A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer

Huoqiang Wang; Yi Li; Jiexi Han; Qin Lin; Long Zhao; Qiang Li; Juan Zhao; Haohao Li; Yiran Wang; Changlong Hu

doi:10.3389/fonc.2023.1192908

A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer

Front Oncol. 2023 Sep 15:13:1192908. doi: 10.3389/fonc.2023.1192908. eCollection 2023.

Authors

Huoqiang Wang¹, Yi Li¹, Jiexi Han², Qin Lin³, Long Zhao¹, Qiang Li¹, Juan Zhao¹, Haohao Li⁴, Yiran Wang², Changlong Hu⁵

Affiliations

¹ Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
² Shanghai miRAN Biotech Co. Ltd, Shanghai, China.
³ Department of Geriatrics, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
⁴ Faculty of Business and Economics, University of Hong Kong, Hong Kong, China.
⁵ School of Life Sciences, Fudan University, Shanghai, China.

Abstract

Objective: The aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data.

Methods: A retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively.

Results: The standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly.

Conclusion: The feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.

Keywords: PET/CT; diagnosis; lung cancer; machine-learning; pulmonary nodule.

Grants and funding

This study was supported by Shanghai Pulmonary Hospital affiliated to Tongji University (FK1947), the Shanghai Small and Medium-Sized Enterprises Innovation Fund (210H1153500) and Natural Science Foundation of Shanghai (23ZR1425900).