Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept
Morteza Salehjahromi, Tatiana V. Karpinets, Sheeba J. Sujit, Mohamed Qayati, Pingjun Chen, Muhammad Aminu, Maliazurina B. Saad, Rukhmini Bandyopadhyay, Lingzhi Hong, Ajay Sheshadri, Julie Qiaojin Lin, Mara B. Antonoff, Boris Sepesi, Edwin J. Ostrin, Iakovos Toumazis, Peng Huang, Chao Cheng, Tina Cascone, Natalie I. Vokes, Carmen Behrens, Jeffrey H. Siewerdsen, John D. Hazle, Joe Y. Chang, Jianhua Zhang, Yang Lü, Myrna C. B. Godoy, Caroline Chung, David A. Jaffray, Ignacio I. Wistuba, J. Jack Lee, Ara A. Vaporciyan, Don L. Gibbons, Gregory W. Gladish, John V. Heymach, Carol C. Wu, Jianjun Zhang, Jia Wu
Abstract
F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.