Relationship between ascites volume and clinical outcomes in epithelial ovarian cancer

Quan, Quan; Zhou, Shuwei; Liu, Yao; Yin, Wanchun; Liao, Qianqian; Ren, Siling; Zhang, Fenfen; Yu, Meng; Mu, Xiaoling

doi:10.1111/jog.14682

Cited by 15 publications

(12 citation statements)

References 38 publications

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“…Malignant ascites frequently develops in women with HGSOC ( 4 , 19 ). The composition of T-cell subsets in ascites is associated with drug resistance and a poor prognosis ( 5 , 6 ).…”

Section: Resultsmentioning

confidence: 99%

Ascitic Senescent T Cells Are Linked to Chemoresistance in Patients With Advanced High-Grade Serous Ovarian Cancer

Zhang

Yin

et al. 2022

Front. Oncol.

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Senescent T cells are reported to be increased in patients with cancer and are poor prognostic indicators. However, the distribution of senescent T cells and their correlation with clinical features in high-grade serous ovarian cancer (HGSOC) is unknown. We detected the percentage of senescent T cells in the peripheral blood and ascites of patients with advanced HGSOC (n = 86) at diagnosis by flow cytometry. Compared with healthy donors, patients with HGSOC exhibited an accumulation of CD28−CD57+ (Tsen) CD8+ T cells in the peripheral blood and ascites. The frequency of Tsen CD8+ T cells in the peripheral blood was positively correlated with age and pretreatment serum CA125 and increased in patients with large volume ascites, whereas the frequency of Tsen CD8+ T cells in ascites was elevated in patients with lymph node metastasis. Patients with Tsen-high in ascites (>19.92%), but not in the peripheral blood, were more likely to be resistant to chemotherapy and had shorter progression-free survival. Tsen CD8+ T cells exhibited common senescence features including increased SA-β-gal activity, declines in proliferation, loss of CD27 and gain of KLRG-1, and the production of cytokines. In ascites, the percentage of Tsen CD8+ T cells was positively correlated with levels of interleukin-10 and granzyme B. This study suggests the potential of ascitic Tsen CD8+ T cells at diagnosis as a prognostic biomarker in HGSOC.

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“…Malignant ascites frequently develops in women with HGSOC ( 4 , 19 ). The composition of T-cell subsets in ascites is associated with drug resistance and a poor prognosis ( 5 , 6 ).…”

Section: Resultsmentioning

confidence: 99%

Ascitic Senescent T Cells Are Linked to Chemoresistance in Patients With Advanced High-Grade Serous Ovarian Cancer

Zhang

Yin

et al. 2022

Front. Oncol.

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“…More investigation for an adequate method to evaluate patients with this metabolic pattern is needed. In any case, the impact of ascites volume on ovarian cancer outcome has been already studied, nding that it is an independent factor correlated with shorter PFS, suboptimal cytoreduction and chemoresistance [22,23].…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of pretreatment 18F-FDG PET/CT metabolic parameters in advanced high- grade serous ovarian cancer

Morales

Poggio

Huerga

et al. 2022

Preprint

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Background: Patients with advanced high-grade serous ovarian cancer (HGSOC) have a poor prognosis. Hence there have been attempts to find prognostic markers to identify patients with higher risk of relapse. Radiomic features have been proved as an emerging tool in different neoplasms. However, in ovarian cancer very few studies have investigated the role of metabolic parameters at diagnosis. The aim of our work was to assess the prognostic value of pretreatment 18F-FDG PET/CT quantitative metabolic parameters in patients with advanced high-grade serous ovarian cancer. A review of 47 patients diagnosed of advanced high grade serous ovarian cancer between 2016 and 2020 in our center was performed, evaluating pretreatment 18F-FDG-PET/CT metabolic parameters: maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG) and metabolic tumoral volume (MTV). The images were evaluated and quantitative parameters were obtained semiautomatically classifying VOIs as target, non target and total according to SULmax. Clinical data of relapse/progression and final status (exitus/alive) was collected. Disease-free survival (DFS) and overall survival (OS) were calculated. Optimal cutoff values with receiver operating characteristic curve/median values were used. Correlation between metabolic parameters and DFS/OS was made using univariate and survival-curves analysis. Results: Median DFS was 18 months (2.55-55) and OS 33.6 months (2.5-92). MTV total, MTV target, TLG total and TLG target were significantly associated with DFS (p 0.005, 0.011, 0.046 and 0.042 respectively). The survival-curves analysis showed a significant difference of DSF in patients with MTV total >427.8 cm3 and MTV target >434 cm3 versus patients with lower values (18.8 and 15.6 months vs 31 and 30 months, p 0.027 and 0.017 respectively). Patients with higher TLG total and TLG target values tended to have worse DSF, although not statistically significant (p 0.263 and 0.317, respectively). None correlation was found with OS. Conclusion: Pretreatment metabolic parameters MTV total and MTV target have prognostic value in patients with high-grade advanced serous ovarian cancer, being able to predict higher risk of relapse.

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“…We included age, NACT, residual tumor, volume of ascites, LNM, CA125, FIGO stage, location of primary tumor, and diameter of primary tumor as clinical features. Previous study indicated the volume of ascites was an independent predictor of PFS and OS in patients with epithelial ovarian carcinoma (24,25). Although in previous study the threshold of volume was set to 1500 ml or 2000 ml respectively to classify patients into small-and large-volume ascites groups.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Wang

2021

Front. Oncol.

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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.

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Relationship between ascites volume and clinical outcomes in epithelial ovarian cancer

Cited by 15 publications

References 38 publications

Ascitic Senescent T Cells Are Linked to Chemoresistance in Patients With Advanced High-Grade Serous Ovarian Cancer

Ascitic Senescent T Cells Are Linked to Chemoresistance in Patients With Advanced High-Grade Serous Ovarian Cancer

Prognostic value of pretreatment 18F-FDG PET/CT metabolic parameters in advanced high- grade serous ovarian cancer

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

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