Emergency and Trauma Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Department of FSTC Clinic of The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Acad Radiol. 2023 Sep;30(9):1794-1804. doi: 10.1016/j.acra.2022.12.014. Epub 2023 Jan 4.
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.
诺丁汉组织学分级（NHG）2 级乳腺癌具有中等复发风险，对于治疗决策没有帮助。本研究旨在开发并独立验证一种基于 MRI 的放射组学特征（Rad-Grade），以改善 NHG 2 肿瘤的预后再分层。
回顾性收集了 908 例浸润性乳腺癌患者的术前 MRI 扫描图像。NHG1 级和 3 级肿瘤随机分为训练集和独立测试集，NHG2 级肿瘤作为预后验证集。从 MRI 图像特征中，使用 LASSO 逻辑回归算法构建了预测组织学分级的放射组学特征模型。该模型用于识别 NHG1 级和 3 级的影像学模式，然后使用学习到的模式将 NHG2 肿瘤重新分为 Rad-Grade（RG）2-低（NHG1 样）和 RG2-高（NHG3 样）亚型，并根据无复发生存率（RFS）评估预后价值。
Rad-Grade 对 NHG2 肿瘤的再分层具有独立的预后价值，与 RG2-低相比，RG2-高的复发风险增加（HR 2.20，1.10-4.40，p=0.026），调整了既定的风险因素后。RG2-低与 NHG1 具有相似的表型特征和 RFS 结局，而 RG2-高与 NHG3 相似，表明该模型捕获了与不同侵袭性相关的 NHG2 中的放射组学特征。Rad-Grade 的预后价值在 NHG2ER+（HR 2.53，1.13-5.56，p=0.023）和 NHG2ER+LN-（HR 5.72，1.24-26.44，p=0.025）亚组中进一步得到验证，并在特定的治疗环境中得到验证。
基于放射组学的 NHG2 肿瘤再分层为肿瘤分级提供了一种具有成本效益的替代基因表达谱的方法，因此可能改善临床决策。
