J Nucl Med. 2021 Oct 28:jnumed.121.262618. doi: 10.2967/jnumed.121.262618. Online ahead of print.
ABSTRACT
Background: Radiomics has been applied to predict recurrence in several disease sites, but current approaches are typically restricted to analyzing tumor features, neglecting non-tumor information in the rest of the body. The purpose of this work was to develop and validate a model incorporating non-tumor 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. 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, and a semi-automated approach combining seed-growing and manual contour review generated whole body muscle, bone, and fat segmentations on each PET/CT. A total of 965 radiomic features were extracted for GTV, PTV, muscle, bone, and fat. 95 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 one clinical biomarker (whether or not a tumor was stage III-IVA), two GTV radiomic biomarkers (PET gray-level size-zone matrix small area low gray level emphasis and zone entropy), one PTV radiomic biomarker (major axis length) and one 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 [95% CI] of 0.019 [0.004, 0.082], an improvement over stratification based on clinical stage alone, which had a hazard ratio of 0.36 [0.16, 0.83]. Conclusion: Incorporating non-tumor radiomic biomarkers can improve the performance of prognostic models compared to using only clinical and tumor radiomic biomarkers. Future work should look to further test these models in larger, multi-institutional cohorts.
PMID:34711618 | DOI:10.2967/jnumed.121.262618
