卵巢和输卵管原发癌肉瘤2例临床病理分析
Chinese Journal of Clinical and Experimental Pathology(2014)
Abstract
目的:探讨卵巢和输卵管原发癌肉瘤的临床病理学特征及鉴别诊断。方法采用免疫组化EliVision法对2例分别原发于卵巢和输卵管的癌肉瘤进行染色,并复习相关文献。结果例1,57岁,双侧卵巢均可见占位性病变,肿瘤组织由恶性的上皮和间叶成分构成。免疫表型:肿瘤上皮及间叶成分均表达CK和vimentin,不表达desmin、CD34和HCG。部分淋巴结可见转移瘤。例2,57岁,左侧输卵管腔内见一占位性病变,肿瘤组织由恶性的上皮和间叶成分共同构成。免疫表型:上皮成分表达CK和vimentin,间叶成分表达vim-entin,不表达CK。结论原发性卵巢和输卵管癌肉瘤少见。需与未成熟性畸胎瘤、分化差的癌、卵巢及输卵管肉瘤等鉴别。该肿瘤病程进展快,患者预后差。
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined