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

医学 无线电技术 放射科 宫颈癌 核医学 比例危险模型 阶段(地层学) 放化疗 近距离放射治疗 癌症 放射治疗 外科 内科学 生物 古生物学
作者
Tahir Yusufaly,Jingjing Zou,Tyler J. Nelson,Casey W. Williamson,Aaron B. Simon,Meenakshi Singhal,Hannah Liu,Hank Wong,Cheryl Saenz,Jyoti Mayadev,Michael McHale,Catheryn M. Yashar,Ramez N. Eskander,Andrew B. Sharabi,Carl K. Hoh,Sebastian Obrzut,Loren K. Mell
出处
期刊:The Journal of Nuclear Medicine [Society of Nuclear Medicine and Molecular Imaging]
卷期号:63 (7): 1087-1093 被引量:14
标识
DOI:10.2967/jnumed.121.262618
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 18F-FDG PET/CT. To quantify effects due to the tumor itself, the gross tumor volume (GTV) was directly contoured on the PET/CT image. Meanwhile, to quantify effects arising from the rest of the body, the planning target volume (PTV) was deformably registered from each planning CT to the PET/CT scan, and a semiautomated approach combining seed-growing and manual contour review generated whole-body muscle, bone, and fat segmentations on each PET/CT image. A total of 965 radiomic features were extracted for GTV, PTV, muscle, bone, and fat. Ninety-five patients were used to train a Cox model of disease recurrence including both radiomic and clinical features (age, stage, tumor grade, histology, and baseline complete blood cell counts), using bagging and split-sample-validation for feature reduction and model selection. To further avoid overfitting, the resulting models were tested for generalization on the remaining 32 patients, by calculating a risk score based on Cox regression and evaluating the c-index (c-index > 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.
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