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The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from 479 breast cancer patients with 488 lesions were studied. The included patients were divided into two cohorts by time (training/testing cohort, n = 366/122). Deep learning features were extracted from diffusion-weighted imaging–quantitatively measured apparent diffusion coefficient (DWI-ADC) imaging and dynamic contrast-enhanced MRI (DCE-MRI) by a pretrained neural network of DenseNet121. After the selection of both radiomic and clinicopathological features, deep learning signature and a nomogram were built for independent validation. Twenty-three deep learning features were automatically selected in the training cohort to establish the deep learning signature of ALNM. Three clinicopathological factors, including LN palpability (odds ratio (OR) = 6.04; 95% confidence interval (CI) = 3.06–12.54, P = 0.004), tumor size in MRI (OR = 1.45, 95% CI = 1.18–1.80, P = 0.104), and Ki-67 (OR = 1.01; 95% CI = 1.00–1.02, P = 0.099), were selected and combined with radiomic signature to build a combined nomogram. The nomogram showed excellent predictive ability for ALNM (AUC 0.80 and 0.71 in training and testing cohorts, respectively). The sensitivity, specificity, and accuracy were 65%, 80%, and 75%, respectively, in the testing cohort. MRI-based deep learning radiomics in patients with breast cancer could be used to predict ALNM, providing a noninvasive approach to structuring the treatment strategy. [ABSTRACT FROM AUTHOR]

Background: Various features extracted from breast MRI have the potential to serve as noninvasive biomarkers for the prediction of the biologic behavior of breast cancer. The purpose of this study was to investigate the value of focal breast edema and breast edema score (BES) on T2-weighted images in providing valuable biological information for breast cancer patients' personalized treatment. Method: Two hundred and five lesions in 201 patients with invasive breast cancer confirmed by surgery or biopsy in Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine from November 2018 to October 2019 were retrospectively recruited and analyzed in this study. Focal edema and BES were evaluated at fat-suppressed T2 weighted imaging. All the lesions were divided into two groups according to the presence of focal edema. The differences in clinicopathological characteristics between the two groups and between different BES were compared. Results: Two hundred and five lesions in 201 patients with invasive breast cancer were retrospectively recruited and analyzed in this study. On the fat-suppressed T2WI, focal edema was detected in 102 of 205 lesions (49.8%). BES was positively correlated with tumor size (p < 0.001), histologic grade (p = 0.006), Ki-67 index (p < 0.001), and N stage (p = 0.007), and was negatively correlated with expression of ER and PR (p < 0.001). Higher BES was more likely to present in patients with non-luminal breast cancer (p < 0.001) and suggested the possibility of a higher N stage. Conclusions: Focal edema on T2WI of breast MRI indicates stronger tumor invasiveness, in which non-luminal breast cancer is more inclined to present focal edema. Breast edema score, a novel and practical tool, helps guide the individualized treatment of patients with invasive breast cancer. Critical relevance statement: Focal edema on T2WI of breast MRI indicates stronger tumor invasiveness. Breast edema score helps guide the individualized treatment of patients with invasive breast cancer. Key points: Focal edema on T2WI of breast MRI indicates stronger tumor invasiveness. Non-luminal breast cancer is more inclined to present focal edema. Breast edema score is a novel and practical tool to guide therapy decisions. [ABSTRACT FROM AUTHOR]

Background: Heterozygous mutations in HTRA1 were recently found to cause autosomal dominant cerebral small vessel disease (CSVD), and it was named HTRA1-autosomal dominant disease (AD-HTRA1) in the consensus recommendations of the European Academy of Neurology. This study aimed to investigate the clinical features of a mutation in HTRA1 and the effect of HTRA1 mutation on white matter hyperintensity (WMH). Methods: A proband's brain magnetic resonance imaging (MRI) showed multiple lacunar infarctions and multiple WMH in the lateral ventricle, external capsule, frontal lobe and corpus callosum. The proband and family members were tested for CSVD-related genes by next-generation sequencing and the clinical data of the patients were collected. The published literature on AD-HTRA1 was collected, and the clinical characteristics and pathogenicity of the patients were summarized. Combined Annotation Dependent Depletion (CADD) is a tool for scoring the deleteriousness of single-nucleotide variants and insertion/deletion variants in the human genome. The relationship between the degree of WMH and the pathogenicity of the mutation was further analyzed. Result: It was found that the proband and her family members had a heterozygous missense mutation of c.854C > T (p.P285L) in the 4 exon of HTRA1 gene. A retrospective analysis of 5 families with c.854C > T mutation found that the patients had an early age of onset, cognitive impairment was more common, and alopecia and spondylosis could be combined at the same time. By univariate analysis, the severity of WMH was found to be significantly associated with the mutated CADD score (p < 0.05, Spearman's rho = 0.266). Conclusion: The clinical manifestations of AD-HTRA1 with mutation site c.854C > T (p.P285L) are similar to CARASIL, and brain MRI are mainly moderate or severe WMH and lacunar infarction (LI). WMH are affected by mutation sites. Therefore, our pathogenicity score for mutations can predict the severity of WMH. [ABSTRACT FROM AUTHOR]

Objectives: To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images. Methods: This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions). The 211 NME lesions acquired by another MR scanner were used as test set B. The AI system was developed using ResNet-50 with the axial and sagittal MIP images. One senior and one junior radiologist reviewed the MIP images of each case independently and rated its Breast Imaging Reporting and Data System category. The performance of the AI system and the radiologists was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The AI system yielded AUCs of 0.859 and 0.816 in the test sets A and B, respectively. The AI system achieved comparable performance as the senior radiologist (p = 0.558, p = 0.041) and outperformed the junior radiologist (p < 0.001, p = 0.009) in both test sets A and B. After AI assistance, the AUC of the junior radiologist increased from 0.740 to 0.862 in test set A (p < 0.001) and from 0.732 to 0.843 in test set B (p < 0.001). Conclusion: Our MIP-based AI system yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance. Key Points: • Our MIP-based AI system yielded good applicability in the dataset both from the same and a different MR scanner in predicting malignant NME lesions. • The AI system achieved comparable diagnostic performance with the senior radiologist and outperformed the junior radiologist. • This AI system can assist the junior radiologist achieve better performance in the classification of NME lesions in MRI. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the value of MRI radiomics based on T2-weighted (T2W) images in predicting preoperative synchronous distant metastasis (SDM) in patients with rectal cancer.Methods: This retrospective study enrolled 177 patients with histopathology-confirmed rectal adenocarcinoma (123 patients in the training cohort and 54 in the validation cohort). A total of 385 radiomics features were extracted from pretreatment T2W images. Five steps, including univariate statistical tests and a random forest algorithm, were performed to select the best preforming features for predicting SDM. Multivariate logistic regression analysis was conducted to build the clinical and clinical-radiomics combined models in the training cohort. The predictive performance was validated by receiver operating characteristics curve (ROC) analysis and clinical utility implementing a nomogram and decision curve analysis.Results: Fifty-nine patients (33.3%) were confirmed to have SDM. Six radiomics features and four clinical characteristics were selected for predicting SDM. The clinical-radiomics combined model performed better than the clinical model in both the training and validation datasets. A threshold of 0.44 yielded an area under the ROC (AUC) value of 0.827 (95% confidence interval (CI), 0.6963-0.9580), a sensitivity of 72.2%, a specificity of 94.4%, and an accuracy of 87.0% in the validation cohort for the combined model. A clinical-radiomics nomogram and decision curve analysis confirmed the clinical utility of the combined model.Conclusions: Our proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM, which could aid in tailoring treatment strategies.Key Points: • T2WI-based radiomics analysis helps predict synchronous distant metastasis (SDM) of rectal cancer. • The clinical-radiomics combined model could be utilized as a noninvasive biomarker for predicting SDM. • Personalized treatment can be carried out with greater confidence based on the risk stratification for SDM in rectal cancer. [ABSTRACT FROM AUTHOR]