Objective To investigate the value of T2WI-based radiomics signatures for preoperatively prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer. Methods T2WI of 209 patients with breast cancer were retrospectively analyzed. The patients were randomly divided into training group (n=145) and validation group (n=64). The ROIs were manually delineated around the tumor profile. Radiomics feature extraction was implemented in MATLAB 2013a. The interclass correlation coefficients, the least absolute shrinkage and selection operator Logistic regression analysis were used for radiomics features selection and generation. The difference of the Rad-score between HER2-positive and HER2- negative subgroups was observed. The predictive performances of the radiomics signatures for HER2 status were evaluated with ROC curves in training group, and were validated in validation group with the obtained predictive threshold. Results The radiomics signatures were constituted by 13 selective features. In both training and validation groups, there were statistically significant differences in Rad-score between HER2-positive subgroup and HER2-negative subgroup (both P<0.05). T2WI-based radiomics signatures exhibited good discrimination for HER2 status, with AUC of 0.798 in training group and 0.707 in validation group. Conclusion The radiomics signatures based on T2WI have certain value for preoperative prediction of HER2 status in breast cancer.

Objective To investigate the effect of image registration on quantitative measurements of free breathing diffusion kurtosis imaging (DKI) in normal human kidney. Methods Twenty healthy volunteers were prospectively enrolled to undergo DKI imaging with a 3.0 T MR scanner. Three b values (0, 500, and 1 000 s/mm2) were adopted,with image registration performed after image acquisition. Acquired images were fitted using the DKI fitting model to generate the DKI metric maps,which were performed on both the pre-registration images and post-registration images. Image quality of the derived metric maps (before and after image registration,respectively) was assessed by two radiologists. Measurements of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (D|), axial diffusivity (D⊥), mean kurtosis (MK), radial kurtosis (K|) and axial kurtosis (K⊥) were conducted. The inter-observer reproducibility of the image quality assessment was analyzed using intra-class correlation coefficient(ICC). Wilcoxon signed-rank test was used to evaluate the difference in the subjective scores of the metric maps between those obtained before registration and those after registration. While paired t test or Wilcoxon signed-rank test was performed to analyze the difference in the quantitative measurements of DKI metrics of the renal cortex and medulla between those obtained before registration and those after registration.Results For the inter-observer reproducibility, satisfactory ICCs were obtained for the quantitative metric measurements (pre-registration:0.784 to 0.821;post-registration:0.836 to 0.934). Significant difference was notice between subjective scores for the quality of metric maps (P<0.05 for each comparison). In both the renal cortex and medulla, significant difference was noticed between each metric value obtained with pre-registration images and that with post-registration images (P<0.05 for each comparison). Conclusion Image registration can not only offer higher quality DKI metric maps,but also has effect on the quantitative measurements of obtained metric maps.

Purpose Lymph-vascular invasion (LVI) is a risk factor for the prognosis of colorectal cancer, and it is of great value to evaluate the status of lymphatic vessels before treatment. This study aims to predict colorectal cancer LVI preoperatively based on radiomics. Materials and Methods Radiomics features were extracted from preoperative CT images of colorectal cancer retrospectively collected and radiomics labels were constructed. The predictive efficacy of radiomics labels were assessed and internally verified. Joint predictive factors were established by combining clinical factors with independent predictive efficacy and radiomics labels, and their predictive efficacy was evaluated. Results Radiomics labels consisted of 58 radiomics features were correlated with LVI status (P<0.0001)with the former showing good discrimination ability[C-index 0.719,95% CI:0.715-0.723]and classification ability(sensitivity 0.726, specificity 0.628) with internal validation (C-index 0.720). Joint predictive factors containing radiomics labels and carcino-embryonic antigen further enhanced the predictability of radiomics labels (C-index 0.751, sensitivity 0.788, specificity 0.667). Conclusion The radiomics labels built in this study can provide individualized prediction of LVI status of patients with colorectal cancer before surgery. Joint predictive factors in combination with clinical risk factors further improved predictive efficacy.

Objective To compare the diagnostic value of transvaginal ultrasound(TVS)and thin-section oblique axial T2WI in deep infiltrating endometriosis(DIE)of uterosacral ligaments(USL).Methods Retrospective analysis was conducted on preoperative TVS and MRI images from 74 patients with USL-DIE.The diagnostic efficacy of TVS and thin-section oblique axial T2WI for USL-DIE was evaluated using receiver operating characteristic(ROC)curve based on pathological findings,and area under the curve(AUC),sensitivity,specificity,accuracy,positive predictive value(PPV)and negative predictive value(NPV)were further calculated.The McNemar two-tailed test was used to analyze the difference in diagnostic accuracy between the two methods for USL-DIE.Results The AUCs of TVS and thin-section oblique axial T2WI for diagnosing left and right USL-DIE were 0.721 vs 0.842 and 0.640 vs 0.901,respectively.Thin-section oblique axial T2WI demonstrated higher sensitivity,accuracy,PPV,and NPV compared to TVS in diagnosing USL-DIE.The diagnostic accuracy was found to be statistically different between the two methods(Pleft =0.026,χ2=4.966;Pright<0.001,χ2=16.531).Conclusion Thin-section oblique axial T2WI outperformed TVS in terms of diagnostic value for USL-DIE,providing valuable imaging support for accurate clinical diagnosis.

Objective To develop and validate a CT-based radiomics predictive model for preoperative predicting the stage of non-small cell lung cancer (NSCLC). Methods In this retrospective study, 657 patients with histologically confirmed was collected from October 2007 to December 2014.The primary dataset consisted of patients with histologically confirmed NSCLC from October 2007 to April 2012, while independent validation was conducted from May 2012 to December 2014.All the patients underwent non-enhanced and contrast-enhanced CT images scan with a standard protocol. The pathological stage (PTNM) of patients with NSCLC were determined by the intraoperative and postoperative pathological findings,and were divided into early stage(Ⅰ,Ⅱstage)and advanced stage(Ⅲ,Ⅳstage).A list of radiomics features were extracted using the software Matlab 2014a and the corresponding radiomics signature was constructed. Multivariable logistic regression analysis was performed with radiomics signature and clinical variables for developing the prediction model. The model performance was assessed with respect to discrimination using the area under the curve (AUC) of receiver operating characteristic(ROC) analysis. Results The discrimination performance of radiomics signature yielded a AUC of 0.715[95% confidence interval (CI):0.709 to 0.721] in the primary dataset and a AUC of 0.724(95% CI:0.717 to 0.731) in the validation dataset. On multivariable logistic regression, radiomics signature, tumor diameter, carcinoembryonic antigen (CEA) level, and cytokeratin 19 fragment (CYFRA21-1) level were showed independently associated with the stage ( Ⅰ,Ⅱ stage vs. Ⅲ, Ⅳ stage) of NSCLC. The prediction model showed good discrimination in both primary dataset (AUC=0.787, 95%CI:0.781 to 0.793;sensitivity=73.4%, specificity=72.2% ,positive predictive value=0.707,negative predictive value=0.868) and independent validation dataset (AUC=0.777, 95% CI:0.771 to 0.783,sensitivity=91.3% ,specificity=67.3% ,positive predictive value=0.607, negative predictive value=0.946). Conclusion The radiomics predictive model, which integrated with the radiomics signature and clinical characteristics can be used as a promising and applicable adjunct approach for preoperatively predicting the clinical stage (Ⅰ,Ⅱ stage vs. Ⅲ,Ⅳ stage) of patients with NSCLC.

To develop and validate a fat-suppressed (T2 weighted-magnetic resonance imaging, T2W-MRI) based radiomics signature to preoperatively evaluate the histologic grade (grade I/II VS. grade III) of invasive breast cancer.
 Methods: A total of 202 patients with MRI examination and pathologically confirmed invasive breast cancer from June 2011 to February 2017 were retrospectively enrolled. After retrieving fat-suppressed T2W images and tumor segmentation, radiomics features were extracted and valuable features were selected to build a radiomic signature with the least absolute shrinkage and selection operator (LASSO) method. Mann-Whitney U test was used to explore the correlation between radiomics signature and histologic grade. Receiver operating characteristics (ROC) curve was applied to determine the discriminative performance of the radiomics signature [area under curre (AUC), sensitivity, specificity, and accuracy]. An independent validation dataset was used to confirm the discriminatory power of radiomics signature. 
 Results: Eight radiomics features were selected to build a radiomics signature, which showed good performance for preoperatively evaluating histologic grade of invasive breast cancer, with an AUC of 0.802 (95% CI 0.729 to 0.875), sensitivity of 78.7%, specificity of 70.3% and accuracy of 73.7% in training dataset and AUC of 0.812 (95% CI 0.686 to 0.938), sensitivity of 80.0%, specificity of 73.3% and accuracy of 76.0% in the validation dataset.
 Conclusion: The fat-suppressed T2W-MRI based radiomics signature can be used to preoperatively evaluate the histologic grade of invasive breast cancer, which may assist clinical decision-maker.
Breast Neoplasms ; diagnostic imaging ; Humans ; Magnetic Resonance Imaging ; Preoperative Care ; ROC Curve ; Retrospective Studies