Less guessing, more evidence in identifying patients least fit for cytoreductive surgery in advanced ovarian cancer: A triage algorithm to individualize surgical management

Neglected Anatomical Areas in Ovarian Cancer: Significance for Optimal Debulking Surgery

Ovarian cancer (OC), the most lethal gynecological malignancy, usually presents in advanced stages. Characterized by peritoneal and lymphatic dissemination, OC necessitates a complex surgical approach usually involving the upper abdomen with the aim of achieving optimal cytoreduction without visible macroscopic disease (R0). Failures in optimal cytoreduction, essential for prognosis, often stem from overlooking anatomical neglected sites that harbor residual tumor. Concealed OC metastases may be found in anatomical locations such as the omental bursa; Morison's pouch; the base of the round ligament and hepatic bridge; the splenic hilum; and suprarenal, retrocrural, cardiophrenic and inguinal lymph nodes. Hence, mastery of anatomy is crucial, given the necessity for maneuvers like liver mobilization, diaphragmatic peritonectomy and splenectomy, as well as dissection of suprarenal, celiac, and cardiophrenic lymph nodes in most cases. This article provides a meticulous anatomical description of neglected anatomical areas during OC surgery and describes surgical steps essential for the dissection of these "neglected" areas. This knowledge should equip clinicians with the tools needed for safe and complete cytoreduction in OC patients.

The Cukurova score in the prediction of primary cytoreduction in ovarian cancer

OBJECTIVE

Primary debulking surgery has been the preferred surgical route and is still considered a quality indicator for advanced ovarian cancer surgery. However, a significant number of patients are not amenable to upfront surgery. Neoadjuvant chemotherapy and interval debulking surgery may be the most suitable approach for this group. This study aimed to evaluate a novel score for prediction of the cytoreduction results at primary debulking surgery for ovarian cancer patients.

METHODS

This observational prospective study was conducted at a tertiary gynecologic oncology center between December 2020 and August 2022. Presumed primary stage III-IV epithelial ovarian carcinoma cases were included. Borderline tumors, and metastatic or non-epithelial ovarian malignancies, were excluded. Based on imaging findings, points were assigned to each anticipated surgical procedure required for complete cytoreduction. The sum of these points was multiplied by the patient's Eastern Cooperative Oncology Group (ECOG) score, and thus, the Cukurova-clinic score was established. Furthermore, the required surgical procedures based on laparoscopic evaluation were recorded, and the score was readjusted and calculated to obtain the Cukurova score.

RESULTS

One hundred and fourteen patients were included in the study. Primary debulking surgery was performed in 70% of cases. Among them, complete cytoreduction (Cukurova score ≤12) was obtained in 97.3% of cases. Complete cytoreduction was not achieved in cases with Cukurova score >12. The odds ratio of 90-day mortality was 13.4 for patients with Cukurova score >12, compared with those with Cukurova score ≤12.

CONCLUSION

The Cukurova score is a model for classifying advanced ovarian cancer patients who may be candidates for primary debulking surgery.

Validation of the Integrated Prediction Model algorithm for outcome of cytoreduction in advanced ovarian cancer

BACKGROUND

We previously developed the Integrated Prediction Model using a 4-step algorithm of unresectable stage IVB, patient factors, surgical resectability, and surgical complexity to predict outcome of <1 cm cytoreduction in advanced epithelial ovarian cancer, and triaged patients to neoadjuvant chemotherapy or primary cytoreductive surgery.

OBJECTIVE

To validate the Integrated Prediction Model on a retrospective cohort of patients.

METHODS

A retrospective cohort study of 107 patients with advanced ovarian cancer treated between January 2017 and September 2018 was carried out. The above mentioned 4-step algorithm determined cut-off points using the Youden Index. This validation study reports sensitivity, specificity, negative and positive predictive value on an external cohort.

RESULTS

Among 107 patients, 61 had primary surgery and 46 had neoadjuvant chemotherapy. Compared with primary surgery, patients treated with neoadjuvant chemotherapy were significantly older (63.5 vs 61, p=0.037), more likely to have stage IV disease (52% vs 18%, p<0.001), Eastern Cooperative Oncology Group (ECOG) score >1 (30% vs 11%, 0.045), lower pre-operative albumin (37 vs 40, p<0.001), and higher CA-125 (970 vs 227.5, p<0.001). They also had higher patient factors (2 vs 0, p=0.013), surgical resectability (4 vs 1, p<0.001), and anticipated surgical complexity (8 vs 5, p<0.001). There was no significant difference in outcome of cytoreduction (<1 cm residual disease: 85% for primary surgery vs 87% interval surgery, p=0.12)In this validation cohort, triaging patients with patient factors ≤2, surgical resectability score ≤5, and surgical complexity score ≤9 to primary surgery had a sensitivity of 91% for optimal cytoreduction <1 cm and a specificity of 81%. The positive predictive value, negative predictive value, and accuracy were 83%, 90%, and 86%, respectively. Application of the Integrated Prediction Model would have prevented five patients from receiving suboptimal cytoreduction and triaged them to neoadjuvant chemotherapy.

CONCLUSIONS

We validated the proposal that a triage algorithm integrating patient factors, surgical complexity, and surgical resectability in advanced ovarian cancer had high sensitivity and specificity to predict optimal cytoreduction <1 cm.

Facility level variation in the utilization of neoadjuvant chemotherapy is associated with higher surgical morbidity for patients with advanced stage epithelial ovarian carcinoma

OBJECTIVE

Investigate outcomes for advanced stage epithelial ovarian cancer (EOC) patients based on facility-level utilization of neoadjuvant chemotherapy (NACT).

METHODS

Stage III-IV EOC patients diagnosed between 2010 and 2016 were identified in the National Cancer Database. Percentage of patients managed with NACT was calculated for facilities, reporting ≥120 patients. Facilities with lowest and highest quartile of NACT rate comprised the low and high-utilizing groups. Clinico-pathological characteristics were collected, and appropriate statistical analysis performed.

RESULTS

High- and low-utilizing facilities managed on average 54.1% and 25.4% of patients with NACT respectively. Patients managed at high-utilizing facilities were significantly more likely to be >65 (p = 0.029), have stage IV disease (p < 0.001) and comorbidities (p < 0.001). Patients managed with primary debulking surgery (PDS) at low-utilizing facilities were significantly more likely to be >65, have stage IV disease, and have comorbidities (all, p < 0.001). Patients undergoing PDS at low-utilizing facilities were significantly less likely to achieve complete gross resection (p < 0.001), and were significantly more likely to experience 90-day mortality (p < 0.001), and unplanned 30-day readmission (p < 0.001). After controlling for age, comorbidities, race, insurance status, stage, grade and histology, high-utilizing facilities trended towards better overall survival (OS) (HR: 0.92, 95% CI: 0.85-0.99). Overall, patients undergoing PDS had better OS compared to those who had NACT (median 42 vs 27 months, p < 0.001).

CONCLUSIONS

Despite treating an EOC population with more advanced disease and comorbidities, high-utilizing facilities have lower surgical morbidity and mortality with no detrimental impact on long-term survival. Careful patient selection to minimize the morbidity and mortality associated with PDS is pivotal.

Validation of a risk-based algorithm to reduce poor operative outcomes after complex surgery for ovarian cancer

OBJECTIVE

We developed an algorithm that identifies patients at high risk of morbidity/mortality after cytoreductive surgery for advanced ovarian cancer. We have previously shown that the Mayo triage algorithm reduces operative mortality internally, followed by validation using an external low complexity national dataset. However, validation in a higher complexity surgical setting is required before widespread acceptance of this approach, and this was the goal of our study.

METHODS

We included patients who underwent debulking surgery (including primary or interval debulking surgery) for stage IIIC/IV ovarian cancer between October 2011 and November 2019 (SCORPION trial patients until May 2016 and non-trial patients thereafter) at Fondazione Policlinico A Gemelli, Italy. Using the algorithm, we classified patients as either high-risk or triage-appropriate and compared 30-day grade 3+ complications and 90-day mortality using a χ2 test or Fisher's exact test.

RESULTS

A total of 625 patients were included. The mean age was 58.7±11.4 years, 73.6% (n=460) were stage IIIC, and 63.0% (n=394) underwent primary debulking surgery. Surgical complexity was intermediate or high in 82.6% (n=516) of patients (95.7% (n=377) for primary surgery and 60.2% (n=139) for interval surgery), and 20.3% (n=127) were classified as high-risk. When compared with triage-appropriate patients, high-risk patients had (1) a threefold higher rate of 90-day mortality (6.3% vs 2.0%, p=0.02); (2) a higher likelihood of 90-day mortality following a grade 3+ complication (25.9% vs 10.0%, p=0.05); and (3) comparable rates of grade 3+ complications (21.3% vs 16.1%, p=0.17).

CONCLUSION

The evidence-based triage algorithm identifies patients at high risk of morbidity/mortality after cytoreductive surgery. Triage high-risk patients are poor candidates for surgery when complex surgery is required. This algorithm has been validated in heterogeneous settings (internal, national, and international) and degree of surgical complexity. Risk-based decision making should be standard of care when planning surgery for patients with advanced ovarian cancer, whether primary or interval surgery.

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer

Simple Summary

Approximately 30% of patients with advanced, high-grade serous ovarian cancer who undergo surgery will have a suboptimal result, resulting in decreased overall survival. Improving the ability to predict a successful surgery would improve survival. We aimed to use tumor genomics to create prediction models, which would predict an optimal or complete cytoreduction prior to entering the operating room. We created two sets of models, one for optimal and one for complete cytoreduction. We then validated those models using the TCGA database as well as statistical learning. We developed 21 models for optimal cytoreduction and 37 models for complete cytoreduction, which have the potential to improve our ability to predict these surgical results in patients with ovarian cancer before taking them to the operating room. Improving our pre-operative decision-making will result in more patients having the desired surgical results and, therefore, improved survival.

Abstract

Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.

The five-factor modified frailty index predicts adverse postoperative and chemotherapy outcomes in gynecologic oncology

INTRODUCTION

Frailty is increasingly recognized as a predictor of postoperative morbidity and oncologic outcomes. Evidence of the predictive value of frailty assessment in gynecologic oncology remains sparse.

OBJECTIVES

To evaluate the National Surgical Quality Improvement Program (NSQIP) comorbidity-based modified Frailty Index-5 (mFI-5) as predictor of severe postoperative complications, non-completion of chemotherapy and other patient-centered outcomes in gynecologic oncology patients >70 years-old undergoing surgery.

METHODS

Prospectively-collected NSQIP data and retrospective chart review of patients undergoing elective laparotomies for gynecologic malignances at a tertiary academic center in Ontario, Canada, between 01/2016-09/2020 were reviewed. Primary outcome was rate of 30-day Clavien-Dindo (Clavien) grade III-V complications. Secondary outcomes included Clavien II-V complications, postoperative length of stay (LOS), non-home discharge and non-completion of chemotherapy. Logistic regression analyses and receiver-operator curves were performed.

RESULTS

Two-hundred and fifty-nine patients were included; 103 were planned to receive adjuvant chemotherapy. Fifty-three patients (20.5%) had an mFI ≥ 2 and were categorized as frail. On multivariable analyses, frailty independently predicted grade III-V complications (OR 24.49, 95%CI 9.72-70.67, p < 0.0001), grade II-V complications (OR 4.64, 95%CI 2.31-9.94, p < 0.0001), non-home discharge (OR 7.37, 95%CI 2.81-20.46, p < 0.0001), LOS ≥ 7d (OR 3.6, 95% CI 1.54-8.6, p = 0.003) and non-completion of chemotherapy (OR 8.42, 95%CI 2.46-32.79, p = 0.001). Adjusted C-statistics demonstrated strong predictive value of the mFI-5 for grade III-V (0.92, 95%CI 0.86-0.97) and grade II-V (0.74, 95%CI 0.68-0.8) complications as well as non-home discharge (0.86, 95%CI 0.78-0.95) and chemotherapy non-completion (0.87, 95%CI 0.8-0.95).

CONCLUSION

Frailty as assessed with the mFI-5 predicted adverse postoperative and chemotherapy outcomes in gynecologic oncology patients aged ≥70 undergoing a laparotomy. The mFI-5 is a concise tool that can be used for routine frailty screening and risk stratification.

Quality is more important than quantity: pre-operative sarcopenia is associated with poor survival in advanced ovarian cancer

BACKGROUND

Sarcopenia is prevalent among older patients with cancer and is associated with poor outcomes.

OBJECTIVE

To explore the relationship between muscle mass, quality, and patient age with overall survival after surgery for advanced ovarian cancer.

METHODS

Patients with advanced stage (IIIC/IV) ovarian cancer who underwent primary cytoreductive surgery between January 2006 and July 2016 were included. Body composition measures were calculated from pre-operative CT imaging: skeletal muscle index (skeletal muscle index=skeletal muscle area normalized for height), skeletal muscle density, and skeletal muscle gauge (product of skeletal muscle index and skeletal muscle density). Each measure was transformed to a z-score and evaluated for association with risk of death using Cox proportional hazards models. Recursive partitioning was used to classify patients into homogeneous subgroups considering age and skeletal muscle gauge as predictors of overall survival.

RESULTS

The study included 429 patients (mean age 64.2 years). Increased age moderately correlated with decreased skeletal muscle gauge (r=-0.45). Decreasing skeletal muscle density and skeletal muscle gauge were significantly associated with increased risk of death; HR (95% CI) per 1-unit decrease in z-score of 1.24 (1.10 to 1.39) for skeletal muscle density and 1.27 (1.12 to 1.44) for skeletal muscle gauge. Associations were diluted after adjusting for age (1.13 (1.00 to 1.29) skeletal muscle density and 1.14 (0.99 to 1.30) skeletal muscle gauge). Recursive partitioning identified three subgroups: <60 years old, ≥60 years old with skeletal muscle gauge ≥937.3, and ≥60 years old with skeletal muscle gauge <937.3; median overall survival was 5.8, 3.3, and 2.3 years, respectively (p<0.001).

CONCLUSIONS

Skeletal muscle gauge, a novel sarcopenia measure incorporating quantity and quality, was associated with poorer survival in patients with advanced ovarian cancer, particularly among patients older than 60. Expanding our knowledge of how sarcopenia relates to solid tumor outcomes among high-risk patients can modify our treatment approach.

Performance validation of the Mayo triage algorithm applied to individualize surgical management of advanced epithelial ovarian cancer

OBJECTIVE

To externally validate the performance of the Mayo triage algorithm applied to treatment strategy management in advanced epithelial ovarian cancer (AEOC) patients.

METHODS

AEOC patients who underwent primary debulking surgery (PDS) were included and were divided into two groups based on the Mayo triage algorithm: "high risk" and "triage appropriate". The surgery outcomes and complications of the patients were compared between the two groups.

RESULTS

179 consecutive AEOC patients were enrolled for analysis, including 32 patients in the high-risk group and 147 patients in the triage-appropriate group. The results showed that patients in the high-risk group were older, had worse physical status and had lower preoperative serum albumin than those in the triage-appropriate group (P<0.01). The high-risk group had a lower proportion of women who underwent intermediate/high complexity surgery (38% vs. 72%, P<0.01) as well as a lower proportion of women who underwent optimal resection (50% vs. 71%, P<0.05). Furthermore, the incidence of 30-day complications (28% vs. 5%, P<0.01) and the proportion of patients who were unable to undergo adjuvant chemotherapy after PDS (22% vs. 2%, P<0.01) were both significantly higher in the high-risk group than in the triage-appropriate group. In addition, compared to the triage-appropriate group, the 90-day mortality rate in the high-risk group was also notably higher, but the difference was not statistically significant (6% vs. 1%, P=0.15).

CONCLUSION

The validity of the Mayo triage algorithm for treatment decision-making in AEOC was externally confirmed in this study. This short-term complication assessment tool could be effectively used for the individualized primary management of high-risk AEOC patients. The feasibility of the Mayo triage algorithm for use in long-term management should be further explored.

Peritoneal cancer index predicts severe complications after ovarian cancer surgery

INTRODUCTION

prediction and importance of severe postoperative complications after ovarian cancer surgery is a strong issue in patient selection and evaluation. Pre- and early peroperative predictors of severe 30-days postoperative complications (Clavien-Dindo class ≥3) after surgery for primary ovarian cancer are not fully established, neither their impact on patients' survival.

MATERIALS AND METHODS

A prospective observational study included 256 patients with primary ovarian cancer FIGO stages IIB-IV, operated during 2009-2018 in a primary or interval debulking surgery setting. Patient variables were analysed in relation to severe postoperative complications (Clavien-Dindo class ≥3) and overall survival.

RESULTS

High-grade postoperative complications occurred in 24.2% patients. Class 3a complications were observed in 12.5% cases. High-grade complications class ≥3 were observed in 31.6% after primary debulking surgery compared to 12.2% after interval debulking surgery (p = 0.0004). Peritoneal cancer index ≥21 and preoperative albumin concentration ≤33 g/L were independent predictors of high-grade complications. Peritoneal cancer index correlated with the surgical complexity score and completeness of cytoreduction. Increased peritoneal cancer index was a negative predictor of overall survival, but high-grade complications did not influence survival negatively.

CONCLUSIONS

Peritoneal cancer index ≥21 was an independent predictor of high-grade complications after ovarian cancer surgery. Increased peritoneal cancer index also impacted overall survival negatively, but high-grade complications did not influence overall survival.

Comparison of Survival Between Primary Debulking Surgery Versus Neoadjuvant Chemotherapy for Ovarian Cancers in a Personalized Treatment Cohort

Objective

To compare survival between primary debulking surgery (PDS) and neoadjuvant chemotherapy (NACT) for the treatment of ovarian cancer patients per our selective protocol.

Methods

Between Sep 1^st, 2015, and Aug 31^st, 2017, 161 patients were enrolled in our prospective cohort. All of the patients received preoperative clinic-radiological assessments, according to the Suidan criteria for R0 resection. Patients with a score of 0-2 received PDS. Patients with a score of ≥3 were counseled on the choices of PDS, NACT, or an optional staging laparoscopy, according to the Fagotti criteria. Clinic-pathological data were prospectively collected until May 1^st, 2020, and the impacts of different treatment strategies on progression-free survival (PFS) and overall survival (OS) were analyzed.

Results

110 patients underwent PDS, and 51 patients received NACT with consequent interval debulking surgery. The R0 resection rate was 57.8%. All but one of the patients received platinum-based chemotherapy, and 105 (65.2%) patients were platinum-sensitive. Based on the univariate analysis, the PDS group exhibited prolonged PFS compared with the NACT group (P=0.029). The subgroup analysis showed that patients receiving NACT with residual disease (RD) exhibited the worst PFS (P=0.001). Based on the multivariate analysis, NACT with RD was still an independent impaired factor for PFS (P=0.04). However, NACT did not affect OS in the univariate or multivariate analyses.

Conclusion

In our prospective cohort, NACT ovarian patients exhibited inferior PFS and noninferior OS compared with PDS patients. Given our selective protocol, NACT cannot be arbitrarily denied while appropriate PDS is still a priority.

Appropriate triage allows aggressive primary debulking surgery with rates of morbidity and mortality comparable to interval surgery after chemotherapy

OBJECTIVE

Morbidity and mortality (M/M) after primary debulking surgery (PDS) is often cited as a rationale for neoadjuvant chemotherapy and interval debulking surgery (IDS). We tested if using an evidence-based algorithm to identify patients fit for surgery would reduce M/M after PDS to that seen after IDS.

METHODS

We included women who underwent PDS or IDS for advanced epithelial ovarian cancer (EOC) (1/2012-7/2016) guided by the use of a prospective triage algorithm. Outcomes were compared after applying inverse-probability of treatment weighting (IPTW) to adjust for covariate imbalance.

RESULTS

Of 334 included patients, 232 (69.5%) underwent PDS and 102 (30.5%) were triaged to IDS. Relative to IDS group, PDS patients were younger (63.9 vs 67.5 years, P=0.01), were less likely to have low albumin (16.8% vs. 32.4%, P<0.001), had longer median operative times (315 vs 263 min, P <0.001), more high complexity surgeries and fewer low complexity surgeries (27.2% vs. 11.8% and 18.5% vs 36.3% respectively, P<0.001). The rates of the following outcomes were comparable for PDS and IDS, respectively: successful cytoreduction (complete, 62.5% vs 66.7%, P=0.47 and optimal, 95.3% vs 98.0%, P=0.36), 30-day grade 3+ complications (IPTW-adjusted 18.3% vs. 12.9%, P=0.22), 90-day mortality (IPTW-adjusted, 2.2% vs. 3.8%, P=0.42), length of hospitalization (P=0.29), and postoperative chemotherapy delivery (P=0.83). 3-year overall survival was higher for PDS group (IPTW-adjusted 64.1% vs. 42.6%, P=0.001).

CONCLUSIONS

Use of our validated triage strategy allowed us to offer 70% of women with advanced EOC PDS surgery. Despite more complex surgery, M/M after this approach is low and comparable to IDS, with similar rates of complete resection and superior OS. Use of a validated triage system should be utilized when considering PDS vs neoadjuvant chemotherapy.

Modified fragility index and surgical complexity score are able to predict postoperative morbidity and mortality after cytoreductive surgery for advanced ovarian cancer

OBJECTIVE

The aim of this study was to assess the impact of surgical complexity on postoperative complications and mortality, according to patient's frailty (mFI) following surgery for ovarian cancer.

METHODS

Patients undergoing cytoreductive surgery for ovarian cancer from 2008 to 2018 were identified from our database. A surgical complexity score from 1 to 3 was used to assess the extent of surgery (simple to complex, respectively). mFI with 11 variables, based on mapping the Canadian Study of Health and Aging Frailty Index to the NSQIP comorbidities was evaluated. Data were analyzed using Fisher exact test, independent sample t-test, and logistic regression.

RESULTS

Of 263 patients identified, 33% reported at least one postoperative complication and 6% had severe complications. BMI ≥ 30 (p = 0.04) increased mFI (p = 0.04) and high-complexity surgery (p < 0.001) were independent predictors of severe complications (G3-G5). Patients with high frailty index score (mFI ≥ 3) who underwent intermediate or high-complexity surgery were at higher risk of severe complications ranging from 29.4% to 50.

CONCLUSIONS

The combined evaluation of mFI and surgical complexity expected may identify patients at higher risk for severe morbidity allowing to stratify patients who are less likely to tolerate a surgical extensive treatment.

Patients triaged to neoadjuvant chemotherapy have higher rates of sarcopenia: An opportunity for prehabilitation

OBJECTIVE

To compare baseline body composition measures (BCM), including sarcopenia, between patients with advanced epithelial ovarian cancer (EOC) undergoing primary cytoreductive surgery (PCS) versus neoadjuvant chemotherapy/interval cytoreductive surgery (NACT/ICS) and evaluate changes in BCM pre-NACT versus pre-ICS.

METHODS

Patients with stage IIIC/IV EOC who underwent PCS or NACT with curative intent between 1/1/2012 and 7/31/2016 were included. Computed tomography scans were evaluated via a semi-automated program to determine BCM. Measures evaluated include skeletal muscle area (SMA), skeletal muscle density (SMD), skeletal muscle index (SMI), and skeletal muscle gauge (SMG). Sarcopenia was defined as SMI <39.0 cm²/m².

RESULTS

The study included 200 PCS patients and 85 NACT/ICS patients, of which 76 had both pre-NACT and pre-ICS scans. NACT patients were significantly more likely to be sarcopenic compared to PCS patients (40.0% vs 27.5%, p = 0.04). Mean SMA (107.3 vs 113.4 cm², p = 0.004) and mean SMG (1344.6 vs. 1456.9 (cm² x HU)/m², p = 0.06) were lower in NACT patients. Among NACT/ICS patients, mean SMI significantly decreased -1.4 cm²/m² (p = 0.005) at the time of surgery, resulting in a non-statistically significant increase in the percentage of sarcopenic patients from baseline (40.8% vs. 50.0%, p = 0.09).

CONCLUSIONS

Sarcopenia is more common in patients with advanced EOC undergoing NACT compared to PCS when using an evidence-based triage system for triage decisions. Body composition changes significantly over the course of NACT. Sarcopenia may be an indicator of debility and another factor for consideration in treatment planning. Further research into body composition's effects on prognosis and altering sarcopenia is necessary.