基于多参数F-FDG PET/MRI的影像组学预测乳腺癌新辅助化疗后的病理完全缓解
Multiparametric F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.
作者信息
Umutlu Lale, Kirchner Julian, Bruckmann Nils-Martin, Morawitz Janna, Antoch Gerald, Ting Saskia, Bittner Ann-Kathrin, Hoffmann Oliver, Häberle Lena, Ruckhäberle Eugen, Catalano Onofrio Antonio, Chodyla Michal, Grueneisen Johannes, Quick Harald H, Fendler Wolfgang P, Rischpler Christoph, Herrmann Ken, Gibbs Peter, Pinker Katja
机构信息
Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany.
Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, 45147 Essen, Germany.
出版信息
Cancers (Basel). 2022 Mar 29;14(7):1727. doi: 10.3390/cancers14071727.
BACKGROUND
The aim of this study was to assess whether multiparametric F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification.
METHODS
A total of 73 female patients (mean age 49 years; range 27-77 years) with newly diagnosed, therapy-naive breast cancer underwent simultaneous F-FDG PET/MRI and were included in this retrospective study. All PET/MRI datasets were imported to dedicated software (ITK-SNAP v. 3.6.0) for lesion annotation using a semi-automated method. Pretreatment biopsy specimens were used to determine tumor histology, tumor and nuclear grades, and immunohistochemical status. Histopathological results from surgical tumor specimens were used as the reference standard to distinguish between complete pathological response (pCR) and noncomplete pathological response. An elastic net was employed to select the most important radiomic features prior to model development. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each model.
RESULTS
The best results in terms of AUCs and NPV for predicting complete pathological response in the entire cohort were obtained by the combination of all MR sequences and PET (0.8 and 79.5%, respectively), and no significant differences from the other models were observed. In further subgroup analyses, combining all MR and PET data, the best AUC (0.94) for predicting complete pathologic response was obtained in the HR+/HER2- group. No difference between results with/without the inclusion of PET characteristics was observed in the TN/HER2+ group, each leading to an AUC of 0.92 for all MR and all MR + PET datasets.
CONCLUSION
F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for the prediction of pCR in breast cancer patients, especially in those with HR+/HER2- receptor status.
背景
本研究旨在评估基于多参数F-FDG PET/MRI的放射组学分析是否能够预测乳腺癌患者的病理完全缓解，从而潜在地改善治疗前患者分层。
方法
共有73例新诊断、未接受过治疗的女性乳腺癌患者（平均年龄49岁；范围27-77岁）接受了同步F-FDG PET/MRI检查，并纳入本回顾性研究。所有PET/MRI数据集均导入专用软件（ITK-SNAP v. 3.6.0），采用半自动方法进行病变标注。治疗前活检标本用于确定肿瘤组织学、肿瘤和核分级以及免疫组化状态。手术肿瘤标本的组织病理学结果用作区分完全病理缓解（pCR）和非完全病理缓解的参考标准。在模型开发之前，采用弹性网络选择最重要的放射组学特征。计算每个模型的敏感性、特异性、阳性预测值、阴性预测值和准确性。
结果
在整个队列中，预测完全病理缓解的AUC和NPV方面，所有MR序列和PET联合使用获得了最佳结果（分别为0.8和79.5%），与其他模型未观察到显著差异。在进一步的亚组分析中，结合所有MR和PET数据，HR+/HER2-组预测完全病理缓解的最佳AUC为0.94。在TN/HER2+组中，纳入/不纳入PET特征的结果之间没有差异，所有MR和所有MR + PET数据集的AUC均为0.92。
结论
F-FDG PET/MRI能够对乳腺癌患者，尤其是HR+/HER2-受体状态的患者进行全面高质量的放射组学分析，以预测pCR。
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