Italiano Antoine, Gautier Othilie, Dupont Jules, Assi Tarek, Dawi Lama, Lawrance Littisha, Bone Alexandre, Jardali Ghina, Choucair Aurelie, Ammari Samy, Bayle Arnaud, Rouleau Etienne, Cournede Paul Henry, Borget Isabelle, Besse Benjamin, Barlesi Fabrice, Massard Christophe, Lassau Nathalie
Department of Medical Oncology, Institut Bergonié, Bordeaux, France; Département d'Innovation Thérapeutique et des Essais Précoce, Gustave Roussy, Villejuif, France.
Laboratoire d'Imagerie Biomedicale Multimodale Paris-Saclay (BioMAPS), Université Paris-Saclay, Inserm, Centre National de la Recherche Scientifique, Commissariat à l'Energie Atomique, Villejuif 94800, France.
Eur J Cancer. 2025 Jul 8;226:115609. doi: 10.1016/j.ejca.2025.115609.
With the advances in artificial intelligence (AI) and precision medicine, radiomics has emerged as a promising tool in the field of oncology. Radiogenomics integrates radiomics with genomic data, potentially offering a non-invasive method for identifying biomarkers relevant to cancer therapy. Liquid biopsy (LB) has further revolutionized cancer diagnostics by detecting circulating tumor DNA (ctDNA), enabling real-time molecular profiling. This study explores the integration of radiomics and LB to predict genomic alterations in solid tumors, including lung, colon, pancreatic, and prostate cancers.
A retrospective study was conducted on 418 patients from the STING trial (NCT04932525), all of whom underwent both LB and CT imaging. Predictive models were developed using an XGBoost logistic classifier, with statistical analysis performed to compare tumor volumes, lesion counts, and affected organs across molecular subtypes. Performance was evaluated using area under the curve (AUC) values and cross-validation techniques.
Radiomic models demonstrated moderate-to-good performance in predicting genomic alterations. KRAS mutations were best identified in pancreatic cancer (AUC=0.97), while moderate discrimination was noted in lung (AUC=0.66) and colon cancer (AUC=0.64). EGFR mutations in lung cancer were detected with an AUC of 0.74, while BRAF mutations showed good discriminatory ability in both lung (AUC=0.79) and colon cancer (AUC=0.76). In the radiomics predictive model, AR mutations in prostate cancer showed limited discrimination (AUC = 0.63).
This study highlights the feasibility of integrating radiomics and LB for non-invasive genomic profiling in solid tumors, demonstrating significant potential in patient stratification and personalized oncology care. While promising, further prospective validation is required to enhance the generalizability of these models.
随着人工智能（AI）和精准医学的发展，放射组学已成为肿瘤学领域一种有前景的工具。放射基因组学将放射组学与基因组数据相结合，有可能提供一种识别与癌症治疗相关生物标志物的非侵入性方法。液体活检（LB）通过检测循环肿瘤DNA（ctDNA）进一步革新了癌症诊断，实现了实时分子分析。本研究探讨了放射组学与液体活检相结合以预测实体瘤（包括肺癌、结肠癌、胰腺癌和前列腺癌）基因组改变的情况。
对来自STING试验（NCT04932525）的418例患者进行了一项回顾性研究，所有患者均接受了液体活检和CT成像。使用XGBoost逻辑分类器开发预测模型，并进行统计分析以比较不同分子亚型的肿瘤体积、病灶数量和受累器官。使用曲线下面积（AUC）值和交叉验证技术评估模型性能。
放射组学模型在预测基因组改变方面表现出中等至良好的性能。KRAS突变在胰腺癌中识别效果最佳（AUC = 0.97），而在肺癌（AUC = 0.66）和结肠癌（AUC = 0.64）中鉴别能力中等。肺癌中EGFR突变的检测AUC为0.74，而BRAF突变在肺癌（AUC = 0.79）和结肠癌（AUC = 0.76）中均显示出良好的鉴别能力。在放射组学预测模型中，前列腺癌中的AR突变鉴别能力有限（AUC = 0.63）。
本研究强调了将放射组学与液体活检相结合用于实体瘤非侵入性基因组分析的可行性，在患者分层和个性化肿瘤护理方面显示出巨大潜力。虽然前景广阔，但需要进一步的前瞻性验证以提高这些模型的通用性。
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