Yang Zhe, Wang Shouen, Yin Wei, Wang Ying, Liu Fanghua, Xu Jianshu, Han Long, Liu Chenglong
Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.
Department of Pathology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, China.
Transl Cancer Res. 2025 Mar 30;14(3):1836-1848. doi: 10.21037/tcr-24-1559. Epub 2025 Mar 18.
Breast cancer is one of the most commonly diagnosed malignancies in women worldwide, and the disease burden continues to aggravate. The tumor-node-metastasis (TNM) staging information is crucial for oncology physicians to develop appropriate clinical strategies. This study aimed to investigate the value of a radiomics-clinical model for predicting TNM stage in breast cancer patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
DCE-MRI images from 166 patients with pathologically confirmed breast cancer were retrospectively collected, including early stage (TNM0-TNM2) and locally advanced or advanced stage (TNM3-TNM4). Included patients were divided into a training cohort (n=116) and a test cohort (n=50). The radiomics, clinical and integrated models were constructed and a nomogram was established to distinguish the TNM0-TNM2 stage from the TNM3-TNM4 stage. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were employed to assess the predictability of the models.
Eighty-five patients were at the early stages, while 81 patients were at the other stages. In the training and test cohorts, the area under the curve (AUC) values for distinguishing early and advanced breast cancer were 0.870 and 0.818 for the nomogram, respectively. The nomogram calibration curves showed good agreement between the predicted and observed TNM stages in the training and test cohorts. The Hosmer-Lemeshow test showed that the nomogram fit perfectly in the two cohorts. DCA indicated that the nomogram displayed clear superiority in forecasting TNM staging over clinical and radiomic signatures.
Compared to traditional imaging methods, the clinical-radiomics nomogram acquired by DCE-MRI could potentially be utilized to preoperatively evaluate the TNM stage of breast cancer with relatively high accuracy. It can be an effective method to guide clinical decisions.
乳腺癌是全球女性中最常被诊断出的恶性肿瘤之一，疾病负担持续加重。肿瘤-淋巴结-转移（TNM）分期信息对于肿瘤内科医生制定合适的临床策略至关重要。本研究旨在探讨使用动态对比增强磁共振成像（DCE-MRI）的放射组学-临床模型在预测乳腺癌患者TNM分期中的价值。
回顾性收集166例经病理确诊的乳腺癌患者的DCE-MRI图像，包括早期（TNM0-TNM2）和局部晚期或晚期（TNM3-TNM4）。纳入患者分为训练队列（n=116）和测试队列（n=50）。构建放射组学、临床和整合模型，并建立列线图以区分TNM0-TNM2期和TNM3-TNM4期。采用受试者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估模型的预测能力。
85例患者处于早期，81例患者处于其他阶段。在训练和测试队列中，列线图区分早期和晚期乳腺癌的曲线下面积（AUC）值分别为0.870和0.818。列线图校准曲线显示训练和测试队列中预测的TNM分期与观察到的TNM分期之间具有良好的一致性。Hosmer-Lemeshow检验表明列线图在两个队列中拟合良好。DCA表明列线图在预测TNM分期方面比临床和放射组学特征具有明显优势。
与传统成像方法相比，通过DCE-MRI获得的临床-放射组学列线图可能有助于术前以相对较高的准确性评估乳腺癌的TNM分期。它可以成为指导临床决策的有效方法。
