目的:术前评估腋窝淋巴结(ALN)状态是决定适当治疗的重要组成部分。根据ACOSOGZ0011试验,ALN状态评估的新目标是肿瘤负荷(低负荷,<3个阳性ALN;高负担,≥3个阳性ALN),而不是转移或非转移。我们旨在开发一种整合临床病理特征的放射学列线图,ABUS成像特征和来自ABUS的影像组学特征预测早期乳腺癌ALN肿瘤负荷.
方法:共纳入310例乳腺癌患者。从ABUS图像产生影像组学评分。采用多因素logistic回归分析建立预测模型,我们纳入了影像组学评分,ABUS影像学特征和临床病理特征,这与放射组学列线图一起呈现。此外,我们分别构建了一个ABUS模型来分析ABUS成像特征在预测ALN肿瘤负荷方面的表现.通过歧视评估模型的性能,校正曲线,和决策曲线。
结果:影像组学评分,由13个选定的特征组成,表现出中等的辨别能力(训练和测试集中的AUC0.794和0.789)。ABUS模型,包括直径,高回声光环,和缩回现象,表现出中等的预测能力(训练和测试集中的AUC0.772和0.736)。ABUS放射组学列线图,整合影像组学评分与回缩现象和美国报告的ALN状态,显示ALN肿瘤负荷和病理验证之间的准确一致性(训练和测试集中的AUC0.876和0.851)。决策曲线表明,ABUS放射组学列线图在临床上很有用,并且比经验丰富的放射科医生报告的ALN状态更好。
结论:ABUS放射组学列线图,非侵入性,个性化和精确的评估,可以帮助临床医生确定最佳治疗策略并避免过度治疗。
OBJECTIVE: Preoperative evaluation of axillary lymph node (ALN) status is an essential part of deciding the appropriate treatment. According to ACOSOG Z0011 trials, the new goal of the ALN status evaluation is tumor burden (low burden, < 3 positive ALNs; high burden, ≥ 3 positive ALNs), instead of metastasis or non-metastasis. We aimed to develop a radiomics nomogram integrating clinicopathologic features, ABUS imaging features and radiomics features from ABUS for predicting ALN tumor burden in early breast cancer.
METHODS: A total of 310 patients with breast cancer were enrolled. Radiomics score was generated from the ABUS images. Multivariate logistic regression analysis was used to develop the predicting model, we incorporated the radiomics score, ABUS imaging features and clinicopathologic features, and this was presented with a radiomics nomogram. Besides, we separately constructed an ABUS model to analyze the performance of ABUS imaging features in predicting ALN tumor burden. The performance of the models was assessed through discrimination, calibration curve, and decision curve.
RESULTS: The radiomics score, which consisted of 13 selected features, showed moderate discriminative ability (AUC 0.794 and 0.789 in the training and test sets). The ABUS model, comprising diameter, hyperechoic halo, and retraction phenomenon, showed moderate predictive ability (AUC 0.772 and 0.736 in the training and test sets). The ABUS radiomics nomogram, integrating radiomics score with retraction phenomenon and US-reported ALN status, showed an accurate agreement between ALN tumor burden and pathological verification (AUC 0.876 and 0.851 in the training and test sets). The decision curves showed that ABUS radiomics nomogram was clinically useful and more excellent than US-reported ALN status by experienced radiologists.
CONCLUSIONS: The ABUS radiomics nomogram, with non-invasive, individualized and precise assessment, may assist clinicians to determine the optimal treatment strategy and avoid overtreatment.