https://orcid.org/0009-0006-4171-2858
Figures
Abstract
Background
To examine the trends in morbidity and mortality among ovarian cancer patients with liver metastases, and investigate the impact of different treatments on both overall survival (OS) and cancer-specific survival (CSS).
Methods
2,925 ovarian cancer patients with liver metastases from Surveillance, Epidemiology, and End Results 2010–2019 were included. The primary endpoint was considered as OS and CSS. We conducted trend analysis of the incidence, OS and CSS rates of liver metastases in ovarian cancer. Univariate and multivariate COX proportional risk models were used to investigate the association between different treatment methods and OS, and univariate and multivariate competing risk models were employed to evaluate the impact of treatment methods on CSS.
Results
At the end of follow-up, 689 patients remained alive. The OS and CSS rates were 76.44% and 72.99% for all patients, respectively. There was a significant decreasing trend in the incidence [average annual percent change (AAPC) = -2.3, 95% confidence interval (CI): -3.9, -0.7], all-cause mortality (AAPC = -12.8, 95% CI: -15.6, -9.9) and specific mortality (AAPC = -13.0, 95% CI: -16.1, -9.8) rate of liver metastases in ovarian cancer. After adjusting all confounding factor, only receiving surgery was associated with OS [hazard ratio (HR) = 0.39, 95%CI: 0.31–0.48]/CSS (HR = 0.37, 95%CI: 0.30–0.47). Chemotherapy was found to be protective factor for OS (HR = 0.33, 95%CI: 0.30–0.37)/CSS (HR = 0.44, 95%CI: 0.39–0.50) of ovarian cancer patients, while not receiving surgery remained a risk factor. Additionally, the result of subgroup analyses also showed that only receiving surgery and chemotherapy still were significant protective factor of OS and CSS for patients without other distant metastases, with distant metastases to the bone, lung, brain or other organs, with bone metastasis, and with lung metastasis.
Citation: Li N, Jin S, Wu J, Ji H, Du C, Liu B (2024) Effect of different treatment modalities on ovarian cancer patients with liver metastases: A retrospective cohort study based on SEER. PLoS ONE 19(4): e0299504. https://doi.org/10.1371/journal.pone.0299504
Editor: Federico Ferrari, University of Brescia: Universita degli Studi di Brescia, ITALY
Received: July 25, 2023; Accepted: February 9, 2024; Published: April 18, 2024
Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets used and/or analyzed during the current study are available at the https://github.com/apbnaliu/Effect-of-different-treatment-modalities-on-ovarian-cancer-patients-with-liver-metastases.
Funding: This study was supported by Jilin Provincial Science and Technology Department Fund (20190201089JC), Liaoning Provincial Science and Technology Talents and Natural Science Foundation (2021-MS-042) and Jilin Provincial Department of Education Fund (JJKH20190002KJ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Ovarian cancer is a prevalent gynecological malignancy and a leading cause of mortality in females [1]. Ovarian cancer possesses the potential to spread through tissue, lymph system, and blood [2]. Approximately 70% of ovarian cancer patients present with distant metastases at the time of diagnosis, resulting in an overall 5-year survival rate of less than 30% [3]. Epidemiological investigation indicates that the liver is the most frequent site of distant metastasis in ovarian cancer, followed by distant lymph nodes, lung, bone and brain [4]. The median survival time among patients with liver metastases was only 30 months [5], indicating a poor prognosis for ovarian cancer patients with liver metastases.
Previous studies have indicated that patients with advanced ovarian cancer who undergo debulking surgery have a more favorable prognosis compared to those who do not receive this treatment [6, 7]. The amount of residual disease following debulking surgery is a significant prognostic indicator for patients [8]. One study based on Surveillance, Epidemiology, and End Results (SEER) database showed that radiotherapy may be associated with a poorer prognosis in patients with primary ovarian cancer compared to those who do not receive radiation therapy [9]. In the study of Teckie et al., radiotherapy was considered an efficacious treatment for brain metastases of epithelial ovarian cancer [10]. In addition, prolonged delay in the initiation of adjuvant chemotherapy was associated with a decrease in overall survival (OS) rates for patients with advanced ovarian cancer [11]. However, to the best of our knowledge, the prognostic implications of various therapies on ovarian cancer patients with liver metastases remain unknown. It is imperative to investigate the optimal treatment for ovarian cancer patients with liver metastasis in order to enhance patient outcomes and alleviate disease burden.
Herein, the aim of this study was to examine the trends in morbidity and mortality among ovarian cancer patients with liver metastases using data from the SEER database, and to investigate the impact of different treatments on both OS and cancer-specific survival (CSS) in this population.
Methods
Population selection
This retrospective cohort study used data from the SEER database. The SEER database is a free access database, which compiles comprehensive information on cancer patients in the United States, encompassing demographics, tumor characteristics and details regarding mortality and survival rates [12, 13]. In this study, SEER*Stat software (version 8.4.0) was utilized to identify patients diagnosed with primary ovarian cancer from the SEER database 2010–2019.
The study population was required to meet the following inclusion criteria: (1) diagnosis of primary ovarian cancer based on International Classification of Diseases for Oncology codes (ICD-O-3); (2) age at diagnosis of 18 years or older; and (3) presence of liver metastases at the time of diagnosis. The exclusion criteria included: (1) patients with two or more primary cancers; (2) patients with missing information on surgery and follow-up. Finally, a total of 2,925 participants were included in this study. The process of selecting participants was illustrated in Fig 1. The requirement of ethical approval for this was waived by the Institutional Review Board of General Hospital of Northern Theater Command, because the data was accessed from SEER (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of General Hospital of Northern Theater Command due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.
Data collection
The primary endpoint of this study was considered as the OS and CSS among ovarian cancer patients with liver metastases. OS was defined as the time from diagnosis to death from any cause, while CSS was defined as the time from the date of diagnosis to death from liver metastasis of ovarian cancer [14]. The following variables were extracted from the SEER database: age, race/ethnicity, marital status, income, grade (I, II, III, IV or unknown), tumor size, local lymph node metastasis, histologic (carcinosarcoma, clear cell, endometrioid, malignant brenner carcinoma, mucinous, serous and other), combined bone metastasis, combined brain metastasis, combined lung metastasis, combined other sites metastasis, cancer antigen-125 (CA-125), tumor location (only one side, bilateral), residual tumor volume, surgery, radiotherapy, chemotherapy, treatment (receiving surgery and radiotherapy, only receiving surgery, only receiving radiotherapy, and none).
Statistical analysis
Continuous variables were presented as mean ± standard deviation (Mean ± SD, normal distribution) or median and quartile range [M (Q1, Q3), non-normal distribution]. Student’s t-test or Mann-Whitney U test was used for between-group comparisons. The categorical variable was reported as the number of cases and composition ratio n (%), and group differences were compared using the chi-square test. P-value of less than 0.05 was deemed statistically significant.
We conducted a trend analysis of the incidence, OS and CSS among ovarian cancer patients with liver metastases in the SEER database from 2010 to 2019. Univariate Cox proportional risk model was utilized to identify potential covariates associated with OS (P<0.05), while the univariate competing risk model was employed to identify potential covariates associated with CSS (P<0.05). Subsequently, OS served as the outcome variable and different treatment methods were considered as independent variables, two models were established to investigate the association between different treatment methods and OS in ovarian cancer patients with liver metastases. Model 1 was univariate Cox proportional risk model (unadjusted covariates). Model 2 was multivariate Cox proportional risk model (adjusted all potential covariates associated with OS). Similarly, to assess the impact of various treatment methods on CSS, we developed univariate and multivariate competing risk models with CSS as the outcome and different treatment methods as independent variables. Model 1 was univariate competing risk model (unadjusted covariates). Model 2 was multivariate competing risk model (adjusted all potential covariates associated with CSS). The OS and CSS were calculated using the Kaplan-Meier curve. Subgroup analyses were conducted based on patients with different distant metastases types. In addition, we also developed online nomograms to predict patients’ OS and CSS, respectively. The concordance index (C-index) was calculated to verify the predicting performance of two nomogram. The statistical analysis was conducted utilizing SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). R software was utilized for the computation of the average annual percent change (AAPC) and its corresponding 95% confidence interval (CI).
Results
Patients’ characteristics
A total of 2,925 ovarian cancer patients with liver metastases, with a mean age of 65.16 ± 13.49 years, were enrolled in this study. Most patients were white (78.46%). The median follow-up duration was 8.00 (1.00, 25.50) months, and at the conclusion of the follow-up period, a total of 689 patients remained alive. The OS and CSS rates were 76.44% and 72.99% for all patients, respectively. Detailed demographic and clinicopathological characteristics of all included patients were shown in Table 1.
The incidence and survival trends of liver metastases in ovarian cancer
Fig 2 illustrates trend in the incidence and mortality of liver metastases in ovarian cancer. The overall age-adjusted incidence rate of liver metastases in ovarian cancer was decreasing trend with an AAPC value of -2.3 (95%CI: -3.9, -0.7). Simultaneously, we conducted an analysis of the trends in OS and CSS among ovarian cancer patients with liver metastases. All-cause mortality and liver metastasis of ovarian cancer-specific mortality trends were declined, with AAPC value of -12.8 (95% CI: -15.6, -9.9) and -13.0 (95% CI: -16.1, -9.8), respectively. In addition, S1 Fig. also shows the incidence trend of liver metastases in ovarian cancer stratified by age, tumor grade, and treatment modality. Although there was no statistical significance in certain subgroups, all incidence rate trend showed a tendency to a decrease in liver metastases in ovarian cancer stratified by age, tumor grade, and treatment modality. S2 Fig. also demonstrates a declining trend in the all-cause mortality of liver metastases in ovarian cancer stratified by age, tumor grade, and treatment modality. Similar results were observed for specific mortality of liver metastases in ovarian cancer in different subgroups (S3 Fig).
Impact of different treatments on survival of liver metastases in ovarian cancer
Univariate Cox proportional risk and competing risk analyses demonstrated that age, race/ethnicity, marital status, income, grade, tumor size, local lymph node metastasis, histologic, combined bone metastasis, combined brain metastasis, combined lung metastasis, combined other sites metastasis, CA-125, residual tumor volume may be covariates associated with OS and CSS (P<0.05) (S1 Table). As shown in Table 2, after adjusting all covariates, ovarian cancer patients with liver metastases who only received surgery was associated with OS [Model 2: hazard ratio (HR) = 0.39, 95%CI: 0.31–0.48, P<0.001]. Using debulking as a reference, the absence of surgery was identified as a significant risk factor for OS (Model 1: HR = 4.23, 95%CI: 3.80–4.70, P<0.001; Model 2: HR = 2.66, 95%CI: 2.13–3.33, P<0.001), while the impact of other surgical methods on OS was not statistically significant (P>0.05). Also, chemotherapy was found to be a significant protective factor for OS of ovarian cancer patients with liver metastases, even after adjusting for variables (Model 2: HR = 0.33, 95%CI: 0.30–0.37, P<0.001). Similarly, after adjusting all covariates, in terms of CSS of ovarian cancer patients with liver metastases, only receiving surgery (Model 2: HR = 0.37, 95%CI: 0.30–0.47, P<0.001) and chemotherapy (Model 2: HR = 0.44, 95%CI: 0.39–0.50, P<0.001) were significant protective factor, while not receiving surgery (Model 2: HR = 2.68, 95%CI: 2.10–3.43, P<0.001) remained a risk factor. The Kaplan-Meier curve showed that only received surgery had a higher OS (S4A Fig) and CSS (S5A Fig). Surgical method also appeared to affect patient survival, with pelvic exenteration were found to have a higher OS (S4B Fig) and CSS than others (S5B Fig). Furthermore, patients who received radiotherapy may have lower OS (S4C Fig) and CSS (S5C Fig). The impact of chemotherapy on prognosis of patients with ovarian cancer was obvious, leading to significantly improved OS (S4D Fig) and CSS (S5D Fig) compared to those who do not receive this treatment.
Subgroup analyses based on patients with different distant metastases types
In order to better explore the effects of treatment modalities on OS and CSS in ovarian cancer patients with liver metastases who had different characteristics, we conducted a stratified analysis based on different distant metastases types. As shown in Table 3, we found that only receiving surgery and chemotherapy still were significant protective factor of OS and CSS for patients without other distant metastases (Subgroup Ⅰ), with distant metastases to the bone, lung, brain or other organs (Subgroup Ⅱ), with bone metastasis (Subgroup Ⅲ), and with lung metastasis (Subgroup Ⅳ). Additionally, for ovarian cancer patients with liver metastases who combined bone metastasis, surgery type, including oophorectomy with omentectomy (HR = 0.42, 95%CI: 0.21–0.84, P = 0.014), pelvic exenteration (HR = 0.24, 95%CI: 0.06–0.98, P = 0.047) and natural orifice surgery (HR = 3.15, 95%CI: 1.02–9.69, P = 0.046) was considered to be associated with CSS.
Establishment and validation of the nomogram
In univariate Cox proportional risk and competing risk analyses, age, race/ethnicity, marital status, income, grade, tumor size, local lymph node metastasis, histologic, combined bone metastasis, combined brain metastasis, combined lung metastasis, combined other sites metastasis, CA-125, and residual tumor volume were associated with both OS and CSS (P<0.05) (S1 Table). Thus, online OS (https://dynamic-nomogram-for-predicting-overall-survival—2023.shinyapps.io/DynNomapp/) and CSS (https://dynamic-nomogram-for-predicting-cancer-specific-survival—2023.shinyapps.io/DynNomapp/) predicting nomogram were established, respectively. C-index was used to verify the predicting performance of two nomogram (Table 4). Specifically, C-index of OS nomogram was 0.756 (95% CI: 0.746–0.766), and OS nomogram was 0.752 (95%CI: 0.742–0.763). These findings also indicated that two online nomograms had a good predicting value for OS and CSS in patients with ovarian cancer liver metastases.
Discussion
It is crucial to explore diverse treatment options for patients with liver metastases from ovarian cancer in order to improve patient prognosis. In this study, utilizing data from the SEER 2010–2019 registry, we examined the trends in morbidity and mortality among patients with liver metastases from ovarian cancer. Additionally, only receiving surgery and chemotherapy were also found to be significant protective factor for OS and CSS of patients.
Liver metastases are a frequent occurrence in patients diagnosed with ovarian cancer and are widely recognized as the primary cause of mortality associated with this disease [15]. The findings of this study revealed a noteworthy decrease in the incidence of liver metastases among ovarian cancer patients in the United States between 2010 and 2019. Additionally, there was a significant decreasing trend in the all-cause mortality/ cancer-specific mortality between 2010 and 2019. This could be attributed to timely intervention, and advancements in therapeutic approaches [16]. Although there was no statistical significance in certain subgroups, all incidence rate, all-cause mortality, cancer specific mortality trend showed a tendency to a decrease in ovarian cancer with liver metastases when stratified by age, tumor grade, and treatment modality.
Distant metastatic sites have a significant impact on the OS of patients with ovarian cancer that has spread [5]. Treatment strategies may vary depending on the specific sites of metastasis. Numerous therapeutic approaches for ovarian cancer with different sites of metastasis are currently under extensive investigation. For example, a review has demonstrated that whole brain radiotherapy (WBRT) is a viable and efficacious treatment modality for ovarian cancer patients with brain metastases [17]. In the study conducted by Cao et al., it was reported that chemotherapy and surgery were associated with lung metastases from ovarian cancer [18], and there was no significant difference in radiotherapy between ovarian cancer patients with or without lung metastasis. Therapeutic approaches play a crucial role in guiding clinical practice [19, 20]; however, the prognostic impact of various therapeutic strategies on patients with ovarian cancer liver metastases remains unclear to date. In our research, we incorporated more clinicopathological information. After adjusting for all potential confounding factors, the findings revealed that only receiving surgery were associated with OS and CSS of patients. A study of 72 patients with brain metastases from ovarian cancer showed that the combination of surgery and WBRT (median survival time: 23.07 months) resulted in superior survival outcomes compared to either surgery alone (median survival time: 6.90 months) or WBRT alone (median survival time: 5.33 months) [21]. Our study revealed no significant difference in the correlation between surgery combined with radiotherapy and OS/CSS among ovarian cancer patients with liver metastasis. Furthermore, in all subgroup analyses, only receiving surgery and chemotherapy also were beneficial for OS/CSS for patients. Similar to the findings in the general population, we observed no significant impact of different surgical method on patients’ OS and CSS in subgroups without other distant metastases (Subgroup Ⅰ), with distant metastases to the bone, lung, brain or other organs (Subgroup Ⅱ), and with lung metastasis (Subgroup Ⅳ) (P>0.05). Notably, when comparing to Debulking as a reference, the absence of surgery posed a risk factor for both OS and CSS within these specific subgroups. This observation may be attributed to the limited sample size of our study. It is worth noting that oophorectomy with omentectomy, pelvic exenteration and natural orifice surgery were related to CSS for ovarian cancer patients with liver metastases who combined bone metastasis. These findings may also imply that the type of surgery has an impact on the prognosis of diverse patients. Further prospective studies with larger sample size should be conducted to confirm our findings.
Additionally, this study also proposed two online dynamic nomogram to predict OS and CSS among ovarian cancer patients with liver metastasis, respectively. Overall, two model exhibited a good prediction performance. In clinical practice, clinicians can utilize these user-friendly dynamic nomograms to make early intervention decisions, which may improve prognoses of patients.
There are several limitations to this study. Firstly, the SEER database solely documented the metastases of liver, brain, lung, bone and distant lymph nodes; hence the specific metastases of other sites such as peritoneal metastases remain unknown [22]. Secondly, variables pertaining to comorbidities, types of chemotherapy administered and adjuvant agents as well as the sequence of treatment were not extracted form SEER database [23]. Thirdly, due to the limited sample size, it was not possible to conduct subgroup analysis on patients with distant metastases to the liver and brain. Fourthly, as mentioned by Forte S et al., liver resection is feasible during either primary debulking surgery (PDS) or interval debulking surgery (IDS) [24]. But all the data were obtained from the SEER public database, which prevented us from distinguishing between the sequencing of patients’ chemotherapy and surgery, as well as differentiating between PDS and IDS patients. Lastly, because the patient cohort in this study was limited to the US population, further validation of these findings is necessary across diverse populations worldwide.
Conclusion
In summary, our research has elucidated a downward trend in morbidity and mortality rates among patients with liver metastases originating from ovarian cancer. Only receiving surgery and chemotherapy were protective factor for OS and CSS of patients. The findings of this study provide valuable guidance for clinicians and patients in selecting optimal treatment modalities to enhance the prognosis of individuals with liver metastases from ovarian cancer.
Supporting information
S1 Fig.
The incidence trend of liver metastases in ovarian cancer stratified by (a) age, (b) tumor grade, (c) surgery, (d) radiotherapy and (e) chemotherapy.
https://doi.org/10.1371/journal.pone.0299504.s001
(PDF)
S2 Fig.
The all-cause mortality trend of liver metastases in ovarian cancer stratified by (a) age, (b) tumor grade, (c) surgery, (d) radiotherapy and (e) chemotherapy.
https://doi.org/10.1371/journal.pone.0299504.s002
(PDF)
S3 Fig.
The specific mortality trend of liver metastases in ovarian cancer stratified by (a) age, (b) tumor grade, (c) surgery, (d) radiotherapy and (e) chemotherapy.
https://doi.org/10.1371/journal.pone.0299504.s003
(PDF)
References
- 1. Armstrong DK, Alvarez RD, Backes FJ, Bakkum-Gamez JN, Barroilhet L, Behbakht K, et al. NCCN Guidelines® Insights: Ovarian Cancer, Version 3.2022. J Natl Compr Canc Netw. 2022;20(9):972–80. pmid:36075393
- 2.
Surveillance, Epidemiology, and End Results Program. Cancer Stat Facts: Ovarian Cancer. National Cancer Institute. https://seer.cancer.gov/statfacts/html/ovary.html.
- 3. Zhao H, Xu F, Li J, Ni M, Wu X. A Population-Based Study on Liver Metastases in Women With Newly Diagnosed Ovarian Cancer. Front Oncol. 2020;10:571671. pmid:33102229
- 4. Gardner AB, Charo LM, Mann AK, Kapp DS, Eskander RN, Chan JK. Ovarian, uterine, and cervical cancer patients with distant metastases at diagnosis: most common locations and outcomes. Clin Exp Metastasis. 2020;37(1):107–13. pmid:31758289
- 5. Deng K, Yang C, Tan Q, Song W, Lu M, Zhao W, et al. Sites of distant metastases and overall survival in ovarian cancer: A study of 1481 patients. Gynecol Oncol. 2018;150(3):460–5. pmid:30001833
- 6. Hudry D, Bécourt S, Scambia G, Fagotti A. Primary or Interval Debulking Surgery in Advanced Ovarian Cancer: a Personalized Decision-a Literature Review. Curr Oncol Rep. 2022;24(12):1661–8. pmid:35969358
- 7. Dabi Y, Huchon C, Ouldamer L, Bendifallah S, Collinet P, Bricou A, et al. Patients with stage IV epithelial ovarian cancer: understanding the determinants of survival. J Transl Med. 2020;18(1):134. pmid:32293460
- 8. Bryant A, Hiu S, Kunonga PT, Gajjar K, Craig D, Vale L, et al. Impact of residual disease as a prognostic factor for survival in women with advanced epithelial ovarian cancer after primary surgery. Cochrane Database Syst Rev. 2022;9(9):Cd015048. pmid:36161421
- 9. Yu L, Gong H, Li Q, Ren H, Wang Y, He H, et al. Survival Analysis of Radiation Therapy in Ovarian Cancer: A SEER Database Analysis. J Oncol. 2021;2021:8849039. pmid:33628244
- 10. Teckie S, Makker V, Tabar V, Alektiar K, Aghajanian C, Hensley M, et al. Radiation therapy for epithelial ovarian cancer brain metastases: clinical outcomes and predictors of survival. Radiat Oncol. 2013;8:36. pmid:23414446
- 11. Liu Y, Zhang T, Wu Q, Jiao Y, Gong T, Ma X, et al. Relationship between initiation time of adjuvant chemotherapy and survival in ovarian cancer patients: a dose-response meta-analysis of cohort studies. Sci Rep. 2017;7(1):9461. pmid:28842667
- 12. Zhu Y, Fang X, Wang L, Zhang T, Yu D. A Predictive Nomogram for Early Death of Metastatic Gastric Cancer: A Retrospective Study in the SEER Database and China. J Cancer. 2020;11(18):5527–35. pmid:32742500
- 13. Jia Z, Yan Y, Wang J, Yang H, Zhan H, Chen Q, et al. Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database. Cancer Med. 2021;10(17):6140–8. pmid:34342153
- 14. Gao J, Wang S, Li F, Xu H, Li X, Yan L, et al. Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Young Patients with Epithelial Ovarian Cancer: Analysis Based on SEER Program. Adv Ther. 2022;39(1):257–85. pmid:34716558
- 15. Yuan Y, Wang R, Guo F, Zhang Y, Wang H, Li X, et al. A clinical model to predict the risk of liver metastases in newly diagnosed ovarian cancer: a population-based study. Transl Cancer Res. 2020;9(11):7044–53. pmid:35117310
- 16. Menon U, Karpinskyj C, Gentry-Maharaj A. Ovarian Cancer Prevention and Screening. Obstet Gynecol. 2018;131(5):909–27. pmid:29630008
- 17. Borella F, Bertero L, Morrone A, Gambella A, Bovetti M, Cosma S, et al. Brain Metastases from Ovarian Cancer: Current Evidence in Diagnosis, Treatment, and Prognosis. Cancers (Basel). 2020;12(8). pmid:32759682
- 18. Cao C, Yang X. The Prevalence, Associated Factors for Lung Metastases Development and Prognosis in Ovarian Serous Cancer Based on SEER Database. Technol Cancer Res Treat. 2020;19:1533033820983801. pmid:33356997
- 19. Chandra A, Pius C, Nabeel M, Nair M, Vishwanatha JK, Ahmad S, et al. Ovarian cancer: Current status and strategies for improving therapeutic outcomes. Cancer Med. 2019;8(16):7018–31. pmid:31560828
- 20. Akter S, Rahman MA, Hasan MN, Akhter H, Noor P, Islam R, et al. Recent Advances in Ovarian Cancer: Therapeutic Strategies, Potential Biomarkers, and Technological Improvements. Cells. 2022;11(4). pmid:35203301
- 21. Cohen ZR, Suki D, Weinberg JS, Marmor E, Lang FF, Gershenson DM, et al. Brain metastases in patients with ovarian carcinoma: prognostic factors and outcome. J Neurooncol. 2004;66(3):313–25. pmid:15015663
- 22. Nougaret S, Sadowski E, Lakhman Y, Rousset P, Lahaye M, Worley M, et al. The BUMPy road of peritoneal metastases in ovarian cancer. Diagn Interv Imaging. 2022;103(10):448–59. pmid:36155744
- 23. Wang Y, Shan X, Dong H, Li M, Yue Y. Prediction for 2-year mortality of metastatic ovarian cancer patients based on surveillance, epidemiology, and end results database. Front Surg. 2022;9:974536. pmid:36338661
- 24. Forte S, Ferrari F, Valenti G, Capozzi VA, Santana BN, Babin G, Guyon F. Liver surgery for advanced ovarian cancer: a systematic review of literature. Eur. J. Gynaecol. Oncol. 2022; 43(1): 64–72. http://doi.org/10.31083/j.ejgo4301015