PAFR/Stat3 axis maintains the symbiotic ecosystem between tumor and stroma to facilitate tumor malignancy

Stroma surrounding the tumor cells plays crucial roles for tumor progression. However, little is known about the factors that maintain the symbiosis between stroma and tumor cells. In this study, we found that the transcriptional regulator-signal transducer and activator of transcription 3 (Stat3) was frequently activated in cancer-associated fibroblasts (CAFs), which was a potent facilitator of tumor malignancy, and formed forward feedback loop with platelet-activating factor receptor (PAFR) both in CAFs and tumor cells. Importantly, PAFR/Stat3 axis connected intercellular signaling crosstalk between CAFs and cancer cells and drove mutual transcriptional programming of these two types of cells. Two central Stat3-related cytokine signaling molecules-interleukin 6 (IL-6) and IL-11 played the critical role in the process of PAFR/Stat3 axis-mediated communication between tumor and CAFs. Pharmacological inhibition of PAFR and Stat3 activities effectively reduced tumor progression using CAFs/tumor co-culture xenograft model. Our study reveals that PAFR/Stat3 axis enhances the interaction between tumor and its associated stroma and suggests that targeting this axis can be an effective therapeutic strategy against tumor malignancy.

A fusion network model based on limited training samples for the automatic segmentation of pelvic endangered organs / 生物医学工程学杂志

When applying deep learning to the automatic segmentation of organs at risk in medical images, we combine two network models of Dense Net and V-Net to develop a Dense V-network for automatic segmentation of three-dimensional computed tomography (CT) images, in order to solve the problems of degradation and gradient disappearance of three-dimensional convolutional neural networks optimization as training samples are insufficient. This algorithm is applied to the delineation of pelvic endangered organs and we take three representative evaluation parameters to quantitatively evaluate the segmentation effect. The clinical result showed that the Dice similarity coefficient values of the bladder, small intestine, rectum, femoral head and spinal cord were all above 0.87 (average was 0.9); Jaccard distance of these were within 2.3 (average was 0.18). Except for the small intestine, the Hausdorff distance of other organs were less than 0.9 cm (average was 0.62 cm). The Dense V-Network has been proven to achieve the accurate segmentation of pelvic endangered organs.

Research on automatic segmentation of female bowel based on Dense V-Network neural network / 中华放射肿瘤学杂志

Objective:To resolve the issue of poor automatic segmentation of the bowel in women with pelvic tumors, a Dense V-Network model was established, trained and evaluated to accurately and automatically delineate the bowel of female patients with pelvic tumors.Methods:Dense Net and V-Net network models were combined to develop a Dense V-Network algorithm for automatic segmentation of 3D CT images. CT data were collected from 160 patients with cervical cancer, 130 of which were randomly selected as the training set to adjust the model parameters, and the remaining 30 were used as test set to evaluate the effect of automatic segmentation.Results:Eight parameters including Dice similarity coefficient (DSC) were utilized to quantitatively evaluate the segmentation effect. The DSC value, JD, ΔV, SI, IncI, HD (cm), MDA (mm), and DC (mm) of the small intestine were 0.86±0.03, 0.25±0.04, 0.10±0.07, 0.88±0.05, 0.85±0.05, 2.98±0.61, 2.40±0.45 and 4.13±1.74, which were better than those of any other single algorithm.Conclusion:Dense V-Network algorithm proposed in this paper can deliver accurate segmentation of the bowel organs. It can be applied in clinical practice after slight revision by physicians.