Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Z.W.).
Breast Disease Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Q.L., H.W., T.B.).
Acad Radiol. 2023 Sep;30 Suppl 2:S71-S81. doi: 10.1016/j.acra.2023.03.038. Epub 2023 May 20.
Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization and individualized risk assessment. The present study aims to build and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that could distinguish DCISM from pure DCIS breast cancer.
MR images of 140 patients obtained between March 2019 and November 2022 at our institution were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The independent clinical risk factors were selected by multivariate logistic regression to establish the clinical model. The optimal radiomics features were chosen by the least absolute shrinkage and selection operator, and a radiomics signature was built. The nomogram model was constructed by integrating the radiomics signature and independent risk factors. The discrimination efficacy of our nomogram was assessed by using calibration and decision curves.
Six features were selected to construct the radiomics signature for distinguishing DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval [CI], 0.703-0.926, 0.848-0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672-0.989, 0.764-0.999) sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544-0.801, 0.527-0.907). The decision curve also demonstrated that the nomogram model exhibited good clinical utility.
The proposed noninvasive MRI-based radiomics nomogram model showed good performance in distinguishing DCISM from DCIS.
准确区分微浸润性导管原位癌（DCISM）与导管原位癌（DCIS）有助于优化治疗方案和进行个体化风险评估。本研究旨在构建并验证一种基于动态对比增强磁共振成像（DCE-MRI）的放射组学列线图，以区分 DCISM 与纯 DCIS 乳腺癌。
纳入 2019 年 3 月至 2022 年 11 月期间在我院进行的 140 例患者的 MRI 图像。患者被随机分为训练集（n=97）和测试集（n=43）。两组患者进一步分为 DCIS 和 DCISM 亚组。采用多变量逻辑回归筛选独立的临床危险因素以建立临床模型。采用最小绝对收缩和选择算子（LASSO）选择最佳放射组学特征，并构建放射组学特征签名。通过整合放射组学特征和独立危险因素构建列线图模型。通过校准和决策曲线评估我们的列线图模型的判别效能。
选择 6 个特征构建用于区分 DCISM 与 DCIS 的放射组学特征签名。放射组学特征签名和列线图模型在训练集（AUC 0.815、0.911、95%置信区间[CI]：0.703-0.926、0.848-0.974）和测试集（AUC 0.830、0.882、95%CI：0.672-0.989、0.764-0.999）中的校准和验证性能均优于临床因素模型（AUC 0.672、0.717、95%CI：0.544-0.801、0.527-0.907）。决策曲线也表明，该列线图模型具有良好的临床应用价值。
本研究提出的基于无创 MRI 的放射组学列线图模型在区分 DCISM 与 DCIS 方面具有良好的性能。
