Comparison of Frozen and Permanent Section Diagnosis in Ovarian Neoplasms: Analysis of Factors Affecting Accuracy

冰冻切片程序 医学 生殖细胞肿瘤 病理 金标准(测试) 放射科 内科学 化疗
作者
Mojgan Akbarzadeh‐Jahromi,Fatemeh Sari Aslani,Hadi Raeisi,Mozhdeh Momtahan,Negar Taheri
出处
期刊:International Journal of Gynecological Pathology [Lippincott Williams & Wilkins]
卷期号:41 (4): 327-336 被引量:1
标识
DOI:10.1097/pgp.0000000000000821
摘要

Ovarian cancer is the seventh most common form of cancer among women worldwide. The aim of the study was to determine the accuracy of a frozen section and the factors affecting the accuracy of frozen diagnosis of ovarian neoplasms. This retrospective, cross-sectional study was conducted on 401 patients with ovarian masses with frozen section diagnosis in Shahid Faghihi Hospital affiliated to Shiraz University of Medical Sciences between 2014 and 2018. Each ovarian tumor sample was evaluated for histopathologic diagnosis using frozen and paraffin-embedded sections, which were reviewed by an expert gynecologic pathologist. Accuracy and diagnostic values were estimated by comparing the results of the 2 techniques, using the paraffin section as the gold standard. The overall accuracy of the frozen section was 94.5%. Its sensitivity was 85.3% for malignant, 88.2% for borderline, and 99.6% for benign tumors. Its specificity was also 99.7% for malignant, 98.0% for borderline, and 90.9% for benign tumors. The positive predictive value was 98.9% for malignant, 86.5% for borderline, and 94.6% for benign tumors. Most false negatives occurred in mucinous and borderline tumors. The sensitivity of malignant tumors of germ cell and sex cord-stromal cell types were 64.3% and 95.5%, respectively. The specificity of germ cell and sex-cord stromal tumors were 100% and 93.8%, respectively. Frozen section seems to be a precise technique for histopathologic diagnosis of ovarian tumors. However, borderline and mucinous tumors are the most problematic issues during frozen section diagnosis and malignant germ cell tumors have the lowest sensitivity.
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
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
刚刚
劳资想要发Nature完成签到,获得积分20
刚刚
李健的小迷弟应助Jrssion采纳,获得10
1秒前
5656发布了新的文献求助10
3秒前
洛希极限完成签到,获得积分10
4秒前
4秒前
4秒前
Charles发布了新的文献求助10
5秒前
gstaihn完成签到,获得积分10
5秒前
6秒前
wqqq完成签到,获得积分10
7秒前
秋秋完成签到 ,获得积分10
10秒前
云竹丶发布了新的文献求助10
11秒前
RJ应助小年小少采纳,获得10
12秒前
清秋1001完成签到 ,获得积分10
12秒前
14秒前
16秒前
思源应助冰霜采纳,获得30
17秒前
李小伟完成签到,获得积分10
17秒前
NYM发布了新的文献求助10
19秒前
23秒前
accerue发布了新的文献求助10
23秒前
23秒前
QVQ完成签到,获得积分10
25秒前
25秒前
赘婿应助科研通管家采纳,获得10
26秒前
SciGPT应助科研通管家采纳,获得10
26秒前
26秒前
在水一方应助科研通管家采纳,获得10
26秒前
AF1sh应助科研通管家采纳,获得10
26秒前
26秒前
26秒前
ding应助科研通管家采纳,获得10
26秒前
杨华启应助科研通管家采纳,获得10
26秒前
乐乐应助科研通管家采纳,获得10
26秒前
领导范儿应助科研通管家采纳,获得10
26秒前
AF1sh应助科研通管家采纳,获得10
26秒前
ddd发布了新的文献求助30
27秒前
迅速发财应助科研通管家采纳,获得10
27秒前
大模型应助科研通管家采纳,获得10
27秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Handbook of pharmaceutical excipients, Ninth edition 5000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 生物化学 化学工程 物理 计算机科学 复合材料 内科学 催化作用 物理化学 光电子学 电极 冶金 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6022862
求助须知:如何正确求助?哪些是违规求助? 7644764
关于积分的说明 16170789
捐赠科研通 5171141
什么是DOI,文献DOI怎么找? 2767001
邀请新用户注册赠送积分活动 1750398
关于科研通互助平台的介绍 1636995