Predictive value of triple negative breast cancer based on DCE-MRI multi-phase full-volume ROI clinical radiomics model.

BACKGROUND: Since no studies compared the value of radiomics features of distinct phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting triple-negative breast cancer (TNBC). PURPOSE: To identify the optimal phase of DCE-MRI for diagnosing TNBC and, in combination with clinical factors, to develop a clinical-radiomics model to well predict TNBC. MATERIAL AND METHODS: This retrospective study included 158 patients with pathology-confirmed breast cancer, including 38 cases of TNBC. The patients were randomly divided into the training and validation set (7:3). Eight radiomics models were built based on eight DCE-MR phases, and their performances were evaluated using receiver operating characteristic curve (ROC) and DeLong's test. The Radscore derived from the best radiomics model was integrated with independent clinical risk factors to construct a clinical-radiomics predictive model, and evaluate its performance using ROC analysis, calibration, and decision curve analyses. RESULTS: WHO classification, margin, and T2-weighted (T2W) imaging signals were significantly correlated with TNBC and independent risk factors for TNBC (P<0.05). The clinical model yielded areas under the curve (AUCs) of 0.867 and 0.843 in the training and validation sets, respectively. The radiomics model based on DCEphase7 achieved the highest efficacy, with an AUC of 0.818 and 0.777. The AUC of the clinical-radiomics model was 0.936 and 0.886 in the training and validation sets, respectively. The decision curve showed the clinical utility of the clinical-radiomics model. CONCLUSION: The radiomics features of DCE-MRI had the potential to predict TNBC and could improve the performance of clinical risk factors for preoperative personalized prediction of TNBC.

The clinical value of dual-energy CT imaging in preoperative evaluation of pathological types of gastric cancer.

BACKGROUND: Gastric cancer (GC) is the fifth most common cancer worldwide and the third leading cause of cancer death. Due to the low rate of early diagnosis, most patients are already in the advanced stage and lose the chance of radical surgery. OBJECTIVE: To investigate the clinical value of computed tomography (CT) dual-energy imaging in preoperative evaluation of pathological types of gastric cancer patients. METHODS: 121 patients with gastric cancer were selected. Dual-energy CT imaging was performed on the patients. The CT values of virtual noncontrast (VNC) images and iodine concentration of the lesion were measured, and the standardized iodine concentration ratio was calculated. The iodine concentration, iodine concentration ratio and CT values of VNC images of different pathological types were analyzed and compared. RESULTS: The iodine concentration and iodine concentration ratio of gastric mucinous carcinoma patients in venous phase and parenchymal phase were lower than those of gastric non-mucinous carcinoma patients, and the differences were statistically significant (P< 0.05). The iodine concentration and iodine concentration ratio of patients with mucinous adenocarcinoma in venous phase and parenchymal phase were lower than those of patients with choriocarcinoma, and the differences were statistically significant (P< 0.05). The iodine concentration and iodine concentration ratio of middle and high differentiated adenocarcinoma patients in venous phase and parenchymal phase were lower than those of low differentiated adenocarcinoma patients, and the differences were statistically significant (P< 0.05). However, there was no significant difference in CT values of VNC images among venous, arterial, and parenchymal phases in all pathological types of gastric cancer patients (P> 0.05). CONCLUSION: Dual-energy CT imaging plays an important role in the preoperative evaluation of patients with gastric cancer. The pathological types of gastric cancer are different, and the iodine concentration will change accordingly. Dual-energy CT imaging can effectively evaluate the pathological types of gastric cancer and has high clinical application value.

Color filtering localization for three-dimensional underwater acoustic sensor networks.

Accurate localization of mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. The proportion factor of distance is also proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. The normalized nearness degrees are considered as the weighted standards to calculate the coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can localize the mobile node in a timely way. The average localization error of PCFL is decreased by about 30.4% compared to the AFLA method.