To develop a radiomics signature based on preoperative MRI to estimate disease-free survival (DFS) in patients with invasive breast cancer and to establish a radiomics nomogram that incorporates the radiomics signature and MRI and clinicopathological findings. We identified 294 patients with invasive breast cancer who underwent preoperative MRI. Patients were randomly divided into training ( = 194) and validation ( = 100) sets. A radiomics signature (Rad-score) was generated using an elastic net in the training set, and the cutoff point of the radiomics signature to divide the patients into high- and low-risk groups was determined using receiver-operating characteristic curve analysis. Univariate and multivariate Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of the radiomics signature, MRI findings, and clinicopathological variables with DFS. A radiomics nomogram combining the Rad-score and MRI and clinicopathological findings was constructed to validate the radiomic signatures for individualized DFS estimation. Higher Rad-scores were significantly associated with worse DFS in both the training and validation sets ( = 0.002 and 0.036, respectively). The radiomics nomogram estimated DFS [C-index, 0.76; 95% confidence interval (CI); 0.74-0.77] better than the clinicopathological (C-index, 0.72; 95% CI, 0.70-0.74) or Rad-score-only nomograms (C-index, 0.67; 95% CI, 0.65-0.69). The radiomics signature is an independent biomarker for the estimation of DFS in patients with invasive breast cancer. Combining the radiomics nomogram improved individualized DFS estimation. .

BackgroundGrading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading.MethodsWe considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort.ResultsFive significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts.DiscussionGlioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas.

Radiomics features derived from multi-parametric MRI can be used to differentiate PCNSL from glioblastoma effectively.

Background and purpose The progression pattern of brain structural changes in patients with isolated cerebrovascular disease (CVD) remains unclear. To investigate the role of isolated CVD in cognitive impairment patients, patterns of cortical thinning and hippocampal atrophy in pure subcortical vascular mild cognitive impairment (svMCI) and pure subcortical vascular dementia (SVaD) patients were characterized. Methods Forty-five patients with svMCI and 46 patients with SVaD who were negative on Pittsburgh compound B (PiB) positron emission tomography imaging and 75 individuals with normal cognition (NC) were recruited. Results Compared with NC, patients with PiB(−) svMCI exhibited frontal, language and retrieval type memory dysfunctions, which in patients with PiB(−) SVaD were further impaired and accompanied by visuospatial and recognition memory dysfunctions. Compared with NC, patients with PiB(−) svMCI exhibited cortical thinning in the frontal, perisylvian, basal temporal and posterior cingulate regions. This atrophy was more prominent and extended further toward the lateral parietal and medial temporal regions in patients with PiB(−) SVaD. Compared with NC subjects, patients with PiB(−) svMCI exhibited hippocampal shape deformities in the lateral body, whilst patients with PiB(−) SVaD exhibited additional deformities within the lateral head and inferior body. Conclusions Our findings suggest that patients with CVD in the absence of Alzheimer’s disease pathology can be demented, showing cognitive impairment in multiple domains, which is consistent with the topography of cortical thinning and hippocampal shape deformity.