PET/CT Radiogenomic Mutation Prediction in Non-Small Cell Lung Cancer Using Multi-Label Learning
2026-08-10 • Machine Learning
Machine Learning
AI summaryⓘ
The authors studied a way to predict specific gene mutations in lung cancer using PET/CT scans and deep learning, which could be less invasive than traditional biopsies. They tested if predicting two mutations at the same time (multi-label learning) works better than predicting each mutation separately. They found that for some gene pairs, like KRAS and TP53, predicting together improved results, but for others, it did not. This suggests that the best prediction method depends on which mutations are considered. Their work is among the first to explore this approach with lung cancer imaging data.
lung cancernon-small cell lung cancer (NSCLC)PET/CT imagingdeep learningradiogenomicsEGFR mutationTP53 mutationKRAS mutationmulti-label learningAUC (Area Under the Curve)
Authors
Mona Furukawa, Sai Hyne, Daniel R. McGowan, Bartłomiej W. Papież
Abstract
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.