Purpose: This study investigated whether radiomic features extracted from [18F]FDG-PET scans acquired before and two weeks after neoadjuvant treatment, and their variation, provided prognostic parameters in locally advanced cervical cancer (LACC) patients treated with neoadjuvant chemo-radiotherapy (CRT) followed by radical surgery. Methods: We retrospectively included LACC patients referred to our Institution from 2010 to 2016. [18F]FDG-PET/CT was performed before neoadjuvant CRT (baseline) and two weeks after the start of treatment (early). Radiomic features were extracted after semi-automatic delineation of the primary tumour, on baseline and early PET images. Delta radiomics were calculated as the relative differences between baseline and early features. We performed 5-fold cross-validation stratified for recurrence and cancer-specific death, integrating dimensionality reduction of the radiomic features and variable hunting with importance within the folds. After supervised feature selection, radiomic models with the best-performing features for each timepoint, as well as clinical models and combined clinico-radiomic models, were built. Model performances are presented as C-indices, for prediction of recurrence/progression (disease-free survival, DFS) and cancer-specific death (overall survival, OS). Results: 95 patients were included. With a median follow-up of 76.0 months (95% CI: 59.5–82.1), 31.6% of patients had recurrence/progression and 20.0% died of disease. None of the models could predict DFS (C-indices ≤ 0.72). Model performances for OS yielded slightly better results, with mean C-indices of 0.75 for both the radiomic and combined model based on early features, 0.79 and 0.78 for the radiomic and combined model derived from delta features, and 0.76 for the clinical models. Conclusion: [18F]FDG-PET early and delta radiomic features could not predict DFS in patients with LACC treated with neoadjuvant CRT followed by radical surgery. Although slightly improved performances for the radiomic and combined models were observed in the prediction of OS compared to the clinical model, the added value of these parameters and their inclusion in the clinical practice seems to be limited.

This study investigated whether radiomic features extracted from pretreatment [18F]FDG PET could improve the prediction of both histopathologic tumor response and survival in patients with locally advanced cervical cancer (LACC) treated with neoadjuvant chemoradiotherapy followed by surgery compared with conventional PET parameters and histopathologic features. Methods: The medical records of all consecutive patients with LACC referred between July 2010 and July 2016 were reviewed. [18F]FDG PET/CT was performed before neoadjuvant chemoradiotherapy. Radiomic features were extracted from the primary tumor volumes delineated semiautomatically on the PET images and reduced by factor analysis. A receiver-operating-characteristic analysis was performed, and conventional and radiomic features were dichotomized with Liu’s method according to pathologic response (pR) and cancer-specific death. According to the study protocol, only areas under the curve of more than 0.70 were selected for further analysis, including logistic regression analysis for response prediction and Cox regression analysis for survival prediction. Results: A total of 195 patients fulfilled the inclusion criteria. At pathologic evaluation after surgery, 131 patients (67.2%) had no or microscopic (≤3 mm) residual tumor (pR0 or pR1, respectively); 64 patients (32.8%) had macroscopic residual tumor (>3 mm, pR2). With a median follow-up of 76.0 mo (95% CI, 70.7–78.7 mo), 31.3% of patients had recurrence or progression and 20.0% died of the disease. Among conventional PET parameters, SUVmean significantly differed between pathologic responders and nonresponders. Among radiomic features, 1 shape and 3 textural features significantly differed between pathologic responders and nonresponders. Three radiomic features significantly differed between presence and absence of recurrence or progression and between presence and absence of cancer-specific death. Areas under the curve were less than 0.70 for all parameters; thus, univariate and multivariate regression analyses were not performed. Conclusion: In a large series of patients with LACC treated with neoadjuvant chemoradiotherapy followed by surgery, PET radiomic features could not predict histopathologic tumor response and survival. It is crucial to further explore the biologic mechanism underlying imaging-derived parameters and plan a large, prospective, multicenter study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine.