Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
GE Healthcare China, Daxing District, Tongji South Road No.1, Beijing, 100176, China.
J Cancer Res Clin Oncol. 2023 Oct;149(13):11635-11645. doi: 10.1007/s00432-023-05001-9. Epub 2023 Jul 5.
Accurate prediction of the grade of invasive ductal carcinoma (IDC) before treatment is vital for individualized therapy and improving patient outcomes. This study aimed to develop and validate a mammography-based radiomics nomogram that would incorporate the radiomics signature and clinical risk factors in the preoperative prediction of the histological grade of IDC.
The data of 534 patients from our hospital with pathologically confirmed IDC (374 in the training cohort and 160 in the validation cohort) were retrospectively analyzed. A total of 792 radiomics features were extracted from the patients' craniocaudal and mediolateral oblique view images. A radiomics signature was generated using the least absolute shrinkage and selection operator method. Multivariate logistic regression was adopted to establish a radiomics nomogram, the utility of which was evaluated using a receiver-operating characteristic curve, calibration curve, and decision curve analysis (DCA).
The radiomics signature was found to have a significant correlation with histological grade (P < 0.01), but the efficacy of the model is limited. The radiomics nomogram, which incorporated the radiomics signature and spicule sign into mammography, showed good consistency and discrimination in both the training cohort [area under the curve (AUC) = 0.75] and the validation cohort (AUC = 0.75). The calibration curves and DCA demonstrated the clinical usefulness of the proposed radiomics nomogram model.
A radiomics nomogram based on the radiomics signature and spicule sign can be used to predict the histological grade of IDC and assist in clinical decision-making for patients with IDC.
在治疗前准确预测浸润性导管癌（IDC）的分级对于个体化治疗和改善患者预后至关重要。本研究旨在开发和验证一种基于乳腺 X 线摄影的放射组学列线图，该列线图将放射组学特征和临床危险因素纳入术前 IDC 组织学分级的预测中。
回顾性分析了我院 534 例经病理证实为 IDC 的患者（训练队列 374 例，验证队列 160 例）的数据。从患者的头尾位和内外斜位图像中提取了 792 个放射组学特征。采用最小绝对值收缩和选择算子法生成放射组学特征。采用多变量逻辑回归建立放射组学列线图，并采用受试者工作特征曲线、校准曲线和决策曲线分析（DCA）评估其效能。
放射组学特征与组织学分级显著相关（P<0.01），但模型的效能有限。该放射组学列线图将放射组学特征和毛刺征纳入乳腺 X 线摄影中，在训练队列（AUC=0.75）和验证队列（AUC=0.75）中均具有良好的一致性和区分度。校准曲线和 DCA 表明了所提出的放射组学列线图模型的临床实用性。
基于放射组学特征和毛刺征的放射组学列线图可用于预测 IDC 的组织学分级，并辅助 IDC 患者的临床决策。
