An MRI-based Radiomics Approach to Improve Breast Cancer Histological Grading

医学 乳腺癌 分级(工程) 逻辑回归 无线电技术 比例危险模型 危险分层 肿瘤科 内科学 队列 放射科 癌症 工程类 土木工程
作者
Meng Jiang,Chang-li Li,Xiaomao Luo,Zhi-Rui Chuan,Ruixue Chen,Chao-Ying Jin
出处
期刊:Academic Radiology [Elsevier]
卷期号:30 (9): 1794-1804 被引量:4
标识
DOI:10.1016/j.acra.2022.12.014
摘要

Rationale and Objectives Nottingham histological grade (NHG) 2 breast cancer has an intermediate risk of recurrence, which is not informative for therapeutic decision-making. We sought to develop and independently validate an MRI-based radiomics signature (Rad-Grade) to improve prognostic re-stratification of NHG 2 tumors. Materials and Methods Nine hundred-eight subjects with invasive breast cancer and preoperative MRI scans were retrospectively obtained. The NHG 1 and 3 tumors were randomly split into training and independent test cohort, with the NHG 2 as the prognostic validation set. From MRI image features, a radiomics-based signature predictive of the histological grade was built by use of the LASSO logistic regression algorithm. The model was developed for identifying NHG 1 and 3 radiological patterns, followed with re-stratification of NHG 2 tumors into Rad-Grade (RG)2-low (NHG 1-like) and RG2-high (NHG 3-like) subtypes using the learned patterns, and the prognostic value was assessed in terms of recurrence-free survival (RFS). Results The Rad-Grade showed independent prognostic value for re-stratification of NHG 2 tumors, where RG2-high had an increased risk for recurrence (HR 2.20, 1.10–4.40, p = 0.026) compared with RG2-low after adjusting for established risk factors. RG2-low shared similar phenotypic characteristics and RFS outcomes with NHG 1, and RG2-high with NHG 3, revealing that the model captures radiomic features in NHG 2 that are associated with different aggressiveness. The prognostic value of Rad-Grade was further validated in the NHG2 ER+ (HR 2.53, 1.13–5.56, p = 0.023) and NHG 2 ER+LN– (HR 5.72, 1.24–26.44, p = 0.025) subgroups, and in specific treatment contexts. Conclusion The radiomics-based re-stratification of NHG 2 tumors offers a cost-effective promising alternative to gene expression profiling for tumor grading and thus may improve clinical decisions. Nottingham histological grade (NHG) 2 breast cancer has an intermediate risk of recurrence, which is not informative for therapeutic decision-making. We sought to develop and independently validate an MRI-based radiomics signature (Rad-Grade) to improve prognostic re-stratification of NHG 2 tumors. Nine hundred-eight subjects with invasive breast cancer and preoperative MRI scans were retrospectively obtained. The NHG 1 and 3 tumors were randomly split into training and independent test cohort, with the NHG 2 as the prognostic validation set. From MRI image features, a radiomics-based signature predictive of the histological grade was built by use of the LASSO logistic regression algorithm. The model was developed for identifying NHG 1 and 3 radiological patterns, followed with re-stratification of NHG 2 tumors into Rad-Grade (RG)2-low (NHG 1-like) and RG2-high (NHG 3-like) subtypes using the learned patterns, and the prognostic value was assessed in terms of recurrence-free survival (RFS). The Rad-Grade showed independent prognostic value for re-stratification of NHG 2 tumors, where RG2-high had an increased risk for recurrence (HR 2.20, 1.10–4.40, p = 0.026) compared with RG2-low after adjusting for established risk factors. RG2-low shared similar phenotypic characteristics and RFS outcomes with NHG 1, and RG2-high with NHG 3, revealing that the model captures radiomic features in NHG 2 that are associated with different aggressiveness. The prognostic value of Rad-Grade was further validated in the NHG2 ER+ (HR 2.53, 1.13–5.56, p = 0.023) and NHG 2 ER+LN– (HR 5.72, 1.24–26.44, p = 0.025) subgroups, and in specific treatment contexts. The radiomics-based re-stratification of NHG 2 tumors offers a cost-effective promising alternative to gene expression profiling for tumor grading and thus may improve clinical decisions.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
茯苓完成签到,获得积分10
2秒前
5秒前
漓子完成签到,获得积分20
6秒前
7秒前
林天完成签到,获得积分10
7秒前
打打应助江楠酒采纳,获得30
7秒前
悠悠夏日长完成签到 ,获得积分10
8秒前
CipherSage应助怦然心动采纳,获得10
10秒前
Akim应助jason采纳,获得10
14秒前
阿玉完成签到 ,获得积分10
17秒前
科研小虫完成签到,获得积分10
22秒前
劲秉应助虚心的渊思采纳,获得30
23秒前
我不困完成签到,获得积分10
23秒前
彼得大帝发布了新的文献求助10
26秒前
研友_ZzrWKZ完成签到 ,获得积分10
26秒前
31秒前
桃博完成签到,获得积分10
31秒前
双黄应助木雨采纳,获得10
31秒前
cherlia发布了新的文献求助10
32秒前
35秒前
懒大王完成签到 ,获得积分10
38秒前
AA发布了新的文献求助10
38秒前
佘炭炭完成签到,获得积分10
38秒前
文文文完成签到,获得积分10
39秒前
jason发布了新的文献求助10
39秒前
66完成签到,获得积分10
49秒前
李健应助Fine采纳,获得10
51秒前
53秒前
可靠若云完成签到,获得积分10
55秒前
baihehuakai完成签到 ,获得积分10
55秒前
56秒前
耶耶喵喵完成签到 ,获得积分10
58秒前
59秒前
1分钟前
1分钟前
Fine发布了新的文献求助10
1分钟前
竹纤维完成签到 ,获得积分10
1分钟前
江楠酒发布了新的文献求助30
1分钟前
孔难破完成签到,获得积分10
1分钟前
乔心发布了新的文献求助10
1分钟前
高分求助中
Rock-Forming Minerals, Volume 3C, Sheet Silicates: Clay Minerals 2000
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Very-high-order BVD Schemes Using β-variable THINC Method 930
The Healthy Socialist Life in Maoist China 600
The Vladimirov Diaries [by Peter Vladimirov] 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3266236
求助须知:如何正确求助?哪些是违规求助? 2906047
关于积分的说明 8336505
捐赠科研通 2576446
什么是DOI,文献DOI怎么找? 1400528
科研通“疑难数据库(出版商)”最低求助积分说明 654786
邀请新用户注册赠送积分活动 633661