Are ADC values of readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) correlated with pathological prognostic factors in rectal adenocarcinoma?

BACKGROUND: Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values as imaging biomarkers of rectal cancer are currently a hot research spot. The use of ADC values for preoperative judgment of pathological features in rectal cancer has been generally accepted. The image quality evaluation of conventional diffusion is severe deformation, and the measurement of ADC values can easily lead to bias. Readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) provides high signal-to-noise ratio images and significantly reduces distortions caused by magnetosensitive effects. The purpose of this study was to explore the correlations between ADC values of RESOLVE and pathological prognostic factors in rectal adenocarcinoma. METHODS: We collected pathological data of 89 patients with pathologically confirmed rectal adenocarcinoma who directly underwent surgical resection without receiving adjuvant therapy. The patients were grouped according to the pathologic type, gross classification, degree of differentiation, TN stage, and immunohistochemical expression of epidermal growth factor receptor (EGFR). RESULTS: RESOLVE ADC values of rectal cancer were measured at b = 800, and correlations between the RESOLVE ADC values obtained in different groups were analysed. We found that RESOLVE ADC values in the ulcer-type group were significantly higher than those in the eminence-type group. CONCLUSION: RESOLVE ADC values in different pathologic types of rectal cancer were significantly different. RESOLVE ADC values in the EGFR-positive group were significantly lower than those in the EGFR-negative group. There was no significant difference in RESOLVE ADC values between different degrees of pathologic differentiation, TN stages, and positive or negative lymph nodes. The quantitative description of RESOLVE ADC values could be used to assess the biological behaviour of rectal adenocarcinoma.

Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach.

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.