Department of Nuclear Medicine, Humanitas Clinical and Research Center- IRCCS, via Manzoni 56, 20089, Rozzano, Milan, Italy.
Department of Medical Oncology and Hematology, Humanitas Clinical and Research Center- IRCCS, Rozzano, Milan, Italy.
Eur J Nucl Med Mol Imaging. 2019 Jul;46(7):1468-1477. doi: 10.1007/s00259-019-04313-8. Epub 2019 Mar 26.
To assess the role of radiomics parameters in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer.
Seventy-nine patients who had undergone pretreatment staging F-FDG PET/CT and treatment with NAC between January 2010 and January 2018 were included in the study. Primary lesions on PET images were delineated, and extraction of first-, second-, and higher-order imaging features was performed using LIFEx software. The relationship between these parameters and pCR to NAC was analyzed by multiple logistic regression models.
Nineteen patients (24%) had pCR to NAC. Different models were generated on complete information and imputed datasets, using univariable and multivariable logistic regression and least absolute shrinkage and selection operator (lasso) regression. All models could predict pCR to NAC, with area under the curve values ranging from 0.70 to 0.73. All models agreed that tumor molecular subtype is the primary predictor of the primary endpoint.
Our models predicted that patients with subtype 2 and subtype 3 (HER2+ and triple negative, respectively) are more likely to have a pCR to NAC than those with subtype 1 (luminal). The association between PET imaging features and pCR suggested that PET imaging features could be considered as potential predictors of pCR in locally advanced breast cancer patients.
评估影像组学参数在预测局部晚期乳腺癌患者新辅助化疗（NAC）病理完全缓解（pCR）中的作用。
本研究纳入了 2010 年 1 月至 2018 年 1 月期间接受治疗前 F-FDG PET/CT 分期和 NAC 治疗的 79 例患者。在 PET 图像上勾画原发肿瘤病灶，并使用 LIFEx 软件提取一级、二级和更高阶的成像特征。通过多变量逻辑回归模型分析这些参数与 NAC 病理完全缓解的关系。
19 例患者（24%）对 NAC 有 pCR。在完整信息和插补数据集上分别生成了不同的模型，使用单变量和多变量逻辑回归以及最小绝对收缩和选择算子（lasso）回归。所有模型均能预测 NAC 的 pCR，曲线下面积（AUC）值在 0.70 至 0.73 之间。所有模型均认为肿瘤分子亚型是主要的预测因素。
我们的模型预测，2 型和 3 型（分别为 HER2+和三阴性）患者比 1 型（luminal）患者更有可能对 NAC 有 pCR。PET 成像特征与 pCR 的相关性表明，PET 成像特征可作为局部晚期乳腺癌患者 pCR 的潜在预测因素。
