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

Autor: Yusufaly TI; Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland; tyusufa2@jhmi.edu., Zou J; Department of Family Medicine and Public Health and Department of Mathematics, University of California San Diego, La Jolla, California., Nelson TJ; Center for Precision Radiation Medicine, La Jolla, California., Williamson CW; Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California., Simon A; Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California., Singhal M; Center for Precision Radiation Medicine, La Jolla, California., Liu H; Center for Precision Radiation Medicine, La Jolla, California., Wong H; Center for Precision Radiation Medicine, La Jolla, California., Saenz CC; Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, University of California San Diego, La Jolla, California; and., Mayadev J; Center for Precision Radiation Medicine, La Jolla, California.; Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California., McHale MT; Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, University of California San Diego, La Jolla, California; and., Yashar CM; Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California., Eskander R; Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, University of California San Diego, La Jolla, California; and., Sharabi A; Center for Precision Radiation Medicine, La Jolla, California.; Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California., Hoh CK; Department of Radiology, Division of Nuclear Medicine, University of California San Diego, La Jolla, California., Obrzut S; Department of Radiology, Division of Nuclear Medicine, University of California San Diego, La Jolla, California., Mell LK; Center for Precision Radiation Medicine, La Jolla, California.; Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California.
Jazyk: angličtina
Zdroj: Journal of nuclear medicine : official publication, Society of Nuclear Medicine [J Nucl Med] 2022 Jul; Vol. 63 (7), pp. 1087-1093. Date of Electronic Publication: 2021 Oct 28.
DOI: 10.2967/jnumed.121.262618
Abstrakt: 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 F-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.
(© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)
Databáze: MEDLINE