Prognostic significance of chemotherapy response score in predicting outcomes for high-grade serous ovarian carcinoma patients undergoing neoadjuvant chemotherapy
Highlight box
Key findings
• We constructed a prognostic model incorporating multiple clinical and pathologic factors [debulking status, chemotherapy response score (CRS), and post-adjuvant chemotherapy cancer antigen 125 (PACT-CA125)] and evaluated their impact on the prognosis of high-grade serous ovarian cancer (HGSOC) patients.
• We performed prognostic analysis on differentially expressed genes using univariate Cox analysis and log-rank test. Additionally, propensity score matching was employed to further validate the impact of different subgroups on prognosis, thereby enhancing the reliability of the genes in the prognostic model.
What is known and what is new?
• Growing evidence suggests that CRS plays a significant role in assessing the response and prognosis of HGSOC patients after neoadjuvant chemotherapy (NACT).
• We first developed a prognostic model combining multiple clinical and pathological factors, including debulking status, CRS, and PACT-CA125, to predict the prognosis of HGSOC patients.
What is the implication, and what should change now?
• In conclusion, our study developed a predictive model, validated the CRS system’s reliability, highlighted PACT-CA125’s potential for monitoring, and proposed the nomogram for guiding NACT decisions and patient stratification in HGSOC patients.
Introduction
Ovarian cancer is the most lethal gynecological malignancy, with most patients being diagnosed at an advanced stage (1,2). High-grade serous ovarian cancer (HGSOC) is the most common subtype, accounting for approximately 75% of all ovarian cancer cases and related deaths (3). The standard treatment for advanced HGSOC includes primary debulking surgery (PDS) followed by platinum-based chemotherapy. In clinical practice, neoadjuvant chemotherapy (NACT) is recommended for patients with high perioperative risks or when optimal debulking is unlikely to be achieved (4,5). Therefore, there is an urgent need to predict survival outcomes in HGSOC patients undergoing NACT based on clinical data.
Reported prognostic factors for ovarian cancer include proteomics, specific tumor biomarkers, expression of selected genes, and associated clinical characteristics (6-8). Compared to gene sequencing or proteomics, clinical features provide a more accessible means for initial patient assessment. Currently, an increasing number of researchers are developing clinical prognostic models to predict outcomes and guide clinical management (9-11). Nomograms have been validated in numerous studies as reliable and effective clinical predictive tools that use clinical features and risk factors to estimate patient progression-free survival (PFS). Recently, Liu et al. (12) developed a nomogram by identifying relevant genetic features to predict the prognosis of individual ovarian cancer patients.
Although studies have shown that the chemotherapy response score (CRS) can evaluate the response of HGSOC patients after NACT, there is limited research on the prognostic impact of multiple factors in patients undergoing interval debulking surgery (IDS) post-NACT (12,13). This study aims to identify independent prognostic factors in HGSOC patients and assess their combined predictive value using a nomogram, with the goal of identifying the subgroup of HGSOC patients who may benefit most from NACT. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1654/rc).
Methods
Patient population
Electronic medical records of HGOSC patients treated with NACT followed by IDS between 2006 and 2021 at Fujian Cancer Hospital were retrospectively analyzed. Female patients aged 18 to 70 years with histologically confirmed International Federation of Gynecology and Obstetrics (FIGO) 2018 stage III or IV HGSOC were eligible for the study, who received 2–4 cycles of platinum-based NACT before IDS and had at least six months of follow-up data. Exclusion criteria included the following: (I) history of other malignancies within the last five years; (II) incomplete study data on major predictors. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was reviewed and approved by the Institutional Review Board and Ethics Committee of Fujian Cancer Hospital (No. K2023-048-01). Written informed consent from participants was not required in accordance with local guidelines.
The following variables of eligible patients were collected: age and cancer antigen 125 (CA125) at diagnosis, stage, histology of the disease, the cycle of NACT, residual disease at IDS, preoperative and postoperative CA125, post-adjuvant chemotherapy cancer antigen 125 (PACT-CA125), the CRS, PFS. PFS was defined as the duration from the beginning of treatment to the diagnosis of disease progression. All clinicopathological characteristics of patients with HGSOC were acquired from medical records.
Pathology review
A histological review was performed for all cases. The slide with the most viable tumor or the least chemotherapy response was selected from the adnexal. The resected specimens were formalin-fixed, paraffin-embedded, and stained with hematoxylin and eosin (H&E) according to the standard protocols at the Department of Pathology, Fujian Cancer Hospital. Adnexal samples were evaluated by more than two senior gynecologic pathologists to assess tumor response by CRS. Typical histopathological images for each CRS categorization are shown in Figure 1.
Variable selection and nomogram construction
We used R language to randomly divide the dataset into training and validation groups. In the training group, univariate analysis was performed on all variables, including 5 continuous and 9 categorical variables. Variables with P<0.05 from the univariate analysis were included in the multivariate analysis, and those with P<0.05 in the multivariate analysis were identified as independent prognostic factors. These independent prognostic factors were then used to construct a nomogram, and risk scores for each patient were calculated. Finally, the optimal cutoff was determined to develop a risk scoring system. The impact of adjuvant chemotherapy (ACT) on the prognosis of patients with HGSOC in distinct risk groups was further analyzed using propensity score matching (PSM) for 1:1 matching to mitigate selection and confounding biases.
Statistical analysis
Wilcoxon was applied for continuous variables, and Chi-squared test (n>5 in either cohort) or Yates’ correction tests (n<5 in either cohort) for categorical variables, with P>0.05 indicating no difference between groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and concordance index (C-index) were used to assess the model’s predictive performance. Decision curve analysis (DCA) plots can be used to evaluate clinical utility. All analyses were implemented with R software (version 4.2.0). All P<0.05 was considered statistically significant.
Results
Baseline characteristics
Based on the inclusion and exclusion criteria, we enrolled a total of 271 HGSOC patients who were divided into the training cohort (n=190) and the validation cohort (n=81). The clinicopathological features are shown in Table 1. The median PFS (mPFS) was 20.6 [interquartile range (IQR), 19.2–27.2] months in the training cohort and 22.5 (IQR, 18.6–30.9) months in the validation cohort. The median follow-up was 43.5 (95% CI: 34.2–51.2) and 41.2 (95% CI: 34.0–72.7) months, respectively.
Table 1
| Characteristics | Train (N=190) | Validation (N=81) | P value |
|---|---|---|---|
| Age (years) | 52.0 (47.0, 59.0) | 51.0 (46.0, 56.0) | 0.31† |
| Duration of surgery (minutes) | 180 (150, 224) | 180 (150, 230) | 0.95† |
| Albumin level at diagnosis (g/dL) | 37 (33.4, 40.0) | 36.9 (33.3, 39.9) | 0.85† |
| Serum CA125 at diagnosis (IU/mL) | 1,513 (598.5, 2,948) | 1,414.8 (634, 2,614) | 0.80† |
| Preoperative serum CA125 (IU/mL) | 41 (18.0, 159.5) | 37 (20.0, 165.0) | 0.96† |
| Menopause | 0.50‡ | ||
| No | 83 (43.7) | 39 (48.1) | |
| Yes | 107 (56.3) | 42 (51.9) | |
| Multipara | 0.79§ | ||
| Yes | 185 (97.4) | 80 (98.8) | |
| No | 5 (2.6) | 1 (1.2) | |
| Complications¶ | 0.91‡ | ||
| No | 149 (78.4) | 64 (79.0) | |
| Yes | 41 (21.6) | 17 (21.0) | |
| Cycle of NACT | 0.64‡ | ||
| ≤4 | 170 (89.5) | 74 (91.4) | |
| >4 | 20 (10.5) | 7 (8.6) | |
| Stage | 0.91‡ | ||
| III | 123 (64.7) | 53 (65.4) | |
| IV | 67 (35.3) | 28 (34.6) | |
| Residual disease at IDS | 0.56c | ||
| R0 | 95 (50.0) | 46 (56.8) | |
| R1 | 91 (47.9) | 34 (42.0) | |
| R2 | 4 (2.1) | 1 (1.2) | |
| Cycle of ACT | 0.02‡ | ||
| ≤3 | 45 (23.7) | 9 (11.1) | |
| >3 | 145 (76.3) | 72 (88.9) | |
| PACT-CA125 | 0.27‡ | ||
| ≤16.5 IU/mL | 123 (64.7) | 58 (71.6) | |
| >16.5 IU/mL | 67 (35.3) | 23 (28.4) | |
| CRS in adnexa | 0.26‡ | ||
| CRS 2 | 76 (40.0) | 28 (34.6) | |
| CRS 3 | 45 (23.7) | 15 (18.5) | |
| CRS 1 | 69 (36.3) | 38 (46.9) |
Data are presented as median (IQR) or n (%). †, Wilcoxon; ‡, Chi-squared test; §, Yates’ correction tests; ¶, hypertension or diabetes. ACT, adjuvant chemotherapy; CA125, cancer antigen 125; CRS, chemotherapy response score; IDS, interval debulking surgery; IQR, interquartile range; NACT, neoadjuvant chemotherapy; PACT-CA125, post-adjuvant chemotherapy CA125.
Prognostic factors selection and nomogram construction
In the Kaplan-Meier survival curve analysis, apart from FIGO stage (P=0.67), residual disease at IDS, CRS, and PACT-CA125 all had significant impacts on PFS (P<0.001) (Figure 2). There was no significant survival difference between patients with FIGO stage III and IV diseases (P=0.67) (Figure 2A). Table 2 shows the results of univariate and multivariate Cox regression analyses. In multivariate Cox regression analysis, PACT-CA125, residual disease at IDS, and CRS were independent prognostic factors for PFS in HGSOC. All three variables were independent prognostic factors in multivariate analysis (P<0.05) and were used to construct the nomogram (Figure 3).
Table 2
| Characteristics | Univariate analysis | Multivariate analysis | |||
|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | ||
| Age | 0.990 (0.968–1.012) | 0.37 | – | – | |
| Albumin level at diagnosis | 0.971 (0.937–1.006) | 0.11 | – | – | |
| Duration of surgery | 1.001 (0.998–1.004) | 0.41 | – | – | |
| Serum CA125 at diagnosis | 1.000 (1.000–1.000) | 0.51 | – | – | |
| Preoperative serum CA125 | 1.000 (1.000–1.001) | 0.26 | – | – | |
| Menopause | |||||
| No | Reference | – | – | ||
| Yes | 0.914 (0.640–1.307) | 0.62 | – | – | |
| Multipara | |||||
| Yes | Reference | – | – | ||
| No | 1.130 (0.417–3.067) | 0.81 | – | – | |
| Complications | |||||
| No | Reference | – | – | ||
| Yes | 1.145 (0.754–1.740) | 0.53 | – | – | |
| Cycle of NACT | |||||
| ≤4 | Reference | Reference | |||
| >4 | 0.393 (0.183–0.844) | 0.02 | 0.530 (0.240–1.169) | 0.12 | |
| Stage | |||||
| III | Reference | – | – | ||
| IV | 0.921 (0.629–1.349) | 0.67 | – | – | |
| Residual disease at IDS | |||||
| R0 | Reference | Reference | |||
| R1 | 2.109 (1.459–3.050) | <0.001 | 1.725 (1.160–2.564) | 0.007 | |
| R2 | 4.771 (1.709–13.322) | 0.003 | 3.503 (1.224–10.020) | 0.02 | |
| Cycle of ACT | |||||
| ≤3 | Reference | Reference | |||
| >3 | 0.577 (0.386–0.863) | 0.007 | 0.678 (0.433–1.060) | 0.09 | |
| PACT-CA125 | |||||
| ≤16.5 IU/mL | Reference | Reference | |||
| >16.5 IU/mL | 2.959 (2.061–4.248) | <0.001 | 2.311 (1.558–3.429) | <0.001 | |
| CRS in adnexa | |||||
| CRS 2 | Reference | Reference | |||
| CRS 3 | 0.522 (0.310–0.881) | 0.02 | 0.801 (0.530–1.210) | 0.29 | |
| CRS 1 | 1.575 (1.064–2.332) | 0.02 | 0.548 (0.826–0.954) | 0.03 | |
ACT, adjuvant chemotherapy; CA125, cancer antigen 125; CI, confidence interval; CRS, chemotherapy response score; HGSOC, high-grade serous ovarian cancer; HR, hazard ratio; IDS, interval debulking surgery; NACT, neoadjuvant chemotherapy; PACT-CA125, post-adjuvant chemotherapy CA125.
Performance and validation of the nomogram
The nomogram demonstrated a strong predictive ability with a C-index of 0.735 (95% CI: 0.683–0.788) in the training cohort and 0.730 (95% CI: 0.652–0.807) in the validation cohort. The AUC values of the 1-, 2-, and 3-year PFS nomogram were 0.776, 0.754, and 0.790 in the training cohort (Figure 4A) and 0.795, 0.726 and 0.704 in the validation cohort, respectively (Figure 4B). In both training and validation cohort, the survival probability predicted by the nomogram corresponded well with actual observations (Figure 4C,4D). Furthermore, DCA curves exhibited the value of the nomogram in clinical application (Figure 5).
Risk classification system
Based on the total score from the nomogram, the optimal cutoff value was identified in the training cohort, allowing the validation cohort to be divided into two subgroups. Kaplan-Meier curves for PFS showed significant differentiation (P<0.001) between the risk groups in both training and validation cohorts (Figure 6A,6B). Residual disease at IDS, CRS, and PACT-CA125 were used as matching factors for the low- and high-risk groups by PSM 1:1 matching with a tolerance of 0.1. Except for the cycles of ACT, all other prognostic factors between the two groups showed no statistically significant differences (P>0.05). A total of 33 cases were matched in 51 cases in the low-risk group and the high-risk group. In the high-risk group, the number of ACT cycles did not significantly impact PFS (P=0.22) (Figure 6C). However, in the low-risk group, patients who received more than 3 cycles of ACT had significantly longer PFS compared to those who received 3 or fewer cycles (P<0.01) (Figure 6D).
Discussion
Many factors are associated with the prognosis of HGSOC, and it is crucial to identify independent factors associated with the prognosis (14). Our study constructed a nomogram to predict 1-, 2-, and 3-year PFS of patients, which evaluated the roles of desiccation status, CRS, and PACT-CA125 levels in prognosis. In addition, compared with previous similar studies, we included not only denervation status, which is widely used to predict survival, but also CRS and PACT-CA125 levels as predictive variables, which could assist clinicians in choosing suitable treatment strategies for these patients.
Complete resection of all macroscopic lesions during PDS is a crucial independent prognostic factor for advanced ovarian cancer (15-18). However, studies suggest that the rate of complete resection is lower after NACT compared to primary surgery, potentially due to fibrosis caused by chemotherapy, which complicates the removal of gross residual disease (5,13). Nevertheless, achieving cytoreduction to less than 1 cm at IDS has been demonstrated to retain prognostic significance. In our study, multivariate and survival analyses confirmed the importance of this factor, demonstrating that complete debulking is associated with longer PFS. However, no significant difference was observed between R0/R1 and R2 resection, likely due to the small number of patients with residual disease greater than 1 cm. Complete surgical resection is ultimately a subjective factor, primarily influenced by the surgeon or surgical conditions, among other factors. Therefore, we combined CRS with other factors in our study to explore whether there is a better combination of clinical characteristics to predict prognosis.
According to reports, adnexal CRS of the omentum has a significant role in predicting prognosis (19-22). Santoro et al. (22) validated the CRS system and demonstrated that prognostic categorization of patients based on the adnexal CRS was statistically significant. Lawson et al. (23) observed that the ovaries act as the reservoir of drug-resistant clones and that measuring the extent of residual tumors in the adnexal site could provide vital prognostic information for HGSOC patients following NACT. Since laparoscopic peritoneal biopsies are not routinely performed before surgery, there is a risk of misjudging peritoneal metastases. Compared to clinical and radiological evaluations, histopathological scoring of tumor tissue after IDS specimens is more likely to be a reliable, accurate, and repeatable measurement to predict the clinical outcome of the patients. Our findings illustrate the prognostic value of the adnexal CRS system and its potential for stratifying patients after NACT following IDS. The importance of CRS in NACT has been demonstrated, but its weight in the prognosis model remains unexplored. The nomogram developed in this study revealed that CRS is a critical prognostic factor, with notable associations with PFS and other clinical characteristics.
Studies have reported that CA125 levels are closely associated with the prognosis of ovarian cancer patients (24-26). The GOG-252 study (27) demonstrated that CA125 levels can serve as a prognostic indicator for ovarian cancer, with proportional risk models indicating that patients with CA125 >10 IU/mL have a 38.5% higher risk of death. In our study, we found that patients with serum PACT-CA125 levels below 16.5 IU/mL had better PFS compared to those with levels above 16.5 IU/mL (P<0.001). Measurement of CA125 levels after the completion of ACT is considered a valuable marker for detecting disease progression and was included in the nomogram that we developed. Patients who achieve significant reductions in CA125 levels after ACT may have a similar prognosis with fewer cycles of postoperative chemotherapy.
A reliable and precise predictive model helps identify high-risk patients, enabling precision medicine to avoid overtreatment or undertreatment. In this study, we identified the clinical characteristics most impacting the prognosis of HGSOC patients: menopausal status, CRS, and CA125 levels after ACT, and combined them to develop a novel nomogram. Previous studies have shown that complete surgical resection and staging at diagnosis are important prognostic factors. To explore whether there are more objective indicators for evaluating advanced ovarian cancer patients, we included CRS and CA125 levels after ACT in our study and revealed their relationship with PFS. Additionally, we developed a risk classification system based on the nomogram, stratifying patients into high-risk and low-risk groups. In the risk system, receiving more cycles of ACT did not yield more favorable treatment outcomes in the low-risk group, while in the high-risk group, additional cycles of chemotherapy had no significant impact on prognosis.
There are several limitations in this study. Firstly, as a retrospective study, it is susceptible to information bias. All patients included were from a single hospital, which may result in selection bias. Secondly, the enrollment period for patients was relatively long, and the overall survival data for some patients could not be assessed. Thirdly, we were unable to validate the model with external data.
Conclusions
In conclusion, our study explored the association between relevant clinical features and prognosis and developed a predictive model. We also reaffirmed the repeatability, validity, and reliability of the CRS system based on adnexal sites in HGSOC cases following NACT. Furthermore, PACT-CA125 appears to be a valuable marker for detecting disease progression after the completion of NACT. We anticipate that the nomogram will facilitate decision-making regarding NACT for HGSOC patients and be utilized in clinical practice to stratify patients effectively.
Acknowledgments
We appreciate all participants and the contributors in this study.
Footnote
Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1654/rc
Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1654/dss
Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1654/prf
Funding: The project was funded by the grants of Joint Funds for
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1654/coif). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study protocol was reviewed and approved by the Institutional Review Board and Ethics Committee of Fujian Cancer Hospital, approval number (No. K2023-048-01). Written informed consent from participants was not required in accordance with local guidelines.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
References
- Colombo N, Sessa C, du Bois A, et al. ESMO-ESGO consensus conference recommendations on ovarian cancer: pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease†. Ann Oncol 2019;30:672-705. [Crossref] [PubMed]
- Chen P, Zheng S, Zhang L. Nomogram for predicting the early death of patients with stage IV ovarian cancer: a retrospective analysis of the SEER database. Transl Cancer Res 2024;13:5845-55. [Crossref] [PubMed]
- Buys SS, Partridge E, Greene MH, et al. Ovarian cancer screening in the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial: findings from the initial screen of a randomized trial. Am J Obstet Gynecol 2005;193:1630-9. [Crossref] [PubMed]
- Horowitz NS, Miller A, Rungruang B, et al. Does aggressive surgery improve outcomes? Interaction between preoperative disease burden and complex surgery in patients with advanced-stage ovarian cancer: an analysis of GOG 182. J Clin Oncol 2015;33:937-43. [Crossref] [PubMed]
- Wright AA, Bohlke K, Armstrong DK, et al. Neoadjuvant Chemotherapy for Newly Diagnosed, Advanced Ovarian Cancer: Society of Gynecologic Oncology and American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol 2016;34:3460-73. [Crossref] [PubMed]
- El Bairi K, Kandhro AH, Gouri A, et al. Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer. Cell Oncol (Dordr) 2017;40:105-18. [Crossref] [PubMed]
- Gao B, Zhao X, Gu P, et al. A nomogram model based on clinical markers for predicting malignancy of ovarian tumors. Front Endocrinol (Lausanne) 2022;13:963559. [Crossref] [PubMed]
- Wang T, Fu X, Zhang L, et al. Prognostic Factors and a Predictive Nomogram of Cancer-Specific Survival of Epithelial Ovarian Cancer Patients with Pelvic Exenteration Treatment. Int J Clin Pract 2023;2023:9219067. [Crossref] [PubMed]
- Lee CK, Asher R, Friedlander M, et al. Development and validation of a prognostic nomogram for overall survival in patients with platinum-resistant ovarian cancer treated with chemotherapy. Eur J Cancer 2019;117:99-106. [Crossref] [PubMed]
- Huang B, Wu FC, Wang WD, et al. The prognosis of breast cancer patients with bone metastasis could be potentially estimated based on blood routine test and biochemical examination at admission. Ann Med 2023;55:2231342. [Crossref] [PubMed]
- Sun YC, Zhao ZD, Yao N, et al. Risk prediction of second primary malignancies in patients after rectal cancer: analysis based on SEER Program. BMC Gastroenterol 2023;23:354. [Crossref] [PubMed]
- Liu L, Zhao J, Du X, et al. Construction and validation of a novel aging-related gene signature and prognostic nomogram for predicting the overall survival in ovarian cancer. Cancer Med 2021;10:9097-114. [Crossref] [PubMed]
- Ivantsov AO. Pathological response of ovarian cancer to neoadjuvant chemotherapy. Chin Clin Oncol 2018;7:59. [Crossref] [PubMed]
- Jones TN, Shih IM, Wang TL. Multiomic characterization of high-grade serous ovarian carcinoma: editorial commentary on recent application and consideration for future directions. Transl Cancer Res 2023;12:1368-71. [Crossref] [PubMed]
- Bryant A, Hiu S, Kunonga PT, 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:CD015048. [PubMed]
- Petrillo M, Zannoni GF, Tortorella L, et al. Prognostic role and predictors of complete pathologic response to neoadjuvant chemotherapy in primary unresectable ovarian cancer. Am J Obstet Gynecol 2014;211:632.e1-8. [Crossref] [PubMed]
- Timmermans M, van der Hel O, Sonke GS, et al. The prognostic value of residual disease after neoadjuvant chemotherapy in advanced ovarian cancer; A systematic review. Gynecol Oncol 2019;153:445-51. [Crossref] [PubMed]
- Rajkumar S, Polson A, Nath R, et al. Prognostic implications of histological tumor regression (Böhm's score) in patients receiving neoadjuvant chemotherapy for high grade serous tubal & ovarian carcinoma. Gynecol Oncol 2018;151:264-8. [Crossref] [PubMed]
- Cohen PA, Powell A, Böhm S, et al. Pathological chemotherapy response score is prognostic in tubo-ovarian high-grade serous carcinoma: A systematic review and meta-analysis of individual patient data. Gynecol Oncol 2019;154:441-8. [Crossref] [PubMed]
- Nero C, Fagotti A, Zannoni GF, et al. Pathologic response to neoadjuvant chemotherapy in advanced ovarian cancer: utility of a scoring system to predict outcomes. Int J Gynecol Cancer 2019;29:1064-71. [Crossref] [PubMed]
- Feng P, Chen T, Wischhusen J, et al. The diagnostic performance of the Mindray system in detecting CA125 and HE4 for patients with ovarian cancer. Transl Cancer Res 2024;13:4474-84. [Crossref] [PubMed]
- Santoro A, Angelico G, Piermattei A, et al. Pathological Chemotherapy Response Score in Patients Affected by High Grade Serous Ovarian Carcinoma: The Prognostic Role of Omental and Ovarian Residual Disease. Front Oncol 2019;9:778. [Crossref] [PubMed]
- Lawson BC, Euscher ED, Bassett RL, et al. A 3-Tier Chemotherapy Response Score for Ovarian/Fallopian Tube/Peritoneal High-grade Serous Carcinoma: Is it Clinically Relevant? Am J Surg Pathol 2020;44:206-13. [Crossref] [PubMed]
- Feng LY, Liao SB, Li L. Preoperative serum levels of HE4 and CA125 predict primary optimal cytoreduction in advanced epithelial ovarian cancer: a preliminary model study. J Ovarian Res 2020;13:17. [Crossref] [PubMed]
- Charkhchi P, Cybulski C, Gronwald J, et al. CA125 and Ovarian Cancer: A Comprehensive Review. Cancers (Basel) 2020;12:3730. [Crossref] [PubMed]
- Zhang M, Cheng S, Jin Y, et al. Roles of CA125 in diagnosis, prediction, and oncogenesis of ovarian cancer. Biochim Biophys Acta Rev Cancer 2021;1875:188503. [Crossref] [PubMed]
- Walker JL, Brady MF, Wenzel L, et al. Randomized Trial of Intravenous Versus Intraperitoneal Chemotherapy Plus Bevacizumab in Advanced Ovarian Carcinoma: An NRG Oncology/Gynecologic Oncology Group Study. J Clin Oncol 2019;37:1380-90. [Crossref] [PubMed]
