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Abstract：
To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.
摘要：
探讨肿瘤周围水肿（PE）是否可以增强深度学习影像组学（DLR）模型预测乳腺癌腋窝淋巴结转移（ALNM）负担。回顾性纳入具有术前MRI的浸润性乳腺癌患者，并根据手术病理将其分为低（<2个淋巴结（LNs））和高（≥2个LNs）负荷组。PE在T2WI上进行评估，并在DCE-MRI中从MRI可见的肿瘤中提取肿瘤内和围肿瘤影像学特征。在训练队列中开发了用于LN负担预测的深度学习模型，并在独立队列中进行了验证。通过接收器工作特性(ROC)分析评估PE的增量值，使用Delong检验确认曲线下面积(AUC)的改善。这得到了净重新分类改进(NRI)和综合歧视改进(IDI)指标的补充。深度学习组合模型，将PE与选定的放射学特征相结合，在训练队列中，与MRI模型和DLR模型相比，AUC值明显更高（n=177）（AUC：0.953vs.0.849和0.867，p<0.05）和验证队列（n=111）（AUC：0.963vs.0.883和0.882,p<0.05)。互补分析表明,PE显著增强DLR模型的预测性能(分类NRI:0.551,p<0.001;IDI=0.343,p<0.001)。这些发现在验证队列中得到证实(分类NRI:0.539,p<0.001;IDI=0.387,p<0.001)。PE改良术前ALNM负荷预测的DLR模型,促进乳腺癌患者的个性化腋窝管理。
