Total Lesion Glycolysis Estimated by a Radiomics Model From CT Image Alone

Hongwei Si; Xinzhong Hao; Lianyu Zhang; Xiaokai Xu; Jianzhong Cao; Ping Wu; Li Li; Zhifang Wu; Shengyang Zhang; Sijin Li

doi:10.3389/fonc.2021.664346

Total Lesion Glycolysis Estimated by a Radiomics Model From CT Image Alone

Front Oncol. 2021 Jun 17:11:664346. doi: 10.3389/fonc.2021.664346. eCollection 2021.

Authors

Hongwei Si^{1

2}, Xinzhong Hao², Lianyu Zhang³, Xiaokai Xu¹, Jianzhong Cao⁴, Ping Wu², Li Li^{1

2}, Zhifang Wu², Shengyang Zhang¹, Sijin Li²

Affiliations

¹ Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
² Nuclear Medicine, The First Affiliated Hospital of Shanxi Medical University, Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China.
³ Department of Diagnostic Imaging, National Cancer Center/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁴ Department of Radiation Oncology, The Cancer Hospital of Shanxi Province, Taiyuan, China.

Abstract

Purpose: In this study, total lesion glycolysis (TLG) on positron emission tomography images was estimated by a trained and validated CT radiomics model, and its prognostic ability was explored among lung cancer (LC) and esophageal cancer patients (EC).

Methods: Using the identical features between the combined and thin-section CT, the estimation model of SUVsum (summed standard uptake value) was trained from the lymph nodes (LNs) of LC patients (n = 1239). Besides LNs of LC patients from other centers, the validation cohorts also included LNs and primary tumors of LC/EC from the same center. After calculating TLG (accumulated SUVsum of each individual) based on the model, the prognostic ability of the estimated and measured values was compared and analyzed.

Results: In the training cohort, the model of 3 features was trained by the deep learning and linear regression method. It performed well in all validation cohorts (n = 5), and a linear regression could correct the bias from different scanners. Additionally, the absolute biases of the model were not significantly affected by the evaluated factors whether they included LN metastasis or not. Between the estimated natural logarithm of TLG (elnTLG) and the measured values (mlnTLG), significant difference existed among both LC (n = 137, bias = 0.510 ± 0.519, r = 0.956, P<0.001) and EC patients (n = 56, bias = 0.251± 0.463, r = 0.934, P<0.001). However, for both cancers, the overall shapes of the curves of hazard ratio (HR) against elnTLG or mlnTLG were quite alike.

Conclusion: Total lesion glycolysis can be estimated by three CT features with particular coefficients for different scanners, and it similar to the measured values in predicting the outcome of cancer patients.

Keywords: computed tomography; positron emission tomography; radiomics; standard uptake value; total lesion glycolysis.