Survival time prediction in patients with high-grade serous ovarian cancer based on 18F-FDG PET/CT- derived inter-tumor heterogeneity metrics

Abstract

Background: The presence of heterogeneity is a significant attribute within the context of ovarian cancer. This study aimed to assess the predictive accuracy of models utilizing quantitative ¹⁸F-FDG PET/CT derived inter-tumor heterogeneity metrics in determining progression-free survival (PFS) and overall survival (OS) in patients diagnosed with high-grade serous ovarian cancer (HGSOC). Additionally, the study investigated the potential correlation between model risk scores and the expression levels of p53 and Ki-67.

Methods: A total of 292 patients diagnosed with HGSOC were retrospectively enrolled at Shengjing Hospital of China Medical University (median age: 54 ± 9.4 years). Quantitative inter-tumor heterogeneity metrics were calculated based on conventional measurements and texture features of primary and metastatic lesions in ¹⁸F-FDG PET/CT. Conventional models, heterogeneity models, and integrated models were then constructed to predict PFS and OS. Spearman's correlation coefficient (ρ) was used to evaluate the correlation between immunohistochemical scores of p53 and Ki-67 and model risk scores.

Results: The C-indices of the integrated models were the highest for both PFS and OS models. The C-indices of the training set and testing set of the integrated PFS model were 0.898 (95% confidence interval [CI]: 0.881-0.914) and 0.891 (95% CI: 0.860-0.921), respectively. For the integrated OS model, the C-indices of the training set and testing set were 0.894 (95% CI: 0.871-0.917) and 0.905 (95% CI: 0.873-0.936), respectively. The integrated PFS model showed the strongest correlation with the expression levels of p53 (ρ = 0.859, p < 0.001) and Ki-67 (ρ = 0.829, p < 0.001).

Conclusions: The models based on ¹⁸F-FDG PET/CT quantitative inter-tumor heterogeneity metrics exhibited good performance for predicting the PFS and OS of patients with HGSOC. p53 and Ki-67 expression levels were strongly correlated with the risk scores of the integrated predictive models.

Keywords: Computed tomography; Heterogeneity; High-grade serous ovarian cancer; Positron emission tomography; Prognosis.

Fig. 1

Data analysis workflow. A Extraction of inter-tumor heterogeneity metrics based on conventional measurements. B Extraction of inter-tumor heterogeneity metrics based on texture features. C Survival analysis using the prognostic model. D Immunohistochemical scores of p53 and Ki-67 using Whole Slide Images. E Correlation analysis between the immunohistochemical scores and the risk scores obtained from the models

Fig. 2

Flowchart of the enrolled patients

Fig. 3

Parameters of the models. The related features in the integrated PFS model (A) and integrated OS model (B). FIGO_stage: the International Federation of Gynecology and Obstetrics stage; Sur_status: surgical excision status; cSE: cluster site entropy; HU_SM: standard error of CT value; HU-Kurtosis: kurtosis value of CT value; TLG-Kurtosis: kurtosis value of the total amount of glucose decomposition; HU_USS: uncorrected sum of squares of CT value; TLG_USS: uncorrected sum of squares of total lesion glycolysis

Fig. 4

Kaplan–Meier survival curves and ROC curves of the integrated models: 1- to 3-year progression-free rate in the training set (A, B) and the testing set (C, D); 1- to 5-year survival rate in the training set (E, F) and the testing set (G, H)

Fig. 5

Nomogram of the integrated PFS predictive model (A) and the integrated OS predictive model (B)

Fig. 6

Spearman’s correlation coefficient graph. The correlation between the risk scores of the prognostic models and immunohistochemical scores of p53 (A) and Ki-67 (B). The distribution of each variable is shown on the diagonal line, including p53/Ki-67 H-Score, Integrated_PFS_riskScore, Integrated_OS_riskScore, Conventional_PFS_riskScore, Conventional_OS_riskScore, Inter-tumor_PFS_riskScore, and Inter-tumor_OS_riskScore. The part below the diagonal line shows the scatterplots and fitting curves of the two variables. The part above the diagonal line shows Spearman’s correlation values of the two variables and the corresponding significance levels: *** represents p < 0.001