Guo Jiamin, Meng Wenjun, Li Qian, Zheng Yichen, Yin Hongkun, Liu Ying, Zhao Shuang, Ma Ji
Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China.
Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China.
Bioengineering (Basel). 2024 Jun 28;11(7):663. doi: 10.3390/bioengineering11070663.
The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its combination with MRI radiomic signatures can improve the predictive accuracy. We collected clinical and pathological information, as well as pretreatment breast MRI and abdominal CT images, of 121 patients with TNBC who underwent NAC at our hospital between January 2012 and September 2021. The presence of pretreatment sarcopenia was assessed using the L3 skeletal muscle index. Clinical models were constructed based on independent risk factors identified by univariate regression analysis. Radiomics data were extracted on breast MRI images and the radiomics prediction models were constructed. We integrated independent risk factors and radiomic features to build the combined models. The results of this study demonstrated that sarcopenia is an independent predictive factor for NAC efficacy in TNBC. The combination of sarcopenia and MRI radiomic signatures can further improve predictive performance.
肌肉减少症与三阴性乳腺癌（TNBC）新辅助化疗（NAC）疗效之间的关联仍不明确。本研究旨在探讨肌肉减少症作为TNBC中NAC反应预测因素的可能性，并评估其与MRI影像组学特征相结合是否能提高预测准确性。我们收集了2012年1月至2021年9月期间在我院接受NAC的121例TNBC患者的临床和病理信息，以及治疗前的乳腺MRI和腹部CT图像。使用L3骨骼肌指数评估治疗前肌肉减少症的存在情况。基于单因素回归分析确定的独立危险因素构建临床模型。从乳腺MRI图像中提取影像组学数据并构建影像组学预测模型。我们整合独立危险因素和影像组学特征以建立联合模型。本研究结果表明，肌肉减少症是TNBC中NAC疗效的独立预测因素。肌肉减少症与MRI影像组学特征相结合可进一步提高预测性能。
