基于多标签学习的非小细胞肺癌PET/CT放射基因组突变预测
PET/CT Radiogenomic Mutation Prediction in Non-Small Cell Lung Cancer Using Multi-Label Learning
- Nuffield Department of Population Health, University of Oxford（牛津大学纳菲尔德人口健康系）
- Oxford University Hospitals NHS FT（牛津大学医院国民保健信托基金会）
- Department of Oncology, University of Oxford（牛津大学肿瘤学系）
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT（牛津大学医院国民保健信托基金会医学物理与临床工程系）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究针对NSCLC的PET/CT放射基因组突变预测问题，利用深度学习探究多标签学习的效果，发现其有效性取决于基因突变组合，为相关预测提供了突变特异性建模的思路。
中文摘要 AI 辅助
肺癌仍是全球癌症相关死亡的主要原因之一。尽管靶向疗法改善了非小细胞肺癌（NSCLC）患者的预后，但这些疗法依赖于通过组织活检进行的突变分析，这是一种具有多种局限性的侵入性操作。本研究利用深度学习探究基于PET/CT的放射基因组对表皮生长因子受体（EGFR）、肿瘤蛋白53（TP53）和Kirsten大鼠肉瘤病毒癌基因（KRAS）突变的预测能力，还评估了成对多标签学习是否比传统的单基因分类更能提升突变预测效果。据我们所知，这是首批系统探究多标签学习用于NSCLC的PET/CT放射基因组突变预测的研究之一。实验在一个新型的英国放射基因组队列上开展，KRAS与TP53的联合预测使KRAS的AUC从0.58提升至0.64，TP53的AUC从0.69提升至0.71；对于EGFR/KRAS配对，仅EGFR从联合学习中获益，而EGFR/TP53配对未观察到提升。这些发现表明，多标签学习的有效性取决于所建模基因突变的特定组合，提示针对突变的特异性建模策略可能更适用于PET/CT放射基因组预测。
英文摘要
Lung cancer remains one of the leading causes of cancer- related mortality worldwide. Although targeted therapies have improved outcomes for patients with non-small cell lung cancer (NSCLC), they rely on mutation profiling through tissue biopsy, an invasive procedure with several limitations. This study investigates PET/CT-based radio- genomic prediction of epidermal growth factor receptor (EGFR), tumour protein 53 (TP53), and Kirsten rat sarcoma viral oncogene (KRAS) mutations using deep learning. We further evaluate whether pairwise multi-label learning improves mutation prediction compared with conventional single-gene classification. To the best of our knowledge, this is among the first studies to systematically investigate multi-label learning for PET/CT radiogenomic mutation prediction in NSCLC. Experiments were conducted on a novel UK-based radiogenomics cohort. Joint pre- diction of KRAS and TP53 improved AUC from 0.58 to 0.64 for KRAS and from 0.69 to 0.71 for TP53. For the EGFR/KRAS pair, only EGFR benefited from joint learning, while no improvement was observed for the EGFR/TP53 pair. These findings demonstrate that the effectiveness of multi-label learning depends on the specific combination of gene mutations being modelled, suggesting that mutation-specific modelling strategies may be preferable for PET/CT radiogenomic prediction.
