Abstract
ObjectiveTo investigate radiomics features extracted from PET and CT components of 18F-FDG PET/CT images integrating clinical factors and metabolic parameters of PET to predict progression-free survival (PFS) in advanced high-grade serous ovarian cancer (HGSOC).MethodsA total of 261 patients were finally enrolled in this study and randomly divided into training (n=182) and validation cohorts (n=79). The data of clinical features and metabolic parameters of PET were reviewed from hospital information system(HIS). All volumes of interest (VOIs) of PET/CT images were semi-automatically segmented with a threshold of 42% of maximal standard uptake value (SUVmax) in PET images. A total of 1700 (850×2) radiomics features were separately extracted from PET and CT components of PET/CT images. Then two radiomics signatures (RSs) were constructed by the least absolute shrinkage and selection operator (LASSO) method. The RSs of PET (PET_RS) and CT components(CT_RS) were separately divided into low and high RS groups according to the optimum cutoff value. The potential associations between RSs with PFS were assessed in training and validation cohorts based on the Log-rank test. Clinical features and metabolic parameters of PET images (PET_MP) with P-value <0.05 in univariate and multivariate Cox regression were combined with PET_RS and CT_RS to develop prediction nomograms (Clinical, Clinical+ PET_MP, Clinical+ PET_RS, Clinical+ CT_RS, Clinical+ PET_MP + PET_RS, Clinical+ PET_MP + CT_RS) by using multivariate Cox regression. The concordance index (C-index), calibration curve, and net reclassification improvement (NRI) was applied to evaluate the predictive performance of nomograms in training and validation cohorts.ResultsIn univariate Cox regression analysis, six clinical features were significantly associated with PFS. Ten PET radiomics features were selected by LASSO to construct PET_RS, and 1 CT radiomics features to construct CT_RS. PET_RS and CT_RS was significantly associated with PFS both in training (P <0.00 for both RSs) and validation cohorts (P=0.01 for both RSs). Because there was no PET_MP significantly associated with PFS in training cohorts. Only three models were constructed by 4 clinical features with P-value <0.05 in multivariate Cox regression and RSs (Clinical, Clinical+ PET_RS, Clinical+ CT_RS). Clinical+ PET_RS model showed higher prognostic performance than other models in training cohort (C-index=0.70, 95% CI 0.68-0.72) and validation cohort (C-index=0.70, 95% CI 0.66-0.74). Calibration curves of each model for prediction of 1-, 3-year PFS indicated Clinical +PET_RS model showed excellent agreements between estimated and the observed 1-, 3-outcomes. Compared to the basic clinical model, Clinical+ PET_MS model resulted in greater improvement in predictive performance in the validation cohort.ConclusionPET_RS can improve diagnostic accuracy and provide complementary prognostic information compared with the use of clinical factors alone or combined with CT_RS. The newly developed radiomics nomogram is an effective tool to predict PFS for patients with advanced HGSOC.
Highlights
Ovarian carcinoma is the leading cause of gynecologic cancer deaths because the majority of patients are diagnosed with advanced-stage disease (Stages III and IV) according to the International Federation of Gynecology and Obstetrics (FIGO) staging classification [1]
Inclusion criteria were as follows: [1] patients received cytoreductive surgery and 6-8 cycles of platinum-based chemotherapy; [2] postoperative pathological examination confirmed stage III and IV HGSOC; [3] 18F-FDG PET/CT examination was performed before surgery and neoadjuvant chemotherapy (NACT); [4] clinical, pathological, and follow-up information was available
FIGO stage, CA125, lymph node metastasis (LNM), volume of ascites, location of primary tumor, residual tumor(>2cm), NACT, and follow-up information were retrieved from the hospital information system
Summary
Ovarian carcinoma is the leading cause of gynecologic cancer deaths because the majority of patients are diagnosed with advanced-stage disease (Stages III and IV) according to the International Federation of Gynecology and Obstetrics (FIGO) staging classification [1]. HGSOC accounts for up to 70% of epithelial ovarian carcinoma [2, 3]. Most of those women achieve complete remission with cytoreductive surgery and cisplatin based chemotherapy. A significant proportion of patients with advanced HGSOC experience tumor recurrence and progression within 3 years [5]. Identification of tumor recurrence and progression in patients with advanced HGSOC after cytoreductive surgery is important since it guides the decisions about personalized treatment and surveillance plans
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