Department of Breast Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Yuexiu District, Guangzhou, Guangdong.
The Second Clinical School of Southern Medical University, Guangzhou.
Int J Surg. 2024 Apr 1;110(4):2162-2177. doi: 10.1097/JS9.0000000000001082.
Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients.
In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing.
For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05).
Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.
腋窝淋巴结（ALN）状态是乳腺癌（BC）的重要预后指标。本研究旨在构建一种基于机器学习和全转录组测序（WTS）的放射基因组多模态模型，以准确评估 BC 患者 ALN 转移（ALNM）、药物治疗反应的风险，并避免不必要的腋窝手术。
本研究对来自癌症基因组图谱（TCGA）、癌症成像档案（TCIA）和佛山队列的 1078 名 BC 患者进行了回顾性分析。这些患者被分为 TCIA 队列（N=103）、TCIA 验证队列（N=51）、杜克队列（N=138）、佛山队列（N=106）和 TCGA 队列（N=680）。从 BC 影像学图像中提取放射学特征，并使用技术校准差异表达基因表达。采用支持向量机模型筛选放射学和遗传特征，并基于放射基因组和临床病理特征建立多模态模型，以预测 ALNM。使用曲线下面积（AUC）评估模型预测的准确性，并使用决策曲线分析测量临床获益。通过基因集富集分析、免疫检查点基因表达的差异比较和药物敏感性测试对 BC 患者进行风险分层分析。
对于 ALNM 的预测，rad-score 能够在 Duke 和佛山队列中显著区分 ALN-和 ALN+患者（P<0.05）。同样，基因评分能够在 TCGA 队列中显著区分 ALN-和 ALN+患者（P<0.05）。放射基因组多模态列线图在 TCIA 队列（AUC 0.82，95%CI：0.74-0.91）和 TCIA 验证队列（AUC 0.77，95%CI：0.63-0.91）中表现出令人满意的性能。在风险亚组分析中，高低风险组之间的基因途径富集存在显著差异（P<0.05）。此外，不同的风险组可能表现出不同的治疗反应（P<0.05）。
总体而言，放射基因组多模态模型采用多模态数据，包括放射学图像、遗传和临床病理分型。放射基因组多模态列线图可以准确预测 BC 患者的 ALNM 和药物治疗反应。
