Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients

Shayesteh, S.P. and Alikhassi, A. and Farhan, F. and Gahletaki, R. and Soltanabadi, M. and Haddad, P. and Bitarafan-Rajabi, A. (2020) Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients. Journal of Gastrointestinal Cancer, 51 (2). pp. 601-609.


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Introduction: 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. Methods: 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. Result: 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 � = 1 preprocessing LoG filter. In T2W MR Images, the best performance was for naive Bayesian network classifier (AUC = 85.1) with a � = 0.5 preprocessing LoG filter. Also, performance of machine learning models with CfsSubsetEval (CF SUB E) feature selection algorithm was better than others. Conclusion: 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. © 2019, The Author(s).

Item Type: Article
Additional Information: cited By 1
Uncontrolled Keywords: antineoplastic agent, adjuvant chemoradiotherapy; adult; aged; Article; cancer grading; cancer patient; cancer prognosis; classification algorithm; female; human; image processing; major clinical study; male; neoadjuvant therapy; nuclear magnetic resonance imaging; prediction; priority journal; rectum cancer; retrospective study; software; texture feature extraction
Subjects: WI Digestive System
QZ Pathology
Depositing User: eprints admin
Date Deposited: 19 Sep 2020 09:12
Last Modified: 19 Sep 2020 09:12

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