RT Journal Article
SR Electronic
T1 PET/CT-Based Radiogenomics Supports KEAP1/NFE2L2 Pathway Targeting for Non–Small Cell Lung Cancer Treated with Curative Radiotherapy
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP jnumed.123.266749
DO 10.2967/jnumed.123.266749
A1 Bourbonne, Vincent
A1 Morjani, Moncef
A1 Pradier, Olivier
A1 Hatt, Mathieu
A1 Jaouen, Vincent
A1 Querellou, Solène
A1 Visvikis, Dimitris
A1 Lucia, François
A1 Schick, Ulrike
YR 2024
UL http://jnm.snmjournals.org/content/early/2024/02/15/jnumed.123.266749.abstract
AB In lung cancer patients, radiotherapy is associated with a increased risk of local relapse (LR) when compared with surgery but with a preferable toxicity profile. The KEAP1/NFE2L2 mutational status (MutKEAP1/NFE2L2) is significantly correlated with LR in patients treated with radiotherapy but is rarely available. Prediction of MutKEAP1/NFE2L2 with noninvasive modalities could help to further personalize each therapeutic strategy. Methods: Based on a public cohort of 770 patients, model RNA (M-RNA) was first developed using continuous gene expression levels to predict MutKEAP1/NFE2L2, resulting in a binary output. The model PET/CT (M-PET/CT) was then built to predict M-RNA binary output using PET/CT-extracted radiomics features. M-PET/CT was validated on an external cohort of 151 patients treated with curative volumetric modulated arc radiotherapy. Each model was built, internally validated, and evaluated on a separate cohort using a multilayer perceptron network approach. Results: The M-RNA resulted in a C statistic of 0.82 in the testing cohort. With a training cohort of 101 patients, the retained M-PET/CT resulted in an area under the curve of 0.90 (P < 0.001). With a probability threshold of 20% applied to the testing cohort, M-PET/CT achieved a C statistic of 0.7. The same radiomics model was validated on the volumetric modulated arc radiotherapy cohort as patients were significantly stratified on the basis of their risk of LR with a hazard ratio of 2.61 (P = 0.02). Conclusion: Our approach enables the prediction of MutKEAP1/NFE2L2 using PET/CT-extracted radiomics features and efficiently classifies patients at risk of LR in an external cohort treated with radiotherapy.