Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

Yanlei Ji; Qingtao Qiu; Jing Fu; Kai Cui; Xia Chen; Ligang Xing; Xiaorong Sun

doi:10.2147/CMAR.S287128

Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

Cancer Manag Res. 2021 Jan 12:13:307-317. doi: 10.2147/CMAR.S287128. eCollection 2021.

Authors

Yanlei Ji^{1

2}, Qingtao Qiu³, Jing Fu⁴, Kai Cui⁵, Xia Chen⁶, Ligang Xing³, Xiaorong Sun⁶

Affiliations

¹ Department of Ultrasound Medicine, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, People's Republic of China.
² Department of Ultrasound Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China.
³ Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China.
⁴ Department of Ultrasound Medicine, The First Affiliated Hospital of Shandong First Medical University, Jinan, Shandong 250014, People's Republic of China.
⁵ Department of PET/CT, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, People's Republic of China.
⁶ Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China.

Abstract

Purpose: To investigate the impact of staging on differences in glucose metabolic heterogeneity between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) by ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) textural analysis and to develop a stage-specific PET radiomic prediction model to distinguish lung ADC from SCC.

Patients and methods: Patients who were histologically diagnosed with lung ADC or SCC and underwent pretreatment ¹⁸F-FDG PET/CT scans were retrospectively identified. Radiomic features were extracted from a semiautomatically outlined tumor region in the Chang-Gung Image Texture Analysis (CGITA) software package. The differences in radiomic parameters between lung ADC and SCC were compared stage-by-stage in 253 consecutive NSCLC patients with stages I to III disease. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. A radiomic signature for each stage was subsequently constructed and evaluated. Then, an individual nomogram incorporating the radiomic signature and clinical risk factors was established and evaluated. The performance of the constructed models was assessed by receiver operating characteristic (ROC) curve analysis, and the nomogram was further validated by calibration curve analysis.

Results: The performance of the radiomic signature for distinguishing lung ADC and SCC in both the training and validation cohorts was good, with AUCs of 0.883, 0.854, and 0.895 in the training cohort and 0.932, 0.944, and 0.886 in the validation cohort for stages I, II, and III NSCLC, respectively. The radiomic-clinical nomogram integrating radiomic features with independent clinical predictors exhibited more favorable discriminative performance, with AUCs of 0.982, 0.963, and 0.979 in the training cohort and 0.989, 0.984, and 0.978 in the validation cohort for stages I, II, and III, respectively.

Conclusion: Differences in PET radiomic features between lung ADC and SCC varied in different stages. Stage-specific PET radiomic prediction models provided more favorable performance for discriminating the histological subtype of NSCLC.

Keywords: PET; heterogeneity; non-small-cell lung cancer; positron emission tomography; staging; textural analysis.

Grants and funding

This work was supported by grants from the National Key Research and Development Program of China (Grant No. 2018YFC1313200), the National Natural Science Foundation of China (Grant Nos. 81572970 and 82001902), the Natural Science Foundation of Shandong Province (Grant Nos. ZR2019LZL019 and ZR2020QH198), the Taishan Scholars Program of Shandong Province (Grant No. ts20120505), the Jinan Scientific and Technology Development Project (Grant No. 201805005) and the Academic Promotion Program of Shandong First Medical University (Grant No. 2019LJ004).