Wang H-J, Cao P-W, Nan S-M, Deng X-Y
Department of Radiology, ShangRao People's Hospital, Shangrao, Jiangxi, 334000, China.
Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
Clin Radiol. 2023 May;78(5):e386-e392. doi: 10.1016/j.crad.2023.01.017. Epub 2023 Feb 18.
To determine whether the mammography (MG)-based radiomics analysis and MG/ultrasound (US) imaging features could predict the malignant risk of phyllodes tumours (PTs) of the breast.
Seventy-five patients with PTs were included retrospectively (39 with benign PTs, 36 with borderline/malignant PTs) and divided into thetraining (n=52) and validation groups (n=23). The clinical information, MG and US imaging characteristics, and histogram features were extracted from craniocaudal (CC) and mediolateral oblique (MLO) images. The lesion region of interest (ROI) and perilesional ROI were delineated. Multivariate logistic regression analysis was performed to determine the malignant factors of PTs. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC), sensitivity, and specificity were calculated.
There was no significant difference found in the clinical or MG/US features between benign and borderline/malignant PTs. In the lesion ROI, variance in the CC view and mean and variance in the MLO view were independent predictors. The AUC was 0.942, sensitivity and specificity were 96.3% and 92%, respectively, in the training group. In the validation group, the AUC was 0.879, the sensitivity was 91.7%, and the specificity was 81.8%. In the perilesional ROI, the AUCs were 0.904 and 0.939, sensitivities were 88.9% and 91.7%, and the specificities were 92% and 90.9% in the training and validation groups, respectively.
MG-based radiomic features could predict the risk of malignancy of patients with PTs and may be used as a potential tool to differentiate benign and borderline/malignant PTs.
确定基于乳腺钼靶（MG）的放射组学分析以及MG/超声（US）成像特征能否预测乳腺叶状肿瘤（PTs）的恶性风险。
回顾性纳入75例PTs患者（39例为良性PTs，36例为交界性/恶性PTs），并分为训练组（n = 52）和验证组（n = 23）。从头尾位（CC）和内外斜位（MLO）图像中提取临床信息、MG和US成像特征以及直方图特征。勾勒出病变感兴趣区（ROI）和病变周围ROI。进行多因素逻辑回归分析以确定PTs的恶性因素。绘制受试者操作特征（ROC）曲线，并计算曲线下面积（AUC）、敏感性和特异性。
良性与交界性/恶性PTs在临床或MG/US特征方面未发现显著差异。在病变ROI中，CC位的方差以及MLO位的均值和方差是独立预测因素。训练组的AUC为0.942，敏感性和特异性分别为96.3%和92%。在验证组中，AUC为0.879，敏感性为91.7%，特异性为81.8%。在病变周围ROI中，训练组和验证组的AUC分别为0.904和0.939，敏感性分别为88.9%和91.7%，特异性分别为92%和90.9%。
基于MG的放射组学特征可预测PTs患者的恶性风险，并可能用作区分良性与交界性/恶性PTs的潜在工具。
