MRI 放射组学在评估新辅助化疗治疗的乳腺癌患者的分子亚型、病理完全缓解和残留癌负荷中的应用。
MRI Radiomics for Assessment of Molecular Subtype, Pathological Complete Response, and Residual Cancer Burden in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy.
机构信息
Department of Radiology, Mayo Clinic, 200 First street SW, Rochester MN 55905.
Department of Radiology, Mayo Clinic, 200 First street SW, Rochester MN 55905.
出版信息
Acad Radiol. 2022 Jan;29 Suppl 1(Suppl 1):S145-S154. doi: 10.1016/j.acra.2020.10.020. Epub 2020 Nov 5.
RATIONALE AND OBJECTIVES
There are limited data on pretreatment imaging features that can predict response to neoadjuvant chemotherapy (NAC). To extract volumetric pretreatment MRI radiomics features and assess corresponding associations with breast cancer molecular subtypes, pathological complete response (pCR), and residual cancer burden (RCB) in patients treated with NAC.
MATERIALS AND METHODS
In this IRB-approved study, clinical and pretreatment MRI data from patients with biopsy-proven breast cancer who received NAC between September 2009 and July 2016 were retrospectively analyzed. Tumors were manually identified and semi-automatically segmented on first postcontrast images. Morphological and three-dimensional textural features were computed, including unfiltered and filtered image data, with spatial scaling factors (SSF) of 2, 4, and 6 mm. Wilcoxon rank-sum tests and area under the receiver operating characteristic curve were used for statistical analysis.
RESULTS
Two hundred and fifty nine patients with unilateral breast cancer, including 73 (28.2%) HER2+, 112 (43.2%) luminal, and 74 (28.6%) triple negative breast cancers (TNBC), were included. There was a significant difference in the median volume (p = 0.008), median longest axial tumor diameter (p = 0.009), and median longest volumetric diameter (p = 0.01) among tumor subtypes. There was also a significant difference in minimum signal intensity and entropy among the tumor subtypes with SSF = 4 mm (p = 0.009 and p = 0.02 respectively) and SSF = 6 mm (p = 0.007 and p < 0.001 respectively). Additionally, sphericity (p = 0.04) in HER2+ tumors and entropy with SSF = 2, 4, 6 mm (p = 0.004, 0.02, 0.047 respectively) in luminal tumors were significantly associated with pCR. Multiple features demonstrated significant association (p < 0.05) with pCR in TNBC and with RCB in luminal tumors and TNBC, with standard deviation of intensity with SSF = 6 mm achieving the highest AUC (AUC = 0.734) for pCR in TNBC.
CONCLUSION
MRI radiomics features are associated with different molecular subtypes of breast cancer, pCR, and RCB. These features may be noninvasive imaging biomarkers to identify cancer subtype and predict response to NAC.
背景与目的
目前关于能够预测新辅助化疗（NAC）反应的预处理成像特征的研究数据有限。本研究旨在提取容积预处理 MRI 放射组学特征，并评估其与接受 NAC 治疗的患者的乳腺癌分子亚型、病理完全缓解（pCR）和残留肿瘤负担（RCB）之间的相关性。
材料与方法
本研究经机构审查委员会批准，回顾性分析了 2009 年 9 月至 2016 年 7 月期间接受 NAC 治疗的经活检证实为乳腺癌的患者的临床和预处理 MRI 数据。首先对增强后的图像进行手动肿瘤识别和半自动分割。计算形态学和三维纹理特征，包括未过滤和过滤的图像数据，空间缩放因子（SSF）为 2、4 和 6mm。采用 Wilcoxon 秩和检验和受试者工作特征曲线下面积进行统计学分析。
结果
共纳入 259 例单侧乳腺癌患者，包括 73 例（28.2%）HER2+、112 例（43.2%） luminal 和 74 例（28.6%）三阴性乳腺癌（TNBC）。肿瘤亚型之间的中位肿瘤体积（p=0.008）、最长轴向肿瘤直径（p=0.009）和最长容积直径（p=0.01）中位数存在显著差异。在 SSF=4mm（p=0.009 和 p=0.02）和 SSF=6mm（p=0.007 和 p<0.001）时，肿瘤亚型之间的最小信号强度和熵也存在显著差异。HER2+肿瘤的球形度（p=0.04）和 luminal 肿瘤的 SSF=2、4、6mm 时的熵（p=0.004、0.02、0.047）与 pCR 显著相关。在 TNBC 中，多个特征与 pCR 显著相关（p<0.05），在 luminal 肿瘤和 TNBC 中，与 RCB 显著相关，其中 SSF=6mm 时的强度标准差获得 TNBC 中 pCR 的最高 AUC（AUC=0.734）。
结论
MRI 放射组学特征与乳腺癌的不同分子亚型、pCR 和 RCB 相关。这些特征可能是非侵入性的影像学生物标志物，可用于识别癌症亚型并预测 NAC 反应。
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