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Abstract Background Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive tool for evaluating the degree of CIRI. Multi-parametric MRI has been widely used to detect and evaluate kidney injury. The machine learning algorithms introduced the opportunity to combine biomarkers from different MRI metrics into a single classifier. Objective To evaluate the performance of multi-parametric magnetic resonance imaging for grading renal injury in a rat model of renal cold ischemia-reperfusion injury using a machine learning approach. Methods Eighty male SD rats were selected to establish a renal cold ischemia -reperfusion model, and all performed multiparametric MRI scans (DWI, IVIM, DKI, BOLD, T1mapping and ASL), followed by pathological analysis. A total of 25 parameters of renal cortex and medulla were analyzed as features. The pathology scores were divided into 3 groups using K-means clustering method. Lasso regression was applied for the initial selecting of features. The optimal features and the best techniques for pathological grading were obtained. Multiple classifiers were used to construct models to evaluate the predictive value for pathology grading. Results All rats were categorized into mild, moderate, and severe injury group according the pathologic scores. The 8 features that correlated better with the pathologic classification were medullary and cortical Dp, cortical T2*, cortical Fp, medullary T2*, ∆T1, cortical RBF, medullary T1. The accuracy(0.83, 0.850, 0.81, respectively) and AUC (0.95, 0.93, 0.90, respectively) for pathologic classification of the logistic regression, SVM, and RF are significantly higher than other classifiers. For the logistic model and combining logistic, RF and SVM model of different techniques for pathology grading, the stable and perform are both well. Based on logistic regression, IVIM has the highest AUC (0.93) for pathological grading, followed by BOLD(0.90). Conclusion The multi-parametric MRI-based machine learning model could be valuable for noninvasive assessment of the degree of renal injury.

Abstract Background T1 mapping can potentially quantitatively assess the intrinsic properties of tumors. This study was conducted to explore the ability of T1 mapping in distinguishing cervical cancer type, grade, and stage and compare the diagnostic performance of T1 mapping with diffusion kurtosis imaging (DKI). Methods One hundred fifty-seven patients with pathologically confirmed cervical cancer were enrolled in this prospectively study. T1 mapping and DKI were performed. The native T1, difference between native and postcontrast T1 (T1diff), mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were calculated. Cervical squamous cell carcinoma (CSCC) and adenocarcinoma (CAC), low- and high-grade carcinomas, and early- and advanced-stage groups were compared using area under the receiver operating characteristic (AUROC) curves. Results The native T1 and MK were higher, and the MD and ADC were lower for CSCC than for CAC (all p

Abstract Background This study seeks to assess the utility of T1 and T2 mapping in distinguishing metastatic lymph nodes from reactive lymphadenopathy in patients with head and neck squamous cell carcinoma (HNSCC), using diffusion-weighted imaging (DWI) as a comparison. Methods Between July 2017 and November 2019, 46 HNSCC patients underwent neck MRI inclusive of T1 and T2 mapping and DWI. Quantitative measurements derived from preoperative T1 and T2 mapping and DWI of metastatic and non-metastatic lymph nodes were compared using independent samples t-test or Mann–Whitney U test. Receiver operating characteristic curves and the DeLong test were employed to determine the most effective diagnostic methodology. Results We examined a total of 122 lymph nodes, 45 (36.9%) of which were metastatic proven by pathology. Mean T2 values for metastatic lymph nodes were significantly lower than those for benign lymph nodes (p

Abstract Background We aimed to evaluate the correlation between the pathological changes and multi-parameter MRI characteristics of liver regeneration (LR) in a standard partial hepatectomy (PH) rat model. Methods Seventy Sprague–Dawley rats were randomly divided into two groups: MR scan group (n = 14) and pathologic analysis (PA) group (n = 56). All 14 rats in the MR group underwent liver T1 mapping, T2 mapping, and diffusion kurtosis imaging before and the 1st, 2nd, 3rd, 5th, 7th, 14th, and 21st day after 70% hepatectomy. Seven rats in the PA group were euthanized at each time point to determine Ki-67 indices, hepatocyte size (HTS), steatosis grade, and inflammation score. Results Liver T1 and T2 values increased to maximum on day 2 (P

Abstract Background Previous studies have indicated that T1 relaxation time could be utilized for the analysis of tissue characteristics. T1 mapping technology has been gradually used on research of body tumor. In this study, the application of native T1 relaxation time for differentiating the histopathologic type, grade, and stage of rectal adenocarcinoma was investigated. Methods One hundred and twenty patients with pathologically confirmed rectal adenocarcinoma were retrospectively evaluated. All patients underwent high-resolution anatomical magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and T1 mapping sequences. Parameters of T1 relaxation time and apparent diffusion coefficient (ADC) were measured between the different groups. The diagnostic power was evaluated though the receiver operating characteristic (ROC) curve. Results The T1 and ADC values varied significantly between rectal mucinous adenocarcinoma (MC) and non-mucinous rectal adenocarcinoma (AC) ([1986.1 ± 163.3 ms] vs. [1562.3 ± 244.2 ms] and [1.38 ± 0.23 × 10−3mm2/s] vs. [1.03 ± 0.15 × 10−3mm2/s], respectively; P 0.05). No significant differences in T1 and ADC values were identified between the different T and N stage groups for both MC and AC (all P > 0.05). Conclusions Native T1 relaxation time can be used to discriminate MC from AC. The T1 relaxation time was helpful for differentiating the low- and high-grade of AC.

Abstract Purpose To compare integrated slice-specific dynamic shimming (iShim) diffusion weighted imaging (DWI) and single-shot echo-planar imaging (SS-EPI) DWI in image quality and pathological characterization of rectal cancer. Materials and methods A total of 193 consecutive rectal tumor patients were enrolled for retrospective analysis. Among them, 101 patients underwent iShim-DWI (b = 0, 800, and 1600 s/mm2) and 92 patients underwent SS-EPI-DWI (b = 0, and 1000 s/mm2). Qualitative analyses of both DWI techniques was performed by two independent readers; including adequate fat suppression, the presence of artifacts and image quality. Quantitative analysis was performed by calculating standard deviation (SD) of the gluteus maximus, signal intensity (SI) of lesion and residual normal rectal wall, apparent diffusion coefficient (ADC) values (generated by b values of 0, 800 and 1600 s/mm2 for iShim-DWI, and by b values of 0 and 1000 s/mm2 for SS-EPI-DWI) and image quality parameters, such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of primary rectal tumor. For the primary rectal cancer, two pathological groups were divided according to pathological results: Group 1 (well-differentiated) and Group 2 (poorly differentiated). Statistical analyses were performed with p