–4.
Nougaret S, Fujii S, Addley HC, Bibeau F, Pandey H, Mikhael H, et al. Neoadjuvant chemotherapy evaluation by MRI volumetry in rectal cancer followed by chemoradiation and total mesorectal excision: initial experience. J Magn Reson Imaging. 2013;38(3):726–32.
Singhal S, Jena M. A study on WEKA tool for data preprocessing, classification and clustering. Int J Innov Technol Exploring Eng. 2013;2(6):250–3.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2015;278(2):563–77.
Moffat BA, Chenevert TL, Lawrence TS, Meyer CR, Johnson TD, Dong Q, et al. Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci U S A. 2005;102(15):5524–9.
Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015;5:13087.
Davnall F, Yip CSP, 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(6):573–89.
De Cecco CN, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Investig Radiol. 2015;50(4):239–45.
Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res. 2016;22(21):5256–64.
Meng Y, et al. MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget. 2018;9(15):11999.
Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology. 2018;287(3):833–43.
Dinapoli N, et al. Magnetic resonance, vendor-independent, intensity histogram analysis predicting pathologic complete response after radiochemotherapy of rectal cancer. Int J Radiat Oncol Biol Phys. 2018.
Megherbi D, Soper B. Effect of feature selection on machine learning algorithms for more accurate predictor of surgical outcomes in benign pro static hyperplasia cases (BPH). In Computational Intelligence for Measurement Systems and Applications (CIMSA), 2011 IEEE International Conference on. 2011. IEEE.