Improved Prognosis of Treatment Failure in Cervical Cancer with Nontumor PET/CT Radiomics

The Journal of Nuclear Medicine

article

Cheryl C. Saenz¹  Jyoti Mayadev²  Michael T. McHale¹  Catheryn M. Yashar³  Ramez Eskander¹  Andrew Sharabi²  Carl K. Hoh⁴  Sebastian Obrzut⁴  Loren K. Mell²  Tahir I. Yusufaly⁵  Jingjing Zou⁶  Tyler J. Nelson²  Casey W. Williamson³  Aaron Simon³  Meenakshi Singhal²  Hannah Liu²  Hank Wong² 

[1] Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, University of California San Diego;Center for Precision Radiation Medicine;Department of Radiation Medicine and Applied Sciences, University of California San Diego;Department of Radiology, Division of Nuclear Medicine, University of California San Diego;Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, School of Medicine;Department of Family Medicine and Public Health and Department of Mathematics, University of California San Diego

关键词: oncology: GYN;    PET/CT;    statistical;    analysis;    cervical cancer;    outcomes;    radiomics;    whole-body;   

DOI  :  10.2967/jnumed.121.262618

学科分类：医学（综合）

来源: Society of Nuclear Medicine

【 摘 要 】

Radiomics has been applied to predict recurrence in several disease sites, but current approaches are typically restricted to analyzing tumor features, neglecting nontumor information in the rest of the body. The purpose of this work was to develop and validate a model incorporating nontumor radiomics, including whole-body features, to predict treatment outcomes in patients with previously untreated locoregionally advanced cervical cancer. Methods: We analyzed 127 cervical cancer patients treated definitively with chemoradiotherapy and intracavitary brachytherapy. All patients underwent pretreatment whole-body 18 0.5 indicates predictive power). Results: Optimal performance was seen in a Cox model including 1 clinical biomarker (whether or not a tumor was stage III–IVA), 2 GTV radiomic biomarkers (PET gray-level size-zone matrix small area low gray level emphasis and zone entropy), 1 PTV radiomic biomarker (major axis length), and 1 whole-body radiomic biomarker (CT bone root mean square). In particular, stratification into high- and low-risk groups, based on the linear risk score from this Cox model, resulted in a hazard ratio of 0.019 (95% CI, 0.004, 0.082), an improvement over stratification based on clinical stage alone, which had a hazard ratio of 0.36 (95% CI, 0.16, 0.83). Conclusion: Incorporating nontumor radiomic biomarkers can improve the performance of prognostic models compared with using only clinical and tumor radiomic biomarkers. Future work should look to further test these models in larger, multiinstitutional cohorts.

【 授权许可】

CC BY