In Vivo Ultrasound Molecular Imaging in the Evaluation of Complex Ovarian Masses: A Practical Guide to Correlation with Ex Vivo Immunohistochemistry

离体 卵巢癌 免疫组织化学 体内 病理 医学 分子成像 癌症 放射科 肿瘤科 生物 内科学 生物技术
作者
Neha Antil,Huaijun Wang,Ahmed El Kaffas,Terry S. Desser,Ann K. Folkins,Teri A. Longacre,Jonathan S. Berek,A.M. Lutz
出处
期刊:Advanced biology [Wiley]
卷期号:7 (8) 被引量:1
标识
DOI:10.1002/adbi.202300091
摘要

Ovarian cancer is the fifth leading cause of cancer-related deaths in women and the most lethal gynecologic cancer. It is curable when discovered at an early stage, but usually remains asymptomatic until advanced stages. It is crucial to diagnose the disease before it metastasizes to distant organs for optimal patient management. Conventional transvaginal ultrasound imaging offers limited sensitivity and specificity in the ovarian cancer detection. With molecularly targeted ligands addressing targets, such as kinase insert domain receptor (KDR), attached to contrast microbubbles, ultrasound molecular imaging (USMI) can be used to detect, characterize and monitor ovarian cancer at a molecular level. In this article, the authors propose a standardized protocol is proposed for the accurate correlation between in- vivo transvaginal KDR-targeted USMI and ex vivo histology and immunohistochemistry in clinical translational studies. The detailed procedures of in vivo USMI and ex vivo immunohistochemistry are described for four molecular markers, CD31 and KDR with a focus on how to enable the accurate correlation between in vivo imaging findings and ex vivo expression of the molecular markers, even if not the entire tumor could can be imaged by USMI, which is not an uncommon scenario in clinical translational studies. This work aims to enhance the workflow and the accuracy of characterization of ovarian masses on transvaginal USMI using histology and immunohistochemistry as reference standards, which involves sonographers, radiologists, surgeons, and pathologists in a highly collaborative research effort of USMI in cancer.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
039Hc完成签到,获得积分10
2秒前
怡然枫叶完成签到,获得积分10
5秒前
jojo发布了新的文献求助10
6秒前
无辜的山柳完成签到,获得积分10
6秒前
852应助舒心的晟睿采纳,获得10
6秒前
U9A完成签到,获得积分20
6秒前
7秒前
Yolo应助武雨寒采纳,获得10
7秒前
xiaoma完成签到,获得积分20
8秒前
科研通AI2S应助杭谷波采纳,获得10
8秒前
Ccccccc完成签到,获得积分10
9秒前
9秒前
10秒前
yishang发布了新的文献求助10
10秒前
Qian发布了新的文献求助10
11秒前
11秒前
12秒前
邓艳梅发布了新的文献求助10
13秒前
脑洞疼应助微笑的桐采纳,获得10
14秒前
赘婿应助仁爱的飞机采纳,获得10
15秒前
16秒前
李健应助jojo采纳,获得10
16秒前
16秒前
17秒前
芸栖发布了新的文献求助10
17秒前
17秒前
18秒前
U9A完成签到,获得积分20
18秒前
19秒前
yizujiang发布了新的文献求助10
19秒前
愉快书琴发布了新的文献求助10
20秒前
量子星尘发布了新的文献求助10
20秒前
欣慰白山应助qq采纳,获得10
20秒前
22秒前
Mango发布了新的文献求助10
22秒前
xuxin发布了新的文献求助10
22秒前
Orange应助Tao采纳,获得10
23秒前
务实寒天发布了新的文献求助10
23秒前
cjchem发布了新的文献求助10
23秒前
大模型应助鱼雁采纳,获得10
24秒前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Social Research Methods (4th Edition) by Maggie Walter (2019) 2390
A new approach to the extrapolation of accelerated life test data 1000
北师大毕业论文 基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 390
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Robot-supported joining of reinforcement textiles with one-sided sewing heads 360
Atlas of Interventional Pain Management 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4005993
求助须知:如何正确求助?哪些是违规求助? 3545917
关于积分的说明 11294361
捐赠科研通 3281886
什么是DOI,文献DOI怎么找? 1809798
邀请新用户注册赠送积分活动 885568
科研通“疑难数据库(出版商)”最低求助积分说明 811048