亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings

前列腺切除术 医学 前列腺癌 接收机工作特性 生化复发 癌症 数字化病理学 前列腺 放射科 病理 内科学
作者
George Lee,Robert W. Veltri,Guangjing Zhu,Sahirzeeshan Ali,Jonathan I. Epstein,Anant Madabhushi
出处
期刊:European urology focus [Elsevier]
卷期号:3 (4-5): 457-466 被引量:53
标识
DOI:10.1016/j.euf.2016.05.009
摘要

Background Gleason scoring represents the standard for diagnosis of prostate cancer (PCa) and assessment of prognosis following radical prostatectomy (RP), but it does not account for patterns in neighboring normal-appearing benign fields that may be predictive of disease recurrence. Objective To investigate (1) whether computer-extracted image features within tumor-adjacent benign regions on digital pathology images could predict recurrence in PCa patients after surgery and (2) whether a tumor plus adjacent benign signature (TABS) could better predict recurrence compared with Gleason score or features from benign or cancerous regions alone. Design, setting, and participants We studied 140 tissue microarray cores (0.6 mm each) from 70 PCa patients following surgery between 2000 and 2004 with up to 14 yr of follow-up. Overall, 22 patients experienced recurrence (biochemical [prostate-specific antigen], local, or distant recurrence and cancer death) and 48 did not. Intervention RP was performed in all patients. Outcome measurements and statistical analysis The top 10 features identified as most predictive of recurrence within both the benign and cancerous regions were combined into a 10-feature signature (TABS). Computer-extracted nuclear shape and architectural features from cancerous regions, adjacent benign fields, and TABS were evaluated via random forest classification accuracy and Kaplan-Meier survival analysis. Results and limitations Tumor-adjacent benign field features were predictive of recurrence (area under the receiver operating characteristic curve [AUC]: 0.72). Tumor-field nuclear shape descriptors and benign-field local nuclear arrangement were the predominant features found for TABS (AUC: 0.77). Combining TABS with Gleason sum further improved identification of recurrence (AUC: 0.81). All experiments were performed using threefold cross-validation without independent test set validation. Conclusions Computer-extracted nuclear features within cancerous and benign regions predict recurrence following RP. Furthermore, TABS was shown to provide added value to common predictors including Gleason sum and Kattan and Stephenson nomograms. Patient summary Future studies may benefit from evaluation of benign regions proximal to the tumor on surgically excised prostate cancer tissue for assessing risk of disease recurrence.

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
OvO_4577完成签到,获得积分10
6秒前
21_xxrr完成签到 ,获得积分10
7秒前
清新的夜蕾完成签到 ,获得积分10
8秒前
北北完成签到,获得积分10
8秒前
9秒前
小二郎应助xiang采纳,获得10
10秒前
13秒前
冉亦完成签到,获得积分10
13秒前
卡卡东完成签到 ,获得积分10
25秒前
cao完成签到,获得积分10
25秒前
木棉完成签到,获得积分10
25秒前
hhh完成签到 ,获得积分10
27秒前
33秒前
FashionBoy应助我有一壶酒采纳,获得10
33秒前
Plikestudy发布了新的文献求助30
35秒前
科研通AI6.1应助Okanryo采纳,获得10
35秒前
37秒前
丸子完成签到 ,获得积分10
39秒前
41秒前
42秒前
43秒前
44秒前
量子星尘发布了新的文献求助10
45秒前
46秒前
科目三应助LY采纳,获得10
48秒前
48秒前
xiang发布了新的文献求助10
49秒前
yangzai完成签到 ,获得积分0
50秒前
alva发布了新的文献求助10
51秒前
katata完成签到 ,获得积分10
53秒前
55秒前
小蘑菇应助心灵美猎豹采纳,获得10
56秒前
AEGUO完成签到 ,获得积分10
59秒前
1分钟前
1分钟前
Criminology34应助后来采纳,获得10
1分钟前
科研通AI6.1应助aaa采纳,获得10
1分钟前
妖妖灵发布了新的文献求助10
1分钟前
兜兜发布了新的文献求助10
1分钟前
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
Encyclopedia of Forensic and Legal Medicine Third Edition 5000
Introduction to strong mixing conditions volume 1-3 5000
Aerospace Engineering Education During the First Century of Flight 3000
Agyptische Geschichte der 21.30. Dynastie 3000
Les Mantodea de guyane 2000
Electron Energy Loss Spectroscopy 1500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5779791
求助须知:如何正确求助?哪些是违规求助? 5649870
关于积分的说明 15452355
捐赠科研通 4910851
什么是DOI,文献DOI怎么找? 2642982
邀请新用户注册赠送积分活动 1590635
关于科研通互助平台的介绍 1545094