Experiments demonstrate that our cascaded CNN method achieves not only a good tumor segmentation result with a high Dice similarity coefficient of 77.03%, but also a competitive genotype prediction result with an average accuracy of 94.85% upon fivefold cross-validation.
Automatic femur segmentation from computed tomography volume is a crucial but challenging task for computer-aided diagnosis in orthopedic surgeries. The main obstacles are weak bone boundaries, narrowness of joint space, variations in femur density and shape, as well as diverse leg postures. In this paper, we presented a novel 3D feature-enhanced network to address these challenges. The novelty of our approach lies in two feature enhancement modules, including the edge detection task and the multi-scale features fusion. First, the edge detection task was embedded into femur segmentation from computed tomography volume to solve the problems of narrow joint space and weak femur boundary. Crucially, a task-specific edge detector was used to optimize the performance of femur segmentation in an end-to-end trainable system. Second, the multi-scale features fusion provided both local and global contexts to handle the problems of large variations in leg postures as well as femur shape and density. The results demonstrated that accurate 3D femur segmentation with a high Dice similarity coefficient of 96.88% was achieved using the developed method, and the segmentation of computed tomography volume took 0.93 s on an average.
Background: Breast cancer (BC) is a type of disease with high heterogeneity. Molecular profiling, by revealing the intrinsic nature of its various subtypes, has extensively improved the therapeutic management of BC patients. However, the genomic mutation landscape of Chinese metastatic BC has not been fully explored. Methods: Matched plasma and mononuclear cells from 290 Chinese women with metastatic BC were sequenced using either of the two commercially-available panels consisting of 520 cancer-related and 108 BC-related genes. Both panels cover the same critical regions of 91 genes. The circulating tumor DNA mutation profile from our cohort was then compared with publicly-available metastatic BC datasets from Memorial Sloan Kettering Cancer Center (MSKCC) and Pan-cancer analysis of whole genomes (PCAWG). Results: A total of 1,201 mutations spanning 91 genes were detected from 234 patients, resulting in a mutation detection rate of 80.7%. TP53 (64.1%) was the gene with highest mutation frequency, followed by PIK3CA (31%), PTEN (11%), and RB1 (10%). Copy number amplifications (CNAs) in MYC (14.1%), FGFR1 (13.3%), CCND1 (6.6%), FGF3 (6.6%), FGF4 (6.2%) and FGF19 (6.2%) were also detected from our cohort. TP53 mutations were significantly more frequent among triple negative BC (TNBC), HR−/HER2+, and HR+/HER2+ BC, while less common in HR+/HER2-(P < 0.01). Meanwhile, PIK3CA mutations were significantly more frequent among HR+/HER2+, HR+/HER2-, and HR−/HER2+ BC, while less common in TNBC (P < 0.01). Pathogenic or likely pathogenic BRCA1/2 germline mutations were detected in 5.9% of the cohort and 4.4% in TNBC subgroup. Maximum allelic fraction (maxAF) of TP53, RB1, and PIK3CA mutations were associated with multiple organ metastasis. Patients with PIK3CA, PTEN, and RB1 mutation were more likely to have liver metastasis (P < 0.02). Compared with MSKCC and PCAWG dataset, Chinese patients had observably difference in genetic variation rates in different molecular subtypes (TNBC: TP53 73.0 vs. 91.
Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high risk of distant metastasis, in which the intercellular communication between tumor cells also plays a role. Exosomes can be released by tumor cells and promote distant metastasis through intercellular communication or changes in tumor microenvironment, it is an optimized transportation facility for biologically active payloads. This was a hypothesis-generating research on role of exosomal payload in TNBC distant metastasis. MethodsExosomes isolated from supernatant of MDA-MB-231 and MDA-MB-231-HM (a highly pulmonary metastatic variant of parental MDA-MB-231 cells) were characterized. Transwell assay, wound healing and CCK-8 assay were employed to explore the effect of exosomal MMP-1 on the metastatic capability of TNBC cells in vitro. Human breast cancer lung metastasis model in nude mice was established to observe the effect of exosomal MMP-1 in vivo. Tissue microarray and blood samples of TNBC patients were applied to analyze the relevance between MMP-1 with metastasis. ResultsMDA-MB-231-HM cells secrete exosomes enriched MMP-1, which can be taken up and enhance invasion and migration activities of TNBC cells. After ingesting exosomes enriched with MMP-1, cells secrete more MMP-1, which may interact with membrane G protein receptor protease activated receptor 1 (PAR1), thereby initiating epithelial-mesenchymal transition (EMT) to enhance capability of migration and invasion. The expressions of MMP-1 and PAR1 in the metastases of the 231-HM-exo treated mice were both up-regulated. Clinically, the exosomal MMP-1 can be detected from serum of patients with metastasis at higher concentration than that in pre-operative patients. Moreover, in patients with multiple distant metastases, the level of exosomal MMP-1 is also higher than that in patients with single lesion.
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