想用影像预测基因突变？这篇论文告诉你多标签学习不是万能药，KRAS和TP53联合预测能提AUC，但EGFR组合就没用，得看突变配对。
该研究利用深度学习基于PET/CT影像预测非小细胞肺癌的EGFR、TP53和KRAS基因突变。实验采用英国新型放射基因组学队列，比较成对多标签学习与单基因分类的效果。联合预测KRAS和TP53将AUC从0.58提升至0.64（KRAS）和从0.69提升至0.71（TP53）。EGFR/KRAS对中仅EGFR受益，而EGFR/TP53对无改善。结果表明多标签学习的有效性取决于特定突变组合，提示需采用突变特异性建模策略。
PET/CT Radiogenomic Mutation Prediction in Non-Small Cell Lung Cancer Using Multi-Label Learning
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.