Conditional Survival of Advanced Epithelial Ovarian Cancer: A Real-World Data Retrospective Cohort Study From the SEER Database

High-throughput viable circulating tumor cell isolation using tapered-slit membrane filter-based chipsets in the differential diagnosis of ovarian tumors

OBJECTIVE

To evaluate the diagnostic performance of circulating tumor cells (CTCs) using tapered-slit membrane filter (TSF)-based chipsets for the differential diagnosis of adnexal tumors.

METHODS

A total of 230 women with indeterminate adnexal tumors were prospectively enrolled. The sensitivity, specificity, and accuracy of the CTC-detecting chipsets were analyzed according to postoperative pathological results and compared with those of cancer antigen (CA)-125 and imaging tests.

RESULTS

Eighty-one (40.3%) benign tumors, 31 (15.4%) borderline tumors, and 89 (44.3%) ovarian cancers were pathologically confirmed. The sensitivity, specificity, and accuracy of CTC-detecting chipsets (75.3%, 58.0%, and 67.1%) for differentiating ovarian cancer from benign tumors were similar to CA-125 (78.7%, 53.1%, and 66.5%), but lower than CT/MRI (94.2%, 77.9%, and 86.5%). "CTC or CA125" showed increased sensitivity (91.0%) and "CTC and CA-125" revealed increased specificity (77.8%), comparable to CT/MRI. CTC detection rates in stage I/II and stage III/IV ovarian cancers were 69.6% and 81.4%, respectively. The sensitivity to detect high-grade serous (HGS) cancer from benign tumors (84.6%) was higher than that to detect non-HGS cancers (68.0%).

CONCLUSION

Although the diagnostic performance of the TSF platform to differentiate between ovarian cancer and benign tumors did not yield significant results, the combination of CTC and CA-125 showed promising potential in the diagnostic accuracy of ovarian cancer.

Prognostic factors and novel nomograms for overall survival and cancer specific survival of malignant ovarian cancer patients with bone metastasis: A SEER-based study

OBJECTIVE

Ovarian cancer (OC) is a frequent and fatal disease in women, and bone metastasis of ovarian cancer (OCBM) leads to a poor survival trend. This study aimed to determine the factors which influence overall survival (OS) and cancer-specific survival (CSS) of OCBM patients and to develop prognostic predictive models.

METHODS

Data of OCBM patients were stratified from the Surveillance, Epidemiology and End Results database from 2010 to 2017 and were randomly divided into training and testing datasets (7:3). Prognostic factors were identified by Cox regression analyses and nomograms were then developed. Nomogram models were examined on the discriminative ability and accuracy by calibration plots, Brier score (BS), and time-dependent receiver operating characteristic (ROC) curves. Decision curve analyses (DCA) was used for estimation of the clinical benefit of nomogram models.

RESULTS

Grade, tumor size, tumor metastasis (liver, lung), primary site surgery, chemotherapy, and systemic therapy were realized as independent prognostic factors for OS and CSS, respectively. Agreement between the actual and predicted outcomes was proved by calibration plots. Nomograms performed well in OS and CSS predictions, as shown by area under the ROC curves (AUCs) and BSs for testing dataset as follows: for OS, 3-/6-/12-month AUCs and BSs were 0.778/0.788/0.822 and 19.0/18.5/15.4, respectively; for CSS, 3-/6-/12-month AUCs and BSs were 0.799/0.806/0.832 and 18.1/18.0/15.4, respectively. DCA suggested an agreeable clinical benefit of both nomograms.

CONCLUSION

The nomograms developed for OCBM patients' survival prediction were proved to be accurate, efficient, and clinically beneficial, which were further deployed as web-based calculators to help in clinical decision making and future studies.

Significant Prognostic Factor at Age Cut-off of 73 Years for Advanced Ovarian Serous Cystadenocarcinoma Patients: Insights from Real-World Study

Objective

The objective of this research was to determine the age cut-off for worse prognosis and investigate age-related differentially expressed genes (DEGs) in patients with advanced ovarian serous cystadenocarcinoma (AOSC).

Methods

In this research, we included a cohort of 20,846 patients diagnosed with AOSC, along with RNA-seq data from 374 patients in publicly available databases. Then we used the X-tile software to determine the age cut-off and stratified the patients into young and old groups. We utilized propensity score matching (PSM) to balance baseline between the young and old groups. Furthermore, we conducted an enrichment analysis of DEGs between the two age groups using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology (GO) to identify dysregulated pathways. To evaluate the potential prognostic value of the DEGs, we performed survival analysis, such as Kaplan-Meier analysis and Log rank test.

Results

We stratified the patients into young group (n=16,336) and old group (n=4510) based on the cut-off age of 73 years by X-tile software. Age over 73 years was identified as an independent risk factor for overall survival (OS) and cancer-specific survival (CSS). Next, we identified 436 DEGs and found that the neurotrophin signaling pathway and translation factor activity were associated with prognosis outcomes. Among the top 10 hub genes (RELA, NFKBIA, TRAF6, IRAK2, TAB3, AKT1, TBP, EIF2S2, MAPK10, and SUPT3H), RELA, TAB3, AKT1, TBP, and SUPT3H were found to be significantly associated with poor prognosis in old patients with AOSC.

Conclusion

Our study determined 73 years as the cutoff value for age in patients with AOSC. RELA, TAB3, AKT1, TBP, and SUPT3H were identified as age-related DEGs that could contribute to the poor prognosis of older patients with AOSC.

Risk factors for the failure of first-line PARP inhibitor maintenance therapy in patients with advanced ovarian cancer: Gynecologic Oncology Research Investigators Collaboration Study (GORILLA-3004)

OBJECTIVE

To identify the risk factors for failure of first-line poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy in patients with advanced ovarian cancer.

METHOD

Patients with stage III-IV epithelial ovarian cancer who received first-line PARPi maintenance therapy were retrospectively reviewed. Clinicopathologic factors were compared between two groups-recur/progression of disease (PD) and non-recur/PD.

RESULTS

In total, 191 patients were included. Median follow-up was 9.9 months, and recurrence rate was 20.9%. BRCA mutations were found in 63.4% patients. Postoperative residual tumor (60.5% vs. 37.8%), non-high grade serous carcinoma (HGSC) (15.0% vs. 6.0%), neoadjuvant chemotherapy (NAC) (55.0% vs. 35.8%), and pre-PARPi serum CA-125 levels ≥23.5 U/mL (35.9% vs. 15.2%) were more frequently observed in the recur/PD group. Multivariate Cox-regression analysis revealed pre-PARPi serum CA-125 levels ≥23.5 U/mL (HR, 2.17; 95%CI, 1.03-4.57; p = 0.042), non-HGSC (3.28; 1.20-8.97; p = 0.021), NAC (2.11; 1.04-4.26; p = 0.037), and no BRCA mutation (2.23; 1.12-4.44; p = 0.023) as independent risk factors associated with poor progression-free survival (PFS). A subgroup analysis according to BRCA mutation status showed that pre-PARPi serum CA-125 levels ≥26.4 U/mL were the only independent risk factor for poor PFS in women with BRCA mutations (2.75; 1.03-7.39; p = 0.044). Non-HGSC (5.05; 1.80-14.18; p = 0.002) and NAC (3.36; 1.25-9.04; p = 0.016) were independent risk factors in women without BRCA mutations.

CONCLUSION

High pre-PARPi serum CA-125 levels, non-HGSC histology, NAC, and no BRCA mutation might be risk factors for early failure of first-line PARPi maintenance therapy. In women with BRCA mutations, high pre-PARPi serum CA-125 levels, which represent a large tumor burden before PARPi, were the only independent risk factor for poor PFS.

Feasibility of extended cycles of neoadjuvant chemotherapy in patients with advanced ovarian cancer in terms of prognosis and surgical outcomes

OBJECTIVE

We aimed to identify the effect of an extended number of neoadjuvant chemotherapy (NAC) cycles on prognosis and surgical morbidity after interval debulking surgery (IDS) in patients with newly diagnosed advanced ovarian cancer.

METHODS

Medical records of patients with advanced ovarian cancer treated with NAC and having undergone IDS were retrospectively reviewed. Clinicopathological factors were compared between two groups: conventional (≤4 cycles) and extended (≥5 cycles) NAC groups. Kaplan-Meier analysis was performed to evaluate progression-free survival (PFS) and overall survival (OS).

RESULTS

A total of 156 patients were included, 112 patients in the conventional group and 44 patients in the extended NAC group. The extended NAC group had a significantly higher frequency of cancer antigen (CA)-125 normalization after NAC (59.1% vs. 33.9%, P = 0.004), a lower rate of bowel surgery (18.2% vs. 34.8%, P = 0.042), and a lower rate of transfusion during or after IDS (36.4% vs. 59.8%, P = 0.008) as compared to the conventional group. The complete cytoreduction rate after IDS was similar between the groups. In multivariate Cox regression analysis for PFS, radiologically stable and progressive disease after NAC (Hazard ratio [HR], 1.983; 95% Confidence interval [CI], 1.141-3.446; P = 0.015) and gross residual tumor after IDS (HR, 2.054; 95% CI, 1.414-2.983; P < 0.001) were independent risk factors for poor PFS. However, extended NAC cycles were not significantly associated with poor PFS. The median PFS was 19.5 and 16.9 months (P = 0.830), and the 5-year OS was 71.4 and 63.2% (P = 0.677) in the conventional and extended NAC groups, respectively.

CONCLUSION

Our study showed that extended NAC cycles were not inferior to conventional NAC cycles in terms of survival in patients with advanced ovarian cancer and reduced surgical morbidity such as bowel surgery and transfusion during or after IDS.

A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer

Background

Liver metastasis (LM) is an independent risk factor that affects the prognosis of patients with ovarian cancer; however, there is still a lack of prediction. This study developed a limit gradient enhancement (XGBoost) to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer, thereby improving prediction efficiency. Patients and Methods. Data of patients diagnosed with ovarian cancer in the Surveillance, Epidemiology, and Final Results (SEER) database from 2010 to 2015 were retrospectively collected. The XGBoost algorithm was used to establish a lung metastasis model for patients with ovarian cancer. The performance of the predictive model was tested by the area under the curve (AUC) of the receiver operating characteristic curve (ROC).

Results

The results of the XGBoost algorithm showed that the top five important factors were age, laterality, histological type, grade, and marital status. XGBoost showed good discriminative ability, with an AUC of 0.843. Accuracy, sensitivity, and specificity were 0.982, 1.000, and 0.686, respectively.

Conclusion

This study is the first to develop a machine-learning-based prediction model for lung metastasis in patients with ovarian cancer. The prediction model based on the XGBoost algorithm has a higher accuracy rate than traditional logistic regression and can be used to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer.