OviR0: A novel application for assessing the feasibility of complete cytoreduction and hyperthermic intraperitoneal chemotherapy in epithelial ovarian cancer following neoadjuvant chemotherapy

INTRODUCTION

High-grade serous epithelial ovarian carcinomas generally present late in advanced stages International Federation of Gynecology and Obstetrics (FIGO Stage IIIC onwards), pose a high morbidity to patients, and affect the quality of life.

Ascites, vague abdominal pain, and distension of the abdomen are the predominant complaints. Due to the redistribution phenomenon leading to extensive intra-abdominal and intrathoracic disease, primary cytoreduction is often considered to be difficult, and most patients are unlikely to benefit from upfront cytoreductive surgery if complete cytoreduction (CC-0/CC-1) is not possible. Before being considered for Interval Cytoreduction, individuals often undergo Neoadjuvant Chemotherapy (NACT). Randomized trials including EORTC 55971, CHORUS, JCOG0602, and SCORPION demonstrated comparable survival outcomes between primary debulking surgery (PDS) and NACT followed by interval debulking surgery (IDS), with lower perioperative morbidity in the NACT group. The recent CHRONO trial compares the outcomes of 3 to 6 cycles of Neoadjuvant Chemotherapy. CC-0/CC-1 remains the most important prognostic factor. When combined with cytoreduction, Hyperthermic intraperitoneal chemotherapy (HIPEC) can help destroy any remaining tumor, but it is only feasible after cytoreduction is complete. This dilemma raises questions about the true benefit of extensive cytoreductive surgery, particularly in view of its potential morbidity and the significant financial burden associated with HIPEC consumables.^[1-10] There is a dearth of research from the Indian Subcontinent on this subject. This study thus aims to develop a predictive model, a nomogram, and a calculator application that predicts the possibility of achieving CC-0/CC-1 after Neoadjuvant Chemotherapy and thus guide treatment decisions.

MATERIAL AND METHODS

This single-center hospital record-based retrospective study included data from 61 female patients previously treated in our tertiary care institute from January 2021 to January 2024. The study was approved by the Institutional Ethical Committee and carried out in compliance with the guidelines provided in the Helsinki Declaration. Written and informed consent was taken from all the participants and their legal guardians for the utilization of their data for this analysis. The patients were included in the study using the following criteria:

RESULTS

This study included 61 participants with an age of 55±4.678 years (Mean±SD). 65.6% of the individuals had one comorbidity, 34.4% had none, and 6.6% had two or more. The majority of patients were diagnosed at stage III (65.6%), with stage II (27.9%) and stage IVA (6.6%) following. The mean CA-125 level after treatment was 95.97 (standard deviation [ SD] ± 83.28). 82.0% of individuals received three cycles of neoadjuvant Paclitaxel and Carboplatin treatment, whereas 18% experienced more than three cycles [Table 1].

Total parietal peritonectomy was performed during interval cytoreduction in all the patients. 82.0% of individuals underwent bilateral pelvic lymph node dissection, while 18% had both pelvic and paraaortic lymph node dissection. Multi-visceral resection was performed in 11.5% of patients. CC-0/ CC-1 was achieved in 86.9% of the patients after surgery, whereas 13.1% did not achieve CC-0/CC-1 and thus HIPEC was not administered in these patients [Table 2].

Univariate regression analysis was done, which explored the association between the predictor factors and the outcome of CC-0/CC-1 . Age (OR 1.363, p = 0.015), number of comorbidities (OR 2.068, p = 0.017), post-chemotherapy CA 125 levels (OR 1.0211, p = 0.004), and PCI index (OR 1.67, p = 0.001) had statistically significant relationships with the outcome. Conversely, BMI, stage, and number of chemotherapy cycles did not demonstrate statistically significant relationships [Table 3a]. Multivariate Logistic Regression analysis was utilized, and a predictive model was developed to establish the relation between predictor variables and the outcome of CC-0/CC-1 . The results of the model revealed that age (OR=1.59, p-value = 0.05), number of co-morbidities (OR = 22.10, p-value< 0.001), post-chemotherapy CA 125 (OR = 1.03, p-value = 0.01), and PCI index (OR=0.52, p-value = 0.04) were associated with Completeness of Cytoreduction [Table 3b]. The overall accuracy of this predictive model was 97.17% (AUC), as per the ROC Curve in Figure 1a and b, which highlights the Sensitivity and Specificity plot of this Logistic Regression Model. Finally, the data were summarized to develop a Logistic Regression Nomogram [Figure 2]. Figure 3 demonstrates the interface of the calculator application to be used for external validation of the data.

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Figure 1a: (a) ROC curve of the model, (b) Sensitivity and specificity curve of the model. ROC: Receiver operating characteristic

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Figure 2: Nomogram developed from the model. PCI: Peritoneal carcinomatosis index

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Figure 3: Interface of the OviR0 application

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DISCUSSION

IDS or Interval Cytoreduction after NACT plays a significant role in the management of invasive Epithelial Ovarian Cancer, as indicated by the National Comprehensive Cancer Network (NCCN). Analyses of data from numerous prospective trials have revealed that the degree of residual disease following NACT followed by IDS is predictive of progression-free survival (PFS) and overall survival (OS). Randomized trials, including EORTC 55971, CHORUS, JCOG0602, and SCORPION, have demonstrated comparable survival outcomes between PDS and NACT followed by IDS, with reduced perioperative morbidity in the NACT arm. More recently, the CHRONO trial has evaluated the impact of extending neoadjuvant chemotherapy from 3 to 6 cycles before IDS.^[1-8] These trials have revealed optimum cytoreduction rates ranging from 45% to 91%, with complete eradication of all macroscopic disease accomplished in 30% to 59% of patients. Therefore, similar to PDS, every effort should be made to accomplish the total eradication of macroscopic disease (R0) during IDS.^[12-15] Experienced gynecological oncologists are necessary for preoperative evaluation and executing IDS. Several trials have studied the role of HIPEC in conjunction with CRS for advanced ovarian cancer. Lim et al.^[16] conducted a study that gave evidence supporting HIPEC post-CRS for primary advanced-stage ovarian cancer, demonstrating excellent results in terms of OS and PFS. Subsequent studies by Lim et al. and van Driel et al.^[16,17] demonstrated mixed results regarding the efficacy of HIPEC, with Lim et al.^[16] reporting no significant improvement in PFS or OS in the overall population but indicating a benefit in the neoadjuvant treatment group, and van Driel et al.^[17] showing significant improvements in disease-free survival (DFS) and OS with HIPEC in patients undergoing interval CRS after neoadjuvant chemotherapy.^[17,18] Aronson SL et al.^[19] also found improved DFS and OS with HIPEC in this context. Despite mixed results about HIPEC's efficacy in primary surgery, three randomized controlled trials (RCTs) suggest its benefits in advanced-stage ovarian cancer.^[19] Safety studies reveal that HIPEC with CRS is linked with similar rates of adverse events compared to CRS alone, but with reduced illness recurrence or fatality rates. The 10-year follow-up data from the OVIHIPEC-1 experiment were recently released, indicating a median survival of 44 months following cytoreductive surgery and HIPEC.^[20] To standardize treatment regimens in this setting, a Delphi Consensus was used in the PSOGI Consensus Guidelines in 2022.^[14] Presently, two trials further investigating the impact of cytoreduction in ovarian cancer include the CHIPPI study and the OVIHIPEC II study.^[21,22]

Several studies have investigated the role of clinical and biomarker-based models in predicting surgical outcomes for advanced epithelial ovarian cancer. E.C. Brockbank et colleagues (2004) identified serum CA-125 levels as a predictor of inadequate debulking.^[23] Arab et al. (2018) corroborated this assumption by identifying CA-125 levels, the presence of ascites, and liver metastases as major indicators of unsatisfactory primary surgery in EOC, underscoring the necessity of preoperative screening for optimal results.^[24] However, Memarzadeh S et al. (2003) cautioned against the reliability of CA-125 levels alone to predict suboptimal cytoreduction.^[25] These data collectively show the need to incorporate several preoperative indicators to increase the accuracy of predicting surgical outcomes in epithelial ovarian cancers (EOC) patients. Similarly, the study by Bhatt A et al.^[20] demonstrated that CA-125 levels don’t predict suboptimal cytoreduction. Other studies demonstrated the use of multiple tumor markers like HE4 and CA-125 to predict suboptimal cytoreduction.^[26]

In the field of imaging or surgery-based models, researchers have examined numerous techniques to predict surgical outcomes in ovarian cancer patients. Llueca A et al. proved the value of the PCI in predicting poor cytoreductive surgery, aiding in the selection between primary debulking surgery and neoadjuvant chemotherapy.^[27,28] Llueca A et al. reiterated the value of CT scans, especially when coupled with performance status (ECOG-PS) data, in predicting appropriate cytoreduction, boosting the prediction ability of CT scan-based models.^[29]

Nomograms and prediction scores have emerged as helpful techniques for determining the feasibility of Cytoreductive Surgery in Ovarian Cancer patients. Artis et al. created a nomogram including surgical aggressiveness index and positron-emission tomography/computed tomography features, displaying good predictive accuracy for In CC-0/ CC-1 , delivering important assistance for preoperative planning and treatment decisions.^[26] Similarly, Son HM et al. and Ferrandina G et al. constructed nomograms incorporating various clinical variables to predict the feasibility of Suboptimal Debulking Surgery (SDS) and IDS, respectively, in patients undergoing neoadjuvant chemotherapy, aiding in treatment decision-making.^[30,31] These findings underline the necessity of incorporating clinical, radiological, and histological characteristics into predictive models to optimize treatment regimens for ovarian cancer patients. Additionally, Cornelis G Shim SH et al. developed a nomogram incorporating preoperative blood platelet count, diffuse peritoneal thickening (DPT), and presence of ascites on CT scan slices, demonstrating predictive accuracy for suboptimal cytoreduction, further emphasizing the value of integrating imaging-based parameters into predictive models for surgical outcomes.^[32] A systematic review by Liu L et al. evaluated various predictive models and emphasized the need for higher-quality evidence to develop more effective prediction models for residual disease after surgery in advanced-stage ovarian cancer, highlighting common prognostic predictors such as peritoneal thickening, mesenteric and diaphragm disease, and ascites.^[33]

In underdeveloped nations like India, the high cost, lack of guidelines for cytoreductive surgery, and HIPEC limit patient access to advanced cancer treatments. This study uses a multivariate regression-based nomogram to predict outcomes after neoadjuvant Chemotherapy and cytoreductive surgery for ovarian cancer, incorporating variables like age, co-morbidities, CA 125 levels, and CT-based PCI. With a high ROC value of 97%, this model is a reliable, cost-effective, and patient-centric decision-making tool. Deploying this application in clinical practice after external validation may improve decision-making in Comprehensive Cancer Care.

The symposium by Yang and Park highlights the challenges and importance of establishing reliable predictive models for surgical resectability in patients with advanced ovarian cancer.^[34] While maximal cytoreduction is crucial, achieving optimal cytoreduction is not always feasible. Many studies have attempted to develop predictive models using various clinical characteristics, imaging, and biomarkers, but most have shown limited effectiveness and lack proper validation. To establish a reliable predictive model, several requirements need to be met, including clearly defining the surgical cytoreduction goal, determining the desired accuracy for clinical usefulness, testing all relevant predictors, and validating the model with external datasets. Ultimately, the goal is to develop a model that can aid decision-making and improve patient outcomes. The symposium emphasizes the urgent need for randomized clinical trials based on prediction models to guide decision-making in the management of advanced ovarian cancer.^[34-40]

The limitations of our study were primarily related to the reliance on retrospective data, which introduced potential biases and limited the generalizability of our findings. Additionally, certain patient populations were excluded, which could have affected the model’s applicability across diverse clinical settings. We also acknowledged the potential for biases in data collection, as well as the fact that the calculation of the radiological PCI score was operator-dependent, which may have led to variability in the results.

While our study introduced an innovative approach to predictive modeling and developed a practical tool for clinical decision-making, further validation and refinement were needed. The external validation of the OviR0 calculator was still pending at the time, and additional prospective studies would be necessary to confirm its utility, particularly in resource-limited settings. Moreover, this study also served as an initial Indian experience in predictive modeling within the domain of peritoneal surface malignancies, an area that had not been extensively explored before. This pioneering effort could pave the way for further research and application of predictive tools to improve outcomes in this challenging clinical domain.

CONCLUSION

Advanced epithelial ovarian cancers generally require Neoadjuvant Chemotherapy for downstaging and resolution of ascites prior to surgery. CC-0/CC-1 is an essential prerequisite prior to the administration of HIPEC. This study demonstrates our initial institutional insights of developing a predictive model that would aid in this decision-making with a user-friendly application. This model is due to be externally validated in a larger cohort and may subsequently shed light on its performance prior to its dispatch into the clinics.