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• W2897901568 abstract "Quantitative analysis of tumor image features may predict treatment outcome and guide individualized treatment. This work aims 1) to evaluate the performance of quantitative imaging features extracted from pre-treatment MRI, as well as clinical characteristics, for prediction of post-treatment response to chemo-radiation treatment (CRT) in locally advanced rectal cancer (LARC); and 2) to identify plausible pre-treatment prognostic biomarkers that are best associated with post-CRT clinical outcome. Forty-five consecutive patients with LARC treated during 2015-2016 were included. Each patient received neo-adjuvant CRT with 50 Gy in 25 fractions, followed by total mesorectal excision surgery after completion of RT of 6-8 weeks. Other than planning CT, all patients underwent longitudinal pre-, during and post-RT MRI scans, including FRFSE T2 and diffusion weighted MRIs, etc. Image features were extracted from pre-treatment T2 images for the GTV, resulting in a total of 1838 quantitative features, along with clinical information (i.e., TNM staging etc.). According to clinical response assessed by post-operative pathology or MRI and colonoscopy, the patients were stratified into: (a) good responder group: pathological complete response (pCR) and partial response (PR); and (b) poor responder group: stable disease (SD) or progressive disease (PD). A novel nonlinear dimensionality reduction technique called T-distributed Stochastic Neighbor Embedding (T-SNE) was utilized to reduce high-dimensional tumor features, in which similar objects are modeled by nearby points and dissimilar objects are modeled by distant points, resulting a remaining of 182 features. Considering that the patient’s features are high dimensional and nonlinearly separable in the sample space, Gaussian kernel function was introduced to map the input space into a high-dimensional feature space. The features were then fed into the support vector machine model to create the predictive model in feature space. Five-fold cross validation method was utilized to train and validate the model. The model performance was evaluated using receiver operating characteristic (ROC) curve. We further identified the most important features (top 5%) that may affect prognosis. There were 54.7% of patients responded to the CRT: pCR (21.4%) or PR (33.3%), while 45.3% of patients achieved poor response, SD (33.4%) or PD (11.9%). Pre-treatment MRI features predicted post-CRT response with a calculated AUC = 0.82. Nine patient specific features, i.e., shape/roundness, morphology/GLCM texture, intensity, as well as clinical N stage, were identified as predominant predictors of response for LARC. Pre-treatment image features, in combination of patient characteristics, could be used for post-treatment outcome prediction for LARC. Systematic analysis of image features via longitudinal multi-parametric MRIs should lead to improved predictive value to guide personalized treatment." @default.
• W2897901568 created "2018-10-26" @default.
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• W2897901568 date "2018-11-01" @default.
• W2897901568 modified "2023-09-27" @default.
• W2897901568 title "Pre-Treatment Magnetic Resonance Imaging Features Predict Post Chemo-Radiation Clinical Outcome for Patients with Local Advanced Rectal Cancer" @default.
• W2897901568 doi "https://doi.org/10.1016/j.ijrobp.2018.06.284" @default.
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