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Objective: to determine the predicting factors for the effectiveness of neoadjuvant treatment in colorectal cancer based on the analysis of overall and relapse-free survival, as well as the possibility of multiparametric magnetic resonance imaging (MRI) in stratifying patients into groups with favorable and unfavorable clinical course.Materials and methods. 112 patients who received preoperative chemoradiotherapy (n = 85) and chemoradiotherapy supplemented with neoadjuvant polychemotherapy (n = 27) followed by surgery were enrolled in retrospective study. To determine the most significant predicting factors and criteria for evaluating the effectiveness of treatment that affect overall and relapse-free survival, Kaplan–Meier estimator and Cox regression were used.Results. The relapse-free survival was significantly affected by the presence or absence of extramural venous invasion according to MRI (mrEMVI) (p = 0.0001), circumferential resection margin status according to pathomorphological data (pCRM) (p = 0.031), change in volume of tumor (mrVolumetric analysis) (p = 0.015), tumor regression grade according to MRI (mrTRG) (p = 0.017) and pathomorphological data (pTRG) (p = 0.038). Independent predictors of overall survival were: extramural venous invasion according to MRI (mrEMVI) (p = 0.0001), posttreatment N staging (p = 0.047) and tumor regression grade according to MRI (mrTRG) (p = 0.059). Based on the most significant MR criteria, a mathematical model was developed to predict the risk of relapse after neoadjuvant treatment.Conclusions. MRI allows stratifying patients into groups with a favorable and unfavorable prognosis at the preoperative stage and optimizing the management of patients after surgery taking into account pathomorphological data.

In this study, the performance of a recently proposed computer-aided diagnosis (CAD) scheme in detection and 3D quantification of reticular and ground glass pattern extent in chest computed tomography of interstitial lung disease (ILD) patients is evaluated. CAD scheme performance was evaluated on a dataset of 37 volumetric chest scans, considering five representative axial anatomical levels per scan. CAD scheme reliability analysis was performed by estimating agreement (intraclass correlation coefficient, ICC) of automatically derived ILD pattern extent to semi-quantitative disease extent assessment in terms of 29-point rating scale provided by two expert radiologists. Receiver operating characteristic (ROC) analysis was employed to assess CAD scheme accuracy in ILD pattern detection in terms of area under ROC curve ( A). Correlation of reticular and ground glass volumetric pattern extent to pulmonary function tests (PFTs) was also investigated. CAD scheme reliability was substantial for ILD extent (ICC = 0.809) and distinct reticular pattern extent (0.806) and moderate for distinct ground glass pattern extent (0.543), performing within inter-observer agreement. CAD scheme demonstrated high accuracy in detecting total ILD ( A = 0.950 ± 0.018), while accuracy in detecting distinct reticular and ground glass patterns was 0.920 ± 0.023 and 0.883 ± 0.024, respectively. Moderate and statistically significant negative correlation was found between reticular volumetric pattern extent and diffusing capacity, forced expiratory volume in 1 s, forced vital capacity, and total lung capacity ( R = −0.581, −0.513, −0.494, and −0.446, respectively), similar to correlations found between radiologists' semi-quantitative ratings with PFTs. CAD-based quantification of disease extent is in agreement with radiologists' semi-quantitative assessment and correlates to specific PFTs, suggesting a potential imaging biomarker for ILD staging and management. [ABSTRACT FROM AUTHOR]